{"id":1784,"date":"2021-02-26T00:54:57","date_gmt":"2021-02-26T00:54:57","guid":{"rendered":"https:\/\/nwekenest.com\/?page_id=1784"},"modified":"2021-03-11T09:00:38","modified_gmt":"2021-03-11T09:00:38","slug":"publications","status":"publish","type":"page","link":"https:\/\/nwekenest.com\/index.php\/publications\/","title":{"rendered":"PUBLICATIONS"},"content":{"rendered":"
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Journal Articles<\/h3>\r\n <\/td>\r\n <\/tr>

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2025<\/h3>\r\n <\/td>\r\n <\/tr>

18.<\/td> Buckreis, Tristan E.; Nweke, Chukwuebuka C; Wang, Pengfei; Brandenberg, Scott J; Ramos-Sep\u00falveda, Maria E; Shams, Rashid; Mohammed, Shako; Pretell, Renmin; Mazzoni, Silvia; Zimmaro, Paolo; Stewart, Jonathan P<\/span>: A Global Application Programming Interface\u2013Enabled Earthquake Ground Motion Relational Database for Engineering Applications<\/a><\/span>. In: <\/span>Earthquake Spectra, <\/span>vol. 0, <\/span>no. 0, <\/span>pp. 87552930251344978, <\/span>2025<\/span>.<\/span> (Type: Journal Article<\/span> | Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span>)<\/span>
@article{doi:10.1177\/87552930251344978,
\r\ntitle = {A Global Application Programming Interface\\textendashEnabled Earthquake Ground Motion Relational Database for Engineering Applications},
\r\nauthor = {Tristan E. Buckreis and Chukwuebuka C Nweke and Pengfei Wang and Scott J Brandenberg and Maria E Ramos-Sep\\'{u}lveda and Rashid Shams and Shako Mohammed and Renmin Pretell and Silvia Mazzoni and Paolo Zimmaro and Jonathan P Stewart},
\r\nurl = {https:\/\/doi.org\/10.1177\/87552930251344978},
\r\ndoi = {10.1177\/87552930251344978},
\r\nyear  = {2025},
\r\ndate = {2025-07-17},
\r\nurldate = {2022-05-31},
\r\njournal = {Earthquake Spectra},
\r\nvolume = {0},
\r\nnumber = {0},
\r\npages = {87552930251344978},
\r\nabstract = {We present a application programming interface (API)-enabled relational database of global earthquake ground motion intensity measures, associated metadata, and processed time-series data. Raw ground motion records were processed by the authors using either manual or semi-automated processing procedures, and every processed record has passed a quality review by a trained analyst. Computed intensity measures include peak acceleration and velocity, pseudo-spectral acceleration response spectra, cumulative absolute velocity, Arias Intensity, and Fourier amplitude spectra. The processed time-series data, associated metadata, and ground motion intensity measures were organized into a web-served relational database consisting of 32 tables connected by primary\/foreign key pairs. Ground motion metadata and intensity measures (but not time-series) from the Next-Generation Attenuation (NGA)-East and NGA-West2 projects and the Hellenic Strong-Motion Database are also contained in the database. As of this writing (June 2025) the database includes intensity measures and metadata for 76,242 multi-component ground motions recorded at 9927 stations for 1391 events, and is approximately 73.5 GB in size. The database is built using the MySQL relational database management system, and is accessible through a web interface and also an API, which allows users to retrieve data using straightforward and intuitive uniform resource locators (URLs). Compared with more traditional file-download-based methods for data release, the relational database (1) increases storage efficiency, (2) improves data integrity, and (3) enables users to query the data subset they wish to retrieve rather than downloading the entire database and loading it into memory. Furthermore, the web-served nature of the database means that users have immediate access to ground motion data following collection, review, and uploading. Periodic static releases of the database will be published as a means of archiving and facilitating reproducibility. The database has been designed to accommodate growth, with ongoing efforts to integrate global ground motion data (e.g. data development for the NGA-West3 project).  keywords = },
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
We present a application programming interface (API)-enabled relational database of global earthquake ground motion intensity measures, associated metadata, and processed time-series data. Raw ground motion records were processed by the authors using either manual or semi-automated processing procedures, and every processed record has passed a quality review by a trained analyst. Computed intensity measures include peak acceleration and velocity, pseudo-spectral acceleration response spectra, cumulative absolute velocity, Arias Intensity, and Fourier amplitude spectra. The processed time-series data, associated metadata, and ground motion intensity measures were organized into a web-served relational database consisting of 32 tables connected by primary\/foreign key pairs. Ground motion metadata and intensity measures (but not time-series) from the Next-Generation Attenuation (NGA)-East and NGA-West2 projects and the Hellenic Strong-Motion Database are also contained in the database. As of this writing (June 2025) the database includes intensity measures and metadata for 76,242 multi-component ground motions recorded at 9927 stations for 1391 events, and is approximately 73.5 GB in size. The database is built using the MySQL relational database management system, and is accessible through a web interface and also an API, which allows users to retrieve data using straightforward and intuitive uniform resource locators (URLs). Compared with more traditional file-download-based methods for data release, the relational database (1) increases storage efficiency, (2) improves data integrity, and (3) enables users to query the data subset they wish to retrieve rather than downloading the entire database and loading it into memory. Furthermore, the web-served nature of the database means that users have immediate access to ground motion data following collection, review, and uploading. Periodic static releases of the database will be published as a means of archiving and facilitating reproducibility. The database has been designed to accommodate growth, with ongoing efforts to integrate global ground motion data (e.g. data development for the NGA-West3 project).  keywords = <\/div>
Close<\/a><\/p><\/div>
17.<\/td> Burton, Henry V; Madero, Sebastian Galicia; Wu, Chenhao; Shams, Rashid; Nweke, Chukwuebuka C<\/span>: Quantifying the realized and unrealized benefits of seismic interventions using causal inference<\/a><\/span>. In: <\/span>Natural Hazards, <\/span>vol. 0, <\/span>no. 0, <\/span>2025<\/span>, ISSN: 1573-0840<\/span>.<\/span> (Type: Journal Article<\/span> | Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span>)<\/span>
@article{doi:10.1007\/s11069-025-07531-6,
\r\ntitle = {Quantifying the realized and unrealized benefits of seismic interventions using causal inference},
\r\nauthor = {Henry V Burton and Sebastian Galicia Madero and Chenhao Wu and Rashid Shams and Chukwuebuka C Nweke},
\r\nurl = {https:\/\/doi.org\/10.1007\/s11069-025-07531-6},
\r\ndoi = {10.1007\/s11069-025-07531-6},
\r\nissn = {1573-0840},
\r\nyear  = {2025},
\r\ndate = {2025-01-01},
\r\njournal = {Natural Hazards},
\r\nvolume = {0},
\r\nnumber = {0},
\r\nabstract = {Prior studies that sought to highlight the benefit of seismic risk mitigation strategies (e.g., retrofits, building code change) during real earthquakes have largely relied on anecdotal evidence. This paper presents a data-driven methodology for quantifying the regional-scale benefit of seismic interventions that leverages the methods, tools, and language of causal inference. From a causal perspective, the seismic intervention is defined as the treatment, and the metric used to quantify performance at scale is the outcome. In addition to the intervention, the additional features that are expected to influence the performance are defined as covariates. Using causal inference, we are able to isolate the effect (or benefit) of the intervention (treatment) on the overall performance (outcome) while controlling for influential covariates or confounders. The framework is implemented to quantify the realized and unrealized inventory-scale benefit of cripple retrofit during the 2014 South Napa earthquake. The building retrofit state and repair costs due to earthquake damage are defined as the treatment and outcome, respectively. The covariates considered are the building and site parameters (e.g., number of stories, age, site shaking intensity during the earthquake) that, in addition to the retrofit state, are expected to influence the level of damage and resulting repair costs. The double machine learning algorithm is adopted as the estimation strategy because of its ability to deal with high-dimensional covariates and confounders. The results of the causal analysis show that the retrofit reduced repair costs in pre-1980 residential buildings with cripple walls by an average of $13,697. The total realized benefit considering all retrofitted buildings is approximately $3.4 million. In other words, this would have been the added inventory level repair cost if these buildings were not retrofitted. Moreover, this reduction in repair costs would have increased by a factor of approximately four (i.e., to $$approx$$$14.5 million) had the entire inventory been retrofitted prior to the earthquake. keywords = },
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
