PUBLICATIONS

@article{doi:10.1007/s12517-023-11469-z,

title = {Prediction of resilient modulus of fine-grained soil for pavement design using KNN, MARS, and random forest techniques},

author = {Christopher C Ikeagwuani and Chukwuebuka C Nweke and Hyginus N Onah},

url = {https://doi.org/10.1007/s12517-023-11469-z},

doi = {10.1007/s12517-023-11469-z},

year  = {2023},

date = {2023-05-27},

journal = {Arabian Journal of Geosciences},

volume = {16},

number = {388},

abstract = {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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1061/JGGEFK.GTENG-11522,

title = {The 2022 Chihshang, Taiwan, Earthquake: Initial GEER Team Observations},

author = {Trevor J Carey and Henry B Mason and Dominiki Asikmaki and Adda Athanasopoulos-Zekkos and Fernando E Garcia and Brian Gray and Grigorios Lavrentiadis and Chukwuebuka C Nweke},

url = {https://doi.org/10.1061/JGGEFK.GTENG-11522},

doi = {10.1061/JGGEFK.GTENG-11522},

year  = {2023},

date = {2023-03-07},

journal = {Journal of Geotechnical and Geoenvironmental Engineering},

volume = {149},

number = {5},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1177/87552930221088609,

title = {Site response of sedimentary basins and other geomorphic provinces in southern California},

author = {Chukwuebuka C Nweke and Jonathan P Stewart and Pengfei Wang and Scott J Brandenberg},

url = {https://doi.org/10.1177/87552930221088609},

doi = {10.1177/87552930221088609},

year  = {2022},

date = {2022-05-31},

journal = {Earthquake Spectra},

abstract = {Ergodic site amplification models for active tectonic regions are conditioned on the time-averaged shear wave velocity in the upper 30 m (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 δzx, 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 δzx. Site-to-site standard deviations vary appreciably across geomorphic provinces, with basins having lower dispersions than mountain/hill sites and the reference ergodic model.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1177/87552930211073159,

title = {Validating Predicted Site Response in Sedimentary Basins from 3D Ground Motion Simulations},

author = {Chukwuebuka C Nweke and Jonathan P Stewart and Robert W Graves and Christine A Goulet and Scott J Brandenberg},

url = {https://doi.org/10.1177/87552930211073159},

doi = {10.1177/87552930211073159},

year  = {2022},

date = {2022-02-16},

journal = {Earthquake Spectra},

abstract = {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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1177/87552930211061167,

title = {A relational database to support post-earthquake building damage and recovery assessment},

author = {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},

url = {https://doi.org/10.1177/87552930211061167},

doi = {10.1177/87552930211061167},

year  = {2022},

date = {2022-01-27},

journal = {Earthquake Spectra},

abstract = {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).},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1785/0120200222,

title = {Comparison of Near‐Fault Displacement Interpretations from Field and Aerial Data for the M 6.5 and 7.1 Ridgecrest Earthquake Sequence Ruptures},

author = {Christine A. Goulet and Yongfei Wang and Chukwuebuka C. Nweke and Bo‐xiang 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},

url = {https://doi.org/10.1785/0120200222},

doi = {10.1785/0120200222},

issn = {0037-1106},

year  = {2021},

date = {2021-08-24},

urldate = {2020-08-24},

journal = {Bulletin of the Seismological Society of America},

abstract = {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 6.5 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 m × 500 m 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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1080/10298436.2021.1895993,

title = {Resilient modulus descriptive analysis and estimation for fine-grained soils using multivariate and machine learning methods},

author = {Chijioke Christopher Ikeagwuani and Donald Chimobi Nwonu and Chukwuebuka C Nweke},

url = {https://doi.org/10.1080/10298436.2021.1895993},

doi = {10.1080/10298436.2021.1895993},

year  = {2021},

date = {2021-01-01},

journal = {International Journal of Pavement Engineering},

volume = {0},

number = {0},

pages = {1-16},

publisher = {Taylor \& Francis},

abstract = {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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1785/0120200025,

title = {Liquefaction and Related Ground Failure from July 2019 Ridgecrest Earthquake Sequence},

author = {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},

url = {https://doi.org/10.1785/0120200025},

doi = {10.1785/0120200025},

issn = {0037-1106},

year  = {2020},

date = {2020-07-21},

journal = {Bulletin of the Seismological Society of America},

volume = {110},

number = {4},

pages = {1549-1566},

abstract = {The 2019 Ridgecrest earthquake sequence produced a 4 July M 6.5 foreshock and a 5 July M 7.1 mainshock, along with 23 events with magnitudes greater than 4.5 in the 24 hr 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‐ground 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‐based 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‐ and postevent mapping of liquefaction hazards. We describe liquefaction and ground‐failure features in Trona and Argus, which produced lateral deformations and impacts on several single‐story 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‐related 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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1785/0120200036,

title = {Engineering Characteristics of Ground Motions Recorded in the 2019 Ridgecrest Earthquake Sequence},

author = {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},

url = {https://doi.org/10.1785/0120200036},

doi = {10.1785/0120200036},

issn = {0037-1106},

year  = {2020},

date = {2020-07-21},

journal = {Bulletin of the Seismological Society of America},

volume = {110},

number = {4},

pages = {1474-1494},

abstract = {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‐lateral cross fault in the Salt Wells Valley fault zone, an M 5.5 foreshock in the Paxton Ranch fault zone, and the M 7.1 mainshock, also occurring in the Paxton Ranch fault zone. We collected and uniformly processed 1483 three‐component recordings from an array of 824 sensors spanning 10 seismographic networks. We developed site metadata using available data and multiple models for the time‐averaged shear‐wave velocity in the upper 30 m (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‐West2 ground‐motion models (GMMs) show good average agreement between observations and model predictions (event terms between about −0.3 and 0.5 for peak ground acceleration to 5 s). The average attenuation with distance is also well captured by the empirical NGA‐West2 GMMs, although azimuthal variations in attenuation were observed that are not captured by the GMMs. An analysis considering directivity and fault‐slip heterogeneity for the M 7.1 event demonstrates that the dispersion in the near‐source ground‐motion residuals can be reduced.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1785/0220190291,

title = {Ground Deformation Data from GEER Investigations of Ridgecrest Earthquake Sequence},