Prior studies that sought to highlight the benefit of seismic risk mitigation strategies (e.g., retrofits, building code change) during real earthquakes have largely relied on anecdotal evidence. This paper presents a data-driven methodology for quantifying the regional-scale benefit of seismic interventions that leverages the methods, tools, and language of causal inference. From a causal perspective, the seismic intervention is defined as the treatment, and the metric used to quantify performance at scale is the outcome. In addition to the intervention, the additional features that are expected to influence the performance are defined as covariates. Using causal inference, we are able to isolate the effect (or benefit) of the intervention (treatment) on the overall performance (outcome) while controlling for influential covariates or confounders. The framework is implemented to quantify the realized and unrealized inventory-scale benefit of cripple retrofit during the 2014 South Napa earthquake. The building retrofit state and repair costs due to earthquake damage are defined as the treatment and outcome, respectively. The covariates considered are the building and site parameters (e.g., number of stories, age, site shaking intensity during the earthquake) that, in addition to the retrofit state, are expected to influence the level of damage and resulting repair costs. The double machine learning algorithm is adopted as the estimation strategy because of its ability to deal with high-dimensional covariates and confounders. The results of the causal analysis show that the retrofit reduced repair costs in pre-1980 residential buildings with cripple walls by an average of $13,697. The total realized benefit considering all retrofitted buildings is approximately $3.4 million. In other words, this would have been the added inventory level repair cost if these buildings were not retrofitted. Moreover, this reduction in repair costs would have increased by a factor of approximately four (i.e., to $$approx$$$14.5 million) had the entire inventory been retrofitted prior to the earthquake. keywords = <\/div>
Close<\/a><\/p><\/div>
16.<\/td> Ko, Kil-Wan; Kayen, Robert E.; Nweke, Chukwuebuka C.<\/span>: Estimation of Timing of Liquefaction Using Spectral Energy Ratio<\/a><\/span>. In: <\/span>Journal of Geotechnical and Geoenvironmental Engineering, <\/span>vol. 151, <\/span>no. 4, <\/span>pp. 04025017, <\/span>2025<\/span>.<\/span> (Type: Journal Article<\/span> | Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span>)<\/span>
@article{doi:10.1061\/JGGEFK.GTENG-12442,
\r\ntitle = {Estimation of Timing of Liquefaction Using Spectral Energy Ratio},
\r\nauthor = {Kil-Wan Ko and Robert E. Kayen and Chukwuebuka C. Nweke},
\r\nurl = {https:\/\/ascelibrary.org\/doi\/abs\/10.1061\/JGGEFK.GTENG-12442},
\r\ndoi = {10.1061\/JGGEFK.GTENG-12442},
\r\nyear  = {2025},
\r\ndate = {2025-01-01},
\r\njournal = {Journal of Geotechnical and Geoenvironmental Engineering},
\r\nvolume = {151},
\r\nnumber = {4},
\r\npages = {04025017},
\r\nabstract = {The significant reduction in the stiffness of liquefied soil is accompanied by a decrease in the shear wave velocity, which ultimately results in the softening of the liquefied site. Time\\textendashfrequency response analysis can identify the sudden drop in the frequency of the liquefied site, which has been widely employed to determine the onset of liquefaction. However, using the modal frequency (corresponding to the maximum power at each time step) to identify the timing of liquefaction (tL) captures the reduction in frequency during earthquakes, but it does not encompass the entire range of frequencies that have changed. Furthermore, previous literature defines tL as the boundary separating the modal frequency into pre- and postliquefaction time segments, but this estimate does not consider the generation of pore water pressure. Two representative case histories are presented to highlight the limitations of identifying tL by solely relying on the modal frequency approach that uses a two-step function. As a result, this study introduces an innovative method to identify tL utilizing the spectral energy ratio (SER), which captures the entire frequency shift. A step-by-step procedure using SER is detailed, and the new estimates of tL are compared with those derived from previous literature using 30 case histories. To validate the approach, a sensitivity analysis was performed using centrifuge test data from the Liquefaction Experiment and Analysis Projects. Results indicated that incorporating a ramp that accounts for pore water pressure buildup in the trilinear function improved tL estimation. An optimized SER value of 0.92 was determined for the proposed method. The notable contribution of this study is an enhanced approach of identifying the timing of liquefaction triggering by only utilizing acceleration records without requiring pore water pressure responses.  keywords = },
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
The significant reduction in the stiffness of liquefied soil is accompanied by a decrease in the shear wave velocity, which ultimately results in the softening of the liquefied site. Time\u2013frequency response analysis can identify the sudden drop in the frequency of the liquefied site, which has been widely employed to determine the onset of liquefaction. However, using the modal frequency (corresponding to the maximum power at each time step) to identify the timing of liquefaction (tL) captures the reduction in frequency during earthquakes, but it does not encompass the entire range of frequencies that have changed. Furthermore, previous literature defines tL as the boundary separating the modal frequency into pre- and postliquefaction time segments, but this estimate does not consider the generation of pore water pressure. Two representative case histories are presented to highlight the limitations of identifying tL by solely relying on the modal frequency approach that uses a two-step function. As a result, this study introduces an innovative method to identify tL utilizing the spectral energy ratio (SER), which captures the entire frequency shift. A step-by-step procedure using SER is detailed, and the new estimates of tL are compared with those derived from previous literature using 30 case histories. To validate the approach, a sensitivity analysis was performed using centrifuge test data from the Liquefaction Experiment and Analysis Projects. Results indicated that incorporating a ramp that accounts for pore water pressure buildup in the trilinear function improved tL estimation. An optimized SER value of 0.92 was determined for the proposed method. The notable contribution of this study is an enhanced approach of identifying the timing of liquefaction triggering by only utilizing acceleration records without requiring pore water pressure responses.  keywords = <\/div>
Close<\/a><\/p><\/div>
15.<\/td> Ilgac, Makbule; Athanasopoulos-Zekkos, Adda; Nweke, Chukwuebuka Chukwuemeka; Ktenidou, Olga-Joan; Peterson, Kyle; Kayen, Robert E.<\/span>: Embankment vibration characteristics using ground motion records and ambient noise measurements, Briones Dam, California<\/a><\/span>. In: <\/span>Soils and Foundations, <\/span>vol. 65, <\/span>no. 4, <\/span>pp. 101664, <\/span>2025<\/span>, ISSN: 0038-0806<\/span>.<\/span> (Type: Journal Article<\/span> | Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span>)<\/span>
@article{ILGAC2025101664,
\r\ntitle = {Embankment vibration characteristics using ground motion records and ambient noise measurements, Briones Dam, California},
\r\nauthor = {Makbule Ilgac and Adda Athanasopoulos-Zekkos and Chukwuebuka Chukwuemeka Nweke and Olga-Joan Ktenidou and Kyle Peterson and Robert E. Kayen},
\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0038080625000988},
\r\ndoi = {https:\/\/doi.org\/10.1016\/j.sandf.2025.101664},
\r\nissn = {0038-0806},
\r\nyear  = {2025},
\r\ndate = {2025-01-01},
\r\njournal = {Soils and Foundations},
\r\nvolume = {65},
\r\nnumber = {4},
\r\npages = {101664},
\r\nabstract = {Investigating the seismic response of earth embankment dams is crucial for assessing the safety of existing dams and guiding new design procedures. The dam fundamental frequency (f0) is a critical parameter in the dynamic response of dams and can be evaluated using seismic recordings through Horizontal-to-Vertical Spectral Ratio (HVSR) and Standard Spectral Ratio (SSR) methods. This study focuses on assessing the vibration characteristics of Briones Dam, a 78\u00a0m-tall earth embankment dam located in the Bay Area in Northern California. First, earthquake-based Horizontal-to-Vertical Spectral Ratio (eHVSR) was estimated by dividing the horizontal records by the vertical components, and the SSR was determined by comparing crest recordings with those from the abutment. Additionally, a field test program was conducted to collect ambient noise measurements at Briones Dam, allowing for the calculation of microtremor-based HVSR. The fundamental frequency was estimated using three empirical methods: mHVSR (0.7\\textendash1\u00a0Hz), eHVSR (0.9\\textendash1.1\u00a0Hz), and SSR (1.2\u00a0Hz). The median fundamental frequency of the dam is estimated to be approximately 1\u00a0Hz at the center of the dam crest. The slight variations among these three methods suggest the need for further investigations that consider the geological and geotechnical conditions of the dam. keywords = },
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
Investigating the seismic response of earth embankment dams is crucial for assessing the safety of existing dams and guiding new design procedures. The dam fundamental frequency (f0) is a critical parameter in the dynamic response of dams and can be evaluated using seismic recordings through Horizontal-to-Vertical Spectral Ratio (HVSR) and Standard Spectral Ratio (SSR) methods. This study focuses on assessing the vibration characteristics of Briones Dam, a 78\u00a0m-tall earth embankment dam located in the Bay Area in Northern California. First, earthquake-based Horizontal-to-Vertical Spectral Ratio (eHVSR) was estimated by dividing the horizontal records by the vertical components, and the SSR was determined by comparing crest recordings with those from the abutment. Additionally, a field test program was conducted to collect ambient noise measurements at Briones Dam, allowing for the calculation of microtremor-based HVSR. The fundamental frequency was estimated using three empirical methods: mHVSR (0.7\u20131\u00a0Hz), eHVSR (0.9\u20131.1\u00a0Hz), and SSR (1.2\u00a0Hz). The median fundamental frequency of the dam is estimated to be approximately 1\u00a0Hz at the center of the dam crest. The slight variations among these three methods suggest the need for further investigations that consider the geological and geotechnical conditions of the dam. keywords = <\/div>
Close<\/a><\/p><\/div>
\r\n

2024<\/h3>\r\n <\/td>\r\n <\/tr>

14.<\/td> Nweke, Chukwuebuka C; Shams, Rashid<\/span>: Southern California basin and non-basin classification algorithm for ground-motion site amplification model applications<\/a><\/span>. In: <\/span>Earthquake Spectra, <\/span>vol. 41, <\/span>no. 1, <\/span>pp. 908-930, <\/span>2024<\/span>.<\/span> (Type: Journal Article<\/span> | Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span>)<\/span>
@article{doi:10.1177\/87552930241293568,
\r\ntitle = {Southern California basin and non-basin classification algorithm for ground-motion site amplification model applications},
\r\nauthor = {Chukwuebuka C Nweke and Rashid Shams},
\r\nurl = {https:\/\/doi.org\/10.1177\/87552930241293568},
\r\ndoi = {10.1177\/87552930241293568},
\r\nyear  = {2024},
\r\ndate = {2024-12-11},
\r\njournal = {Earthquake Spectra},
\r\nvolume = {41},
\r\nnumber = {1},
\r\npages = {908-930},
\r\nabstract = {In ground-motion modeling, the estimated level of ground shaking at any given location for an expected earthquake scenario depends on the contributions from the source component (type of fault mechanism and size of the fault slip), the path component (distance between the source and site of interest, and the geologic characteristics of that region), and the site component (the local geology at the site of interest). Each component captures some level of variability and uncertainty in the overall ground-motion estimate. In particular, the site component represents the potential amplification (or de-amplification) of the seismic waves that may lead to magnified and prolonged ground shaking at any given location. This feature is referred to as site effects and in current ground-motion models (GMMs) is dependent on the time-averaged shear wave velocity in the upper 30\u2009m of the earth\u2019s crust (Vs30) and the depth to a particular shear wave velocity iso-surface (\u201cbasin depth,\u201dzx). The latter is responsible for determining the contributions of basin effects, which is additional ground-motion amplification due to three-dimensional effects such as trapped seismic waves that lead to surface wave generation. However, an evaluation of the relationship between zx and basin locations reveals cases of misclassification that is a result of geologic variability (i.e. zx is not sufficient in differentiating basins from non-basins). The study performed in this article proposes a resolution in the form of a statistical classification model that determines the probability of a location residing within or outside a basin based on simple geologic features such as ground surface texture.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
In ground-motion modeling, the estimated level of ground shaking at any given location for an expected earthquake scenario depends on the contributions from the source component (type of fault mechanism and size of the fault slip), the path component (distance between the source and site of interest, and the geologic characteristics of that region), and the site component (the local geology at the site of interest). Each component captures some level of variability and uncertainty in the overall ground-motion estimate. In particular, the site component represents the potential amplification (or de-amplification) of the seismic waves that may lead to magnified and prolonged ground shaking at any given location. This feature is referred to as site effects and in current ground-motion models (GMMs) is dependent on the time-averaged shear wave velocity in the upper 30\u2009m of the earth\u2019s crust (Vs30) and the depth to a particular shear wave velocity iso-surface (\u201cbasin depth,\u201dzx). The latter is responsible for determining the contributions of basin effects, which is additional ground-motion amplification due to three-dimensional effects such as trapped seismic waves that lead to surface wave generation. However, an evaluation of the relationship between zx and basin locations reveals cases of misclassification that is a result of geologic variability (i.e. zx is not sufficient in differentiating basins from non-basins). The study performed in this article proposes a resolution in the form of a statistical classification model that determines the probability of a location residing within or outside a basin based on simple geologic features such as ground surface texture.<\/div>
Close<\/a><\/p><\/div>
13.<\/td> Ko, Kil-Wan; Kayen, Robert E; Kokusho, Takaji; Ilgac, Makbule; Nozu, Atsushi; Nweke, Chukwuebuka C<\/span>: Energy-Based Liquefaction Evaluation: The Port of Kushiro in Hokkaido, Japan, 2003 Tokachi-Oki Earthquake<\/a><\/span>. In: <\/span>Journal of Geotechnical and Geoenvironmental Engineering, <\/span>vol. 150, <\/span>no. 10, <\/span>2024<\/span>.<\/span> (Type: Journal Article<\/span> | Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span>)<\/span>
@article{doi:10.1061\/JGGEFK.GTENG-11989,
\r\ntitle = {Energy-Based Liquefaction Evaluation: The Port of Kushiro in Hokkaido, Japan, 2003 Tokachi-Oki Earthquake},
\r\nauthor = {Kil-Wan Ko and Robert E Kayen and Takaji Kokusho and Makbule Ilgac and Atsushi Nozu and Chukwuebuka C Nweke},
\r\nurl = {https:\/\/doi.org\/10.1061\/JGGEFK.GTENG-11989},
\r\ndoi = {10.1061\/JGGEFK.GTENG-11989},
\r\nyear  = {2024},
\r\ndate = {2024-10-01},
\r\njournal = {Journal of Geotechnical and Geoenvironmental Engineering},
\r\nvolume = {150},
\r\nnumber = {10},
\r\nabstract = {The magnitude (\ud835\udc40\ud835\udc64) 8.3 Tokachi-oki earthquake occurred in September 2003, causing extensive damage in Hokkaido, Japan, and triggering extensive soil liquefaction in the region. The Port of Kushiro was one of the locations where surficial evidence of liquefaction was observed but was also a well-instrumented location with four pore-water pressure transducers installed in the backfill of the quay wall. However, all of the sensors malfunctioned during the earthquake. As a result, the pore-water pressure response recorded by those sensors were inaccurate and unusable with regard to evaluating liquefaction triggering and extent. This study introduced the energy-based soil liquefaction evaluation to estimate the excess pore water pressure responses at the Port of Kushiro based on the cumulative strain energy of the soil during the 2003 Tokachi-oki earthquake. In order to apply the energy-based method to this case history, this study explored the empirical equation describing a relationship between normalized cumulative energy and excess pore water pressure ratio while incorporating the bidirectional shaking effect on strain energy development. Although the energy-based method allowed for the estimation of the time needed to trigger liquefaction at a target site, it was derived using the empirical coefficients that were developed for a different soil from those at the site of interest. This indicated that an adjustment to the estimated timing of liquefaction was needed, which was accomplished by additional evaluation through a Stockwell transform and Arias intensity-based liquefaction assessment. Both procedures indicated a similar timing of liquefaction at the site. Based on the updated time of liquefaction triggering, the empirical coefficient was recalibrated to estimate the excess pore water pressure ratio, and the result provided reasonable excess pore water pressure responses at the backfill of the Port of Kushiro during the 2003 Tokachi-oki earthquake.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
The magnitude (\ud835\udc40\ud835\udc64) 8.3 Tokachi-oki earthquake occurred in September 2003, causing extensive damage in Hokkaido, Japan, and triggering extensive soil liquefaction in the region. The Port of Kushiro was one of the locations where surficial evidence of liquefaction was observed but was also a well-instrumented location with four pore-water pressure transducers installed in the backfill of the quay wall. However, all of the sensors malfunctioned during the earthquake. As a result, the pore-water pressure response recorded by those sensors were inaccurate and unusable with regard to evaluating liquefaction triggering and extent. This study introduced the energy-based soil liquefaction evaluation to estimate the excess pore water pressure responses at the Port of Kushiro based on the cumulative strain energy of the soil during the 2003 Tokachi-oki earthquake. In order to apply the energy-based method to this case history, this study explored the empirical equation describing a relationship between normalized cumulative energy and excess pore water pressure ratio while incorporating the bidirectional shaking effect on strain energy development. Although the energy-based method allowed for the estimation of the time needed to trigger liquefaction at a target site, it was derived using the empirical coefficients that were developed for a different soil from those at the site of interest. This indicated that an adjustment to the estimated timing of liquefaction was needed, which was accomplished by additional evaluation through a Stockwell transform and Arias intensity-based liquefaction assessment. Both procedures indicated a similar timing of liquefaction at the site. Based on the updated time of liquefaction triggering, the empirical coefficient was recalibrated to estimate the excess pore water pressure ratio, and the result provided reasonable excess pore water pressure responses at the backfill of the Port of Kushiro during the 2003 Tokachi-oki earthquake.<\/div>
Close<\/a><\/p><\/div>
12.<\/td> Mohammed, Shako; Shams, Rashid; Nweke, Chukwuebuka C; Buckreis, Tristan E; Kohler, Monica D; Bozorgnia, Yousef; Stewart, Jonathan P<\/span>: Usability of Community Seismic Network Recordings for Ground Motion Modeling<\/a><\/span>. In: <\/span>Earthquake Spectra, <\/span>2024<\/span>.<\/span> (Type: Journal Article<\/span> | Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span>)<\/span>
@article{doi:10.1177\/87552930241267749,
\r\ntitle = {Usability of Community Seismic Network Recordings for Ground Motion Modeling},
\r\nauthor = {Shako Mohammed and Rashid Shams and Chukwuebuka C Nweke and Tristan E Buckreis and Monica D Kohler and Yousef Bozorgnia and Jonathan P Stewart},
\r\nurl = {https:\/\/doi.org\/10.1177\/87552930241267749},
\r\ndoi = {10.1177\/87552930241267749},
\r\nyear  = {2024},
\r\ndate = {2024-08-09},
\r\njournal = {Earthquake Spectra},
\r\nabstract = {A source of ground-motion recordings in urban Los Angeles that has seen limited prior application is the Community Seismic Network (CSN), which uses low-cost, micro\\textendashelectro\\textendashmechanical system (MEMS) sensors that are deployed at much higher densities than stations for other networks. We processed CSN data for the 29 earthquakes with M\u2009\\>\u20094 between July 2012 and January 2023 that produced CSN recordings, including selection of high- and low-pass corner frequencies (fcHP and fcLP, respectively). Each record was classified as follows: (1) Broadband Record (BBR)\\textemdashrelatively broad usable frequency range from fcHP\u2009\\<\u20090.5 to fcLP\u2009\\>\u200910\u2009Hz; (2) Narrowband Record (NBR)\\textemdashlimited usable frequency range relative to those for BBR; and (3) Rejected Record (REJ)\\textemdashnoise-dominated. We compare recordings from proximate (within 3\u2009km) CSN and non-CSN stations (screened to only include cases of similar surface geology and favorable CSN instrument housing). We find similar high- to medium-frequency ground motions (i.e. peak ground acceleration (PGA) and Sa for T\u2009\\<\u20095\u2009s) from CSN BBR and non-CSN stations, whereas NBRs have lower amplitudes. We examine PGA distributions for BBR and REJ records and find them to be distinguished, on average across the network, at 0.005\u2009g, whereas 0.0015\u2009g was found to be the threshold between usable records (BBR and NBR) and pre-event noise. Recordings with amplitudes near or below these thresholds are generally noise-dominated, reflecting environmental and anthropogenic ground vibrations and instrument noise. We find nominally higher noise levels in areas of high-population density and lower noise levels by a factor of about 1.5 in low-population density areas. By applying the 0.0015\u2009g threshold, limiting distances for the network-average site condition, based on the expected fifth-percentile ground-motion levels, are 89, 210, 280, and 370\u2009km for M 5, 6, 7, and 8 events, respectively.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
A source of ground-motion recordings in urban Los Angeles that has seen limited prior application is the Community Seismic Network (CSN), which uses low-cost, micro\u2013electro\u2013mechanical system (MEMS) sensors that are deployed at much higher densities than stations for other networks. We processed CSN data for the 29 earthquakes with M\u2009>\u20094 between July 2012 and January 2023 that produced CSN recordings, including selection of high- and low-pass corner frequencies (fcHP and fcLP, respectively). Each record was classified as follows: (1) Broadband Record (BBR)\u2014relatively broad usable frequency range from fcHP\u2009<\u20090.5 to fcLP\u2009>\u200910\u2009Hz; (2) Narrowband Record (NBR)\u2014limited usable frequency range relative to those for BBR; and (3) Rejected Record (REJ)\u2014noise-dominated. We compare recordings from proximate (within 3\u2009km) CSN and non-CSN stations (screened to only include cases of similar surface geology and favorable CSN instrument housing). We find similar high- to medium-frequency ground motions (i.e. peak ground acceleration (PGA) and Sa for T\u2009<\u20095\u2009s) from CSN BBR and non-CSN stations, whereas NBRs have lower amplitudes. We examine PGA distributions for BBR and REJ records and find them to be distinguished, on average across the network, at 0.005\u2009g, whereas 0.0015\u2009g was found to be the threshold between usable records (BBR and NBR) and pre-event noise. Recordings with amplitudes near or below these thresholds are generally noise-dominated, reflecting environmental and anthropogenic ground vibrations and instrument noise. We find nominally higher noise levels in areas of high-population density and lower noise levels by a factor of about 1.5 in low-population density areas. By applying the 0.0015\u2009g threshold, limiting distances for the network-average site condition, based on the expected fifth-percentile ground-motion levels, are 89, 210, 280, and 370\u2009km for M 5, 6, 7, and 8 events, respectively.<\/div>
Close<\/a><\/p><\/div>
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2023<\/h3>\r\n <\/td>\r\n <\/tr>

11.<\/td> Ikeagwuani, Christopher C; Nweke, Chukwuebuka C; Onah, Hyginus N<\/span>: Prediction of resilient modulus of fine-grained soil for pavement design using KNN, MARS, and random forest techniques<\/a><\/span>. In: <\/span>Arabian Journal of Geosciences, <\/span>vol. 16, <\/span>no. 388, <\/span>2023<\/span>.<\/span> (Type: Journal Article<\/span> | Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span>)<\/span>
@article{doi:10.1007\/s12517-023-11469-z,
\r\ntitle = {Prediction of resilient modulus of fine-grained soil for pavement design using KNN, MARS, and random forest techniques},
\r\nauthor = {Christopher C Ikeagwuani and Chukwuebuka C Nweke and Hyginus N Onah},
\r\nurl = {https:\/\/doi.org\/10.1007\/s12517-023-11469-z},
\r\ndoi = {10.1007\/s12517-023-11469-z},
\r\nyear  = {2023},
\r\ndate = {2023-05-27},
\r\njournal = {Arabian Journal of Geosciences},
\r\nvolume = {16},
\r\nnumber = {388},
\r\nabstract = {This study was motivated by the difficulty in determining the resilient modulus of soils using the repeated load triaxial test (RLTT) recommended by the mechanistic-empirical pavement design guide (MEPDG). An alternative means to estimate the resilient modulus of fine-grained soils has been established in the form of three models that were developed using three supervised machine-learning techniques. This includes k-nearest neighbor (KNN), multivariate adaptive regression splines (MARS), and random forest. The data utilized for the development of the models were sourced from the long-term pavement performance (LTPP) database domiciled in the Infopave database in the USA. A total of twelve routine soil properties that have significant influence on the resilient modulus of fine-grained soils were considered in this study. Results obtained from this study revealed that the three developed models (KNN, MARS, and random forest) had high prediction accuracy and high generalization ability. However, the random forest model, based on the statistical indices used to evaluate the models, gave the best prediction accuracy (R2 = 0.9312 for the testing dataset) of the three developed model. It was followed closely by the MARS model with an R2 value of 0.9057. The last model in terms of prediction accuracy was the KNN model with an R2 value of 0.8748. Furthermore, based on parameter significance assessment using the random forest model, it was revealed that the nominal maximum axial stress and confining pressure are the best predictor variables for the estimation of the resilient modulus of fine-grained soils.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
This study was motivated by the difficulty in determining the resilient modulus of soils using the repeated load triaxial test (RLTT) recommended by the mechanistic-empirical pavement design guide (MEPDG). An alternative means to estimate the resilient modulus of fine-grained soils has been established in the form of three models that were developed using three supervised machine-learning techniques. This includes k-nearest neighbor (KNN), multivariate adaptive regression splines (MARS), and random forest. The data utilized for the development of the models were sourced from the long-term pavement performance (LTPP) database domiciled in the Infopave database in the USA. A total of twelve routine soil properties that have significant influence on the resilient modulus of fine-grained soils were considered in this study. Results obtained from this study revealed that the three developed models (KNN, MARS, and random forest) had high prediction accuracy and high generalization ability. However, the random forest model, based on the statistical indices used to evaluate the models, gave the best prediction accuracy (R2 = 0.9312 for the testing dataset) of the three developed model. It was followed closely by the MARS model with an R2 value of 0.9057. The last model in terms of prediction accuracy was the KNN model with an R2 value of 0.8748. Furthermore, based on parameter significance assessment using the random forest model, it was revealed that the nominal maximum axial stress and confining pressure are the best predictor variables for the estimation of the resilient modulus of fine-grained soils.<\/div>
Close<\/a><\/p><\/div>
10.<\/td> Carey, Trevor J; Mason, Henry B; Asikmaki, Dominiki; Athanasopoulos-Zekkos, Adda; Garcia, Fernando E; Gray, Brian; Lavrentiadis, Grigorios; Nweke, Chukwuebuka C<\/span>: The 2022 Chihshang, Taiwan, Earthquake: Initial GEER Team Observations<\/a><\/span>. In: <\/span>Journal of Geotechnical and Geoenvironmental Engineering, <\/span>vol. 149, <\/span>no. 5, <\/span>2023<\/span>.<\/span> (Type: Journal Article<\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span>)<\/span>
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2022<\/h3>\r\n <\/td>\r\n <\/tr>

9.<\/td> Nweke, Chukwuebuka C; Stewart, Jonathan P; Wang, Pengfei; Brandenberg, Scott J<\/span>: Site response of sedimentary basins and other geomorphic provinces in southern California<\/a><\/span>. In: <\/span>Earthquake Spectra, <\/span>2022<\/span>.<\/span> (Type: Journal Article<\/span> | Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span>)<\/span>
@article{doi:10.1177\/87552930221088609,
\r\ntitle = {Site response of sedimentary basins and other geomorphic provinces in southern California},
\r\nauthor = {Chukwuebuka C Nweke and Jonathan P Stewart and Pengfei Wang and Scott J Brandenberg},
\r\nurl = {https:\/\/doi.org\/10.1177\/87552930221088609},
\r\ndoi = {10.1177\/87552930221088609},
\r\nyear  = {2022},
\r\ndate = {2022-05-31},
\r\njournal = {Earthquake Spectra},
\r\nabstract = {Ergodic site amplification models for active tectonic regions are conditioned on the time-averaged shear wave velocity in the upper 30\u2009m (VS30) and the depth to a shear wave velocity isosurface (zx). The depth components of such models are derived using data from sites within many geomorphic domains. We provide a site amplification model utilizing VS30 and depth, with the depth component conditioned on type of geomorphic province: basins, valleys, and mountain\/hills. As with current models, the depth component of our model is centered with respect to the VS30-scaling model using differential depth \u03b4zx, taken as the difference between a site-specific depth and a VS30-conditioned average depth. Using data from southern California, we find that long-period site response for all sites combined exhibits relative de-amplification and amplification for negative and positive differential depths, respectively. Individual provinces exhibit broadly similar trends with depth, but amplification levels are on average stronger in basins such that little relative de-amplification occurs at negative differential depths. Valley and mountain\/hill sites have, on average, weaker amplification levels but stronger scaling with \u03b4zx. Site-to-site standard deviations vary appreciably across geomorphic provinces, with basins having lower dispersions than mountain\/hill sites and the reference ergodic model.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
Ergodic site amplification models for active tectonic regions are conditioned on the time-averaged shear wave velocity in the upper 30\u2009m (VS30) and the depth to a shear wave velocity isosurface (zx). The depth components of such models are derived using data from sites within many geomorphic domains. We provide a site amplification model utilizing VS30 and depth, with the depth component conditioned on type of geomorphic province: basins, valleys, and mountain\/hills. As with current models, the depth component of our model is centered with respect to the VS30-scaling model using differential depth \u03b4zx, taken as the difference between a site-specific depth and a VS30-conditioned average depth. Using data from southern California, we find that long-period site response for all sites combined exhibits relative de-amplification and amplification for negative and positive differential depths, respectively. Individual provinces exhibit broadly similar trends with depth, but amplification levels are on average stronger in basins such that little relative de-amplification occurs at negative differential depths. Valley and mountain\/hill sites have, on average, weaker amplification levels but stronger scaling with \u03b4zx. Site-to-site standard deviations vary appreciably across geomorphic provinces, with basins having lower dispersions than mountain\/hill sites and the reference ergodic model.<\/div>
Close<\/a><\/p><\/div>
8.<\/td> Nweke, Chukwuebuka C; Stewart, Jonathan P; Graves, Robert W; Goulet, Christine A; Brandenberg, Scott J<\/span>: Validating Predicted Site Response in Sedimentary Basins from 3D Ground Motion Simulations<\/a><\/span>. In: <\/span>Earthquake Spectra, <\/span>2022<\/span>.<\/span> (Type: Journal Article<\/span> | Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span>)<\/span>
@article{doi:10.1177\/87552930211073159,
\r\ntitle = {Validating Predicted Site Response in Sedimentary Basins from 3D Ground Motion Simulations},
\r\nauthor = {Chukwuebuka C Nweke and Jonathan P Stewart and Robert W Graves and Christine A Goulet and Scott J Brandenberg},
\r\nurl = {https:\/\/doi.org\/10.1177\/87552930211073159},
\r\ndoi = {10.1177\/87552930211073159},
\r\nyear  = {2022},
\r\ndate = {2022-02-16},
\r\njournal = {Earthquake Spectra},
\r\nabstract = {We introduce procedures to validate site response in sedimentary basins as predicted using ground motion simulations. These procedures aim to isolate contributions of site response to computed intensity measures relative to those from seismic source and path effects. In one of the validation procedures, simulated motions are analyzed in the same manner as earthquake recordings to derive non-ergodic site terms. This procedure compares the scaling with sediment isosurface depth of simulated versus empirical site terms (the latter having been derived in a separate study). A second validation procedure utilizes two sets of simulations, one that considers three-dimensional (3D) basin structure and a second that utilizes a one-dimensional (1D) representation of the crustal structure. Identical sources are used in both procedures, and after correcting for variable path effects, differences in ground motions are used to estimate site amplification in 3D basins. Such site responses are compared to those derived empirically to validate both the absolute levels and the depth scaling of site response from 3D simulations. We apply both procedures to southern California in a manner that is consistent between the simulated and empirical data (i.e. by using similar event locations and magnitudes). The results show that the 3D simulations overpredict the depth-scaling and absolute levels of site amplification in basins. However, overall patterns of site amplification with depth are similar, suggesting that future calibration may be able to remove observed biases.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
We introduce procedures to validate site response in sedimentary basins as predicted using ground motion simulations. These procedures aim to isolate contributions of site response to computed intensity measures relative to those from seismic source and path effects. In one of the validation procedures, simulated motions are analyzed in the same manner as earthquake recordings to derive non-ergodic site terms. This procedure compares the scaling with sediment isosurface depth of simulated versus empirical site terms (the latter having been derived in a separate study). A second validation procedure utilizes two sets of simulations, one that considers three-dimensional (3D) basin structure and a second that utilizes a one-dimensional (1D) representation of the crustal structure. Identical sources are used in both procedures, and after correcting for variable path effects, differences in ground motions are used to estimate site amplification in 3D basins. Such site responses are compared to those derived empirically to validate both the absolute levels and the depth scaling of site response from 3D simulations. We apply both procedures to southern California in a manner that is consistent between the simulated and empirical data (i.e. by using similar event locations and magnitudes). The results show that the 3D simulations overpredict the depth-scaling and absolute levels of site amplification in basins. However, overall patterns of site amplification with depth are similar, suggesting that future calibration may be able to remove observed biases.<\/div>
Close<\/a><\/p><\/div>
7.<\/td> Omoya, Morolake; Ero, Itohan; Esteghamati, Mohsen Zaker; Burton, Henry V; Brandenberg, Scott; Sun, Han; Yi, Zhengxiang; Kang, Hua; Nweke, Chukuebuka C<\/span>: A relational database to support post-earthquake building damage and recovery assessment<\/a><\/span>. In: <\/span>Earthquake Spectra, <\/span>2022<\/span>.<\/span> (Type: Journal Article<\/span> | Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span>)<\/span>
@article{doi:10.1177\/87552930211061167,
\r\ntitle = {A relational database to support post-earthquake building damage and recovery assessment},
\r\nauthor = {Morolake Omoya and Itohan Ero and Mohsen Zaker Esteghamati and Henry V Burton and Scott Brandenberg and Han Sun and Zhengxiang Yi and Hua Kang and Chukuebuka C Nweke},
\r\nurl = {https:\/\/doi.org\/10.1177\/87552930211061167},
\r\ndoi = {10.1177\/87552930211061167},
\r\nyear  = {2022},
\r\ndate = {2022-01-27},
\r\njournal = {Earthquake Spectra},
\r\nabstract = {Systematically collected and curated data sets from historical events provide a strong basis for simulating the physical and functional effects of natural hazards on the built environment. This article develops a relational database to support post-earthquake damage and recovery modeling of building portfolios. The current version of the database has been populated with information on the 3695 buildings affected by the 2014 South Napa, California, earthquake. The associated data categories include general building characteristics, site properties and shaking intensities, building damage and repair permitting (timing and type) information, and census-block-level sociodemographics. The Napa data set can be used to validate post-earthquake recovery simulation methodologies and explore the effectiveness of different modeling techniques in predicting damage. The database can be expanded to include other earthquakes and the overall framework can be adapted to other types of natural hazards (e.g. hurricanes, flooding).},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
\r\n

2021<\/h3>\r\n <\/td>\r\n <\/tr>

6.<\/td> Goulet, Christine A.; Wang, Yongfei; Nweke, Chukwuebuka C.; Tang, Bo\u2010xiang; Wang, Pengfei; Hudson, Kenneth S.; Ahdi, Sean K.; Meng, Xiaofeng; Hudson, Martin B.; Donnellan, Andrea; Lyzenga, Gregory A.; Brandenberg, Scott J.; Stewart, Jonathan P.; Gallien, Timu; Winters, Maria A.<\/span>: Comparison of Near\u2010Fault Displacement Interpretations from Field and Aerial Data for the M\u00a06.5 and 7.1 Ridgecrest Earthquake Sequence Ruptures<\/a><\/span>. In: <\/span>Bulletin of the Seismological Society of America, <\/span>2021<\/span>, ISSN: 0037-1106<\/span>.<\/span> (Type: Journal Article<\/span> | Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span>)<\/span>
@article{doi:10.1785\/0120200222,
\r\ntitle = {Comparison of Near\u2010Fault Displacement Interpretations from Field and Aerial Data for the M\u00a06.5 and 7.1 Ridgecrest Earthquake Sequence Ruptures},
\r\nauthor = {Christine A. Goulet and Yongfei Wang and Chukwuebuka C. Nweke and Bo\u2010xiang Tang and Pengfei Wang and Kenneth S. Hudson and Sean K. Ahdi and Xiaofeng Meng and Martin B. Hudson and Andrea Donnellan and Gregory A. Lyzenga and Scott J. Brandenberg and Jonathan P. Stewart and Timu Gallien and Maria A. Winters},
\r\nurl = {https:\/\/doi.org\/10.1785\/0120200222},
\r\ndoi = {10.1785\/0120200222},
\r\nissn = {0037-1106},
\r\nyear  = {2021},
\r\ndate = {2021-08-24},
\r\nurldate = {2020-08-24},
\r\njournal = {Bulletin of the Seismological Society of America},
\r\nabstract = {Coseismic surface fault displacement presents a serious potential hazard for structures and for lifeline infrastructure. Distributed lifeline infrastructure tends to cover large distances and may cross faults in multiple locations, especially in active tectonic regions like California. However, fault displacement measurements for engineering applications are quite sparse, rendering the development of predictive models extremely difficult and fraught with large uncertainties. Detailed fault surface rupture mapping products exist for a few documented cases, but they may not capture the full width of ground deformations that are likely to impact distributed infrastructure. The 2019 Ridgecrest earthquake sequence presented an ideal opportunity to collect data and evaluate the ability of different techniques to capture coseismic deformations on and near the fault ruptures. Both the M\u00a06.5 and 7.1 events ruptured the surface in sparsely populated desert areas where little vegetation is present to obscure surficial features. Two study areas (~400\u00a0m\u00a0\u00d7\u00a0500\u00a0m each) around the surface ruptures from the two events were selected. Teams of researchers were deployed and coordinated to gather data in three ways: field measurements and photographs, imagery from small uninhabited aerial systems, and imagery from airborne light detection and ranging. Each of these techniques requires different amounts of resources in terms of cost, labor, and time associated with the data collection, processing, and interpretation efforts. This article presents the data collection methods used for the two study areas, and qualitative and quantitative comparisons of the results interpretations. While all three techniques capture the key features that are important for displacement design of distributed infrastructure, the use of remote sensing methods in combination with field measurements presents an advantage over the use of any single technique.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
Coseismic surface fault displacement presents a serious potential hazard for structures and for lifeline infrastructure. Distributed lifeline infrastructure tends to cover large distances and may cross faults in multiple locations, especially in active tectonic regions like California. However, fault displacement measurements for engineering applications are quite sparse, rendering the development of predictive models extremely difficult and fraught with large uncertainties. Detailed fault surface rupture mapping products exist for a few documented cases, but they may not capture the full width of ground deformations that are likely to impact distributed infrastructure. The 2019 Ridgecrest earthquake sequence presented an ideal opportunity to collect data and evaluate the ability of different techniques to capture coseismic deformations on and near the fault ruptures. Both the M\u00a06.5 and 7.1 events ruptured the surface in sparsely populated desert areas where little vegetation is present to obscure surficial features. Two study areas (~400\u00a0m\u00a0\u00d7\u00a0500\u00a0m each) around the surface ruptures from the two events were selected. Teams of researchers were deployed and coordinated to gather data in three ways: field measurements and photographs, imagery from small uninhabited aerial systems, and imagery from airborne light detection and ranging. Each of these techniques requires different amounts of resources in terms of cost, labor, and time associated with the data collection, processing, and interpretation efforts. This article presents the data collection methods used for the two study areas, and qualitative and quantitative comparisons of the results interpretations. While all three techniques capture the key features that are important for displacement design of distributed infrastructure, the use of remote sensing methods in combination with field measurements presents an advantage over the use of any single technique.<\/div>
Close<\/a><\/p><\/div>
5.<\/td> Ikeagwuani, Chijioke Christopher; Nwonu, Donald Chimobi; Nweke, Chukwuebuka C<\/span>: Resilient modulus descriptive analysis and estimation for fine-grained soils using multivariate and machine learning methods<\/a><\/span>. In: <\/span>International Journal of Pavement Engineering, <\/span>vol. 0, <\/span>no. 0, <\/span>pp. 1-16, <\/span>2021<\/span>.<\/span> (Type: Journal Article<\/span> | Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span>)<\/span>
@article{doi:10.1080\/10298436.2021.1895993,
\r\ntitle = {Resilient modulus descriptive analysis and estimation for fine-grained soils using multivariate and machine learning methods},
\r\nauthor = {Chijioke Christopher Ikeagwuani and Donald Chimobi Nwonu and Chukwuebuka C Nweke},
\r\nurl = {https:\/\/doi.org\/10.1080\/10298436.2021.1895993},
\r\ndoi = {10.1080\/10298436.2021.1895993},
\r\nyear  = {2021},
\r\ndate = {2021-01-01},
\r\njournal = {International Journal of Pavement Engineering},
\r\nvolume = {0},
\r\nnumber = {0},
\r\npages = {1-16},
\r\npublisher = {Taylor \\& Francis},
\r\nabstract = {ABSTRACTThe adoption of mechanistic-empirical approach to pavement design requires the use of resilient modulus of subgrade soils as a crucial input. The determination of in the laboratory is inexpedient due to the nature of the existing test protocols. This prompted the use of estimated values, which inadvertently has gained popularity lately. However, the accuracy of estimated values is questionable due to spatial variability of soil properties. This necessitated the aggressive search for robust and thorough approaches for predictive modelling of the . In the present study, a systematic approach was adopted for the descriptive analysis and estimation of . from routine soil properties using data from Long-Term Pavement Performance (LTPP) and considering the spatial variability of the soil properties. Descriptive analysis was executed using non-parametric correlation and principal component analysis (PCA), while the estimation was done using three machine learning methods which include gradient boosting regression (GBR), adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Based on the PCA, four factors which explained a total of 77.5% variance in the data had significant influence on the . These include the effect of moisture-induced changes on the soil consistency limits and physical condition, effect of the soil clay content, effect of the soil gradation and effect of the soil stress state. Various factors of the machine learning methods such as the learning rate, number of clusters and number of hidden layers had a significant effect on the prediction accuracy. The three machine learning methods were satisfactory for the prediction based on R2 values which were generally above 0.9. Also, when considering spatial variability of routine soil properties, the GBR and ANFIS have a comparative advantage over the ANN, since they exhibited a high stability in the prediction for both the training and testing dataset.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
ABSTRACTThe adoption of mechanistic-empirical approach to pavement design requires the use of resilient modulus of subgrade soils as a crucial input. The determination of in the laboratory is inexpedient due to the nature of the existing test protocols. This prompted the use of estimated values, which inadvertently has gained popularity lately. However, the accuracy of estimated values is questionable due to spatial variability of soil properties. This necessitated the aggressive search for robust and thorough approaches for predictive modelling of the . In the present study, a systematic approach was adopted for the descriptive analysis and estimation of . from routine soil properties using data from Long-Term Pavement Performance (LTPP) and considering the spatial variability of the soil properties. Descriptive analysis was executed using non-parametric correlation and principal component analysis (PCA), while the estimation was done using three machine learning methods which include gradient boosting regression (GBR), adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Based on the PCA, four factors which explained a total of 77.5% variance in the data had significant influence on the . These include the effect of moisture-induced changes on the soil consistency limits and physical condition, effect of the soil clay content, effect of the soil gradation and effect of the soil stress state. Various factors of the machine learning methods such as the learning rate, number of clusters and number of hidden layers had a significant effect on the prediction accuracy. The three machine learning methods were satisfactory for the prediction based on R2 values which were generally above 0.9. Also, when considering spatial variability of routine soil properties, the GBR and ANFIS have a comparative advantage over the ANN, since they exhibited a high stability in the prediction for both the training and testing dataset.<\/div>
Close<\/a><\/p><\/div>
\r\n

2020<\/h3>\r\n <\/td>\r\n <\/tr>

4.<\/td> Ahdi, Sean Kamran; Mazzoni, Silvia; Kishida, Tadahiro; Wang, Pengfei; Nweke, Chukwuebuka C.; Kuehn, Nicolas M.; Contreras, Victor; Rowshandel, Badie; Stewart, Jonathan P.; Bozorgnia, Yousef<\/span>: Engineering Characteristics of Ground Motions Recorded in the 2019 Ridgecrest Earthquake Sequence<\/a><\/span>. In: <\/span>Bulletin of the Seismological Society of America, <\/span>vol. 110, <\/span>no. 4, <\/span>pp. 1474-1494, <\/span>2020<\/span>, ISSN: 0037-1106<\/span>.<\/span> (Type: Journal Article<\/span> | Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span>)<\/span>
@article{doi:10.1785\/0120200036,
\r\ntitle = {Engineering Characteristics of Ground Motions Recorded in the 2019 Ridgecrest Earthquake Sequence},
\r\nauthor = {Sean Kamran Ahdi and Silvia Mazzoni and Tadahiro Kishida and Pengfei Wang and Chukwuebuka C. Nweke and Nicolas M. Kuehn and Victor Contreras and Badie Rowshandel and Jonathan P. Stewart and Yousef Bozorgnia},
\r\nurl = {https:\/\/doi.org\/10.1785\/0120200036},
\r\ndoi = {10.1785\/0120200036},
\r\nissn = {0037-1106},
\r\nyear  = {2020},
\r\ndate = {2020-07-21},
\r\njournal = {Bulletin of the Seismological Society of America},
\r\nvolume = {110},
\r\nnumber = {4},
\r\npages = {1474-1494},
\r\nabstract = {We present a database and analyze ground motions recorded during three events that occurred as part of the July 2019 Ridgecrest earthquake sequence: a moment magnitude (M) 6.5 foreshock on a left\u2010lateral cross fault in the Salt Wells Valley fault zone, an M\u00a05.5 foreshock in the Paxton Ranch fault zone, and the M\u00a07.1 mainshock, also occurring in the Paxton Ranch fault zone. We collected and uniformly processed 1483 three\u2010component recordings from an array of 824 sensors spanning 10 seismographic networks. We developed site metadata using available data and multiple models for the time\u2010averaged shear\u2010wave velocity in the upper 30\u00a0m (VS30) and for basin depth terms. We processed ground motions using Next Generation Attenuation (NGA) procedures and computed intensity measures including spectral acceleration at a number of oscillator periods and inelastic response spectra. We compared elastic and inelastic response spectra to seismic design spectra in building codes to evaluate the damage potential of the ground motions at spatially distributed sites. Residuals of the observed spectral accelerations relative to the NGA\u2010West2 ground\u2010motion models (GMMs) show good average agreement between observations and model predictions (event terms between about \u22120.3 and 0.5 for peak ground acceleration to 5\u00a0s). The average attenuation with distance is also well captured by the empirical NGA\u2010West2 GMMs, although azimuthal variations in attenuation were observed that are not captured by the GMMs. An analysis considering directivity and fault\u2010slip heterogeneity for the M\u00a07.1 event demonstrates that the dispersion in the near\u2010source ground\u2010motion residuals can be reduced.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
We present a database and analyze ground motions recorded during three events that occurred as part of the July 2019 Ridgecrest earthquake sequence: a moment magnitude (M) 6.5 foreshock on a left\u2010lateral cross fault in the Salt Wells Valley fault zone, an M\u00a05.5 foreshock in the Paxton Ranch fault zone, and the M\u00a07.1 mainshock, also occurring in the Paxton Ranch fault zone. We collected and uniformly processed 1483 three\u2010component recordings from an array of 824 sensors spanning 10 seismographic networks. We developed site metadata using available data and multiple models for the time\u2010averaged shear\u2010wave velocity in the upper 30\u00a0m (VS30) and for basin depth terms. We processed ground motions using Next Generation Attenuation (NGA) procedures and computed intensity measures including spectral acceleration at a number of oscillator periods and inelastic response spectra. We compared elastic and inelastic response spectra to seismic design spectra in building codes to evaluate the damage potential of the ground motions at spatially distributed sites. Residuals of the observed spectral accelerations relative to the NGA\u2010West2 ground\u2010motion models (GMMs) show good average agreement between observations and model predictions (event terms between about \u22120.3 and 0.5 for peak ground acceleration to 5\u00a0s). The average attenuation with distance is also well captured by the empirical NGA\u2010West2 GMMs, although azimuthal variations in attenuation were observed that are not captured by the GMMs. An analysis considering directivity and fault\u2010slip heterogeneity for the M\u00a07.1 event demonstrates that the dispersion in the near\u2010source ground\u2010motion residuals can be reduced.<\/div>
Close<\/a><\/p><\/div>
3.<\/td> Zimmaro, Paolo; Nweke, Chukwuebuka C.; Hernandez, Janis L.; Hudson, Kenneth S.; Hudson, Martin B.; Ahdi, Sean K.; Boggs, Matthew L.; Davis, Craig A.; Goulet, Christine A.; Brandenberg, Scott J.; Hudnut, Kenneth W.; Stewart, Jonathan P.<\/span>: Liquefaction and Related Ground Failure from July 2019 Ridgecrest Earthquake Sequence<\/a><\/span>. In: <\/span>Bulletin of the Seismological Society of America, <\/span>vol. 110, <\/span>no. 4, <\/span>pp. 1549-1566, <\/span>2020<\/span>, ISSN: 0037-1106<\/span>.<\/span> (Type: Journal Article<\/span> | Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span>)<\/span>
@article{doi:10.1785\/0120200025,
\r\ntitle = {Liquefaction and Related Ground Failure from July 2019 Ridgecrest Earthquake Sequence},
\r\nauthor = {Paolo Zimmaro and Chukwuebuka C. Nweke and Janis L. Hernandez and Kenneth S. Hudson and Martin B. Hudson and Sean K. Ahdi and Matthew L. Boggs and Craig A. Davis and Christine A. Goulet and Scott J. Brandenberg and Kenneth W. Hudnut and Jonathan P. Stewart},
\r\nurl = {https:\/\/doi.org\/10.1785\/0120200025},
\r\ndoi = {10.1785\/0120200025},
\r\nissn = {0037-1106},
\r\nyear  = {2020},
\r\ndate = {2020-07-21},
\r\njournal = {Bulletin of the Seismological Society of America},
\r\nvolume = {110},
\r\nnumber = {4},
\r\npages = {1549-1566},
\r\nabstract = {The 2019 Ridgecrest earthquake sequence produced a 4 July M\u00a06.5 foreshock and a 5 July M\u00a07.1 mainshock, along with 23 events with magnitudes greater than 4.5 in the 24\u00a0hr period following the mainshock. The epicenters of the two principal events were located in the Indian Wells Valley, northwest of Searles Valley near the towns of Ridgecrest, Trona, and Argus. We describe observed liquefaction manifestations including sand boils, fissures, and lateral spreading features, as well as proximate non\u2010ground failure zones that resulted from the sequence. Expanding upon results initially presented in a report of the Geotechnical Extreme Events Reconnaissance Association, we synthesize results of field mapping, aerial imagery, and inferences of ground deformations from Synthetic Aperture Radar\u2010based damage proxy maps (DPMs). We document incidents of liquefaction, settlement, and lateral spreading in the Naval Air Weapons Station China Lake US military base and compare locations of these observations to pre\u2010 and postevent mapping of liquefaction hazards. We describe liquefaction and ground\u2010failure features in Trona and Argus, which produced lateral deformations and impacts on several single\u2010story masonry and wood frame buildings. Detailed maps showing zones with and without ground failure are provided for these towns, along with mapped ground deformations along transects. Finally, we describe incidents of massive liquefaction with related ground failures and proximate areas of similar geologic origin without ground failure in the Searles Lakebed. Observations in this region are consistent with surface change predicted by the DPM. In the same region, geospatial liquefaction hazard maps are effective at identifying broad percentages of land with liquefaction\u2010related damage. We anticipate that data presented in this article will be useful for future liquefaction susceptibility, triggering, and consequence studies being undertaken as part of the Next Generation Liquefaction project.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
The 2019 Ridgecrest earthquake sequence produced a 4 July M\u00a06.5 foreshock and a 5 July M\u00a07.1 mainshock, along with 23 events with magnitudes greater than 4.5 in the 24\u00a0hr period following the mainshock. The epicenters of the two principal events were located in the Indian Wells Valley, northwest of Searles Valley near the towns of Ridgecrest, Trona, and Argus. We describe observed liquefaction manifestations including sand boils, fissures, and lateral spreading features, as well as proximate non\u2010ground failure zones that resulted from the sequence. Expanding upon results initially presented in a report of the Geotechnical Extreme Events Reconnaissance Association, we synthesize results of field mapping, aerial imagery, and inferences of ground deformations from Synthetic Aperture Radar\u2010based damage proxy maps (DPMs). We document incidents of liquefaction, settlement, and lateral spreading in the Naval Air Weapons Station China Lake US military base and compare locations of these observations to pre\u2010 and postevent mapping of liquefaction hazards. We describe liquefaction and ground\u2010failure features in Trona and Argus, which produced lateral deformations and impacts on several single\u2010story masonry and wood frame buildings. Detailed maps showing zones with and without ground failure are provided for these towns, along with mapped ground deformations along transects. Finally, we describe incidents of massive liquefaction with related ground failures and proximate areas of similar geologic origin without ground failure in the Searles Lakebed. Observations in this region are consistent with surface change predicted by the DPM. In the same region, geospatial liquefaction hazard maps are effective at identifying broad percentages of land with liquefaction\u2010related damage. We anticipate that data presented in this article will be useful for future liquefaction susceptibility, triggering, and consequence studies being undertaken as part of the Next Generation Liquefaction project.<\/div>
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2.<\/td> Brandenberg, Scott J.; Stewart, Jonathan P.; Wang, Pengfei; Nweke, Chukwuebuka C.; Hudson, Kenneth; Goulet, Christine A.; Meng, Xiaofeng; Davis, Craig A.; Ahdi, Sean K.; Hudson, Martin B.; Donnellan, Andrea; Lyzenga, Gregory; Pierce, Marlon; Wang, Jun; Winters, Maria A.; Delisle, Marie\u2010Pierre; Lucey, Joseph; Kim, Yeulwoo; and Timu W. Gallien,; Lyda, Andrew; Yeung, Sean J.; Issa, Omar; Buckreis, Tristan; Yi, Zhengxiang<\/span>: Ground Deformation Data from GEER Investigations of Ridgecrest Earthquake Sequence<\/a><\/span>. In: <\/span>Seismological Research Letters, <\/span>vol. 91, <\/span>no. 4, <\/span>pp. 2024-2034, <\/span>2020<\/span>, ISSN: 0895-0695<\/span>.<\/span> (Type: Journal Article<\/span> | Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span>)<\/span>
@article{doi:10.1785\/0220190291,
\r\ntitle = {Ground Deformation Data from GEER Investigations of Ridgecrest Earthquake Sequence},
\r\nauthor = {Scott J. Brandenberg and Jonathan P. Stewart and Pengfei Wang and Chukwuebuka C. Nweke and Kenneth Hudson and Christine A. Goulet and Xiaofeng Meng and Craig A. Davis and Sean K. Ahdi and Martin B. Hudson and Andrea Donnellan and Gregory Lyzenga and Marlon Pierce and Jun Wang and Maria A. Winters and Marie\u2010Pierre Delisle and Joseph Lucey and Yeulwoo Kim and and Timu W. Gallien and Andrew Lyda and Sean J. Yeung and Omar Issa and Tristan Buckreis and Zhengxiang Yi},
\r\nurl = {https:\/\/doi.org\/10.1785\/0220190291},
\r\ndoi = {10.1785\/0220190291},
\r\nissn = {0895-0695},
\r\nyear  = {2020},
\r\ndate = {2020-02-19},
\r\njournal = {Seismological Research Letters},
\r\nvolume = {91},
\r\nnumber = {4},
\r\npages = {2024-2034},
\r\nabstract = {Following the Ridgecrest earthquake sequence, consisting of an M\u00a06.4 foreshock and M\u00a07.1 mainshock along with many other events, the Geotechnical Extreme Events Reconnaissance association deployed a team to gather perishable data. The team focused their efforts on documenting ground deformations including surface fault rupture south of the Naval Air Weapons Station China Lake, and liquefaction features in Trona and Argus. The team published a report within two weeks of the M\u00a07.1 mainshock. This article presents data products gathered by the team, which are now published and publicly accessible. The data products presented herein include ground\u2010based observations using Global Positioning System trackers, digital cameras, and hand\u2010measuring devices, as well as unmanned aerial vehicle\u2010based imaging products using Structure from Motion to create point clouds and digital surface models. The article describes the data products, as well as tools available for interacting with the products.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
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1.<\/td> Mangalathu, Sujith; Sun, Han; Nweke, Chukwuebuka C.; Yi, Zhengxiang; Burton, Henry V.<\/span>: Classifying earthquake damage to buildings using machine learning<\/a><\/span>. In: <\/span>Earthquake Spectra, <\/span>vol. 36, <\/span>no. 1, <\/span>pp. 183-208, <\/span>2020<\/span>.<\/span> (Type: Journal Article<\/span> | Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span>)<\/span>
@article{doi:10.1177\/8755293019878137,
\r\ntitle = {Classifying earthquake damage to buildings using machine learning},
\r\nauthor = {Sujith Mangalathu and Han Sun and Chukwuebuka C. Nweke and Zhengxiang Yi and Henry V. Burton},
\r\nurl = {https:\/\/doi.org\/10.1177\/8755293019878137},
\r\ndoi = {10.1177\/8755293019878137},
\r\nyear  = {2020},
\r\ndate = {2020-01-29},
\r\njournal = {Earthquake Spectra},
\r\nvolume = {36},
\r\nnumber = {1},
\r\npages = {183-208},
\r\nabstract = {The ability to rapidly assess the spatial distribution and severity of building damage is essential to post-event emergency response and recovery. Visually identifying and classifying individual building damage requires significant time and personnel resources and can last for months after the event. This article evaluates the feasibility of using machine learning techniques such as discriminant analysis, k-nearest neighbors, decision trees, and random forests, to rapidly predict earthquake-induced building damage. Data from the 2014 South Napa earthquake are used for the study where building damage is classified based on the assigned Applied Technology Council (ATC)-20 tag (red, yellow, and green). Spectral acceleration at a period of 0.3\u2009s, fault distance, and several building specific characteristics (e.g. age, floor area, presence of plan irregularity) are used as features or predictor variables for the machine learning models. A portion of the damage data from the Napa earthquake is used to obtain the forecast model, and the performance of each machine learning technique is evaluated using the remaining (test) data. It is noted that the random forest algorithm can accurately predict the assigned tags for 66% of the buildings in the test dataset.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
The ability to rapidly assess the spatial distribution and severity of building damage is essential to post-event emergency response and recovery. Visually identifying and classifying individual building damage requires significant time and personnel resources and can last for months after the event. This article evaluates the feasibility of using machine learning techniques such as discriminant analysis, k-nearest neighbors, decision trees, and random forests, to rapidly predict earthquake-induced building damage. Data from the 2014 South Napa earthquake are used for the study where building damage is classified based on the assigned Applied Technology Council (ATC)-20 tag (red, yellow, and green). Spectral acceleration at a period of 0.3\u2009s, fault distance, and several building specific characteristics (e.g. age, floor area, presence of plan irregularity) are used as features or predictor variables for the machine learning models. A portion of the damage data from the Napa earthquake is used to obtain the forecast model, and the performance of each machine learning technique is evaluated using the remaining (test) data. It is noted that the random forest algorithm can accurately predict the assigned tags for 66% of the buildings in the test dataset.<\/div>
Close<\/a><\/p><\/div>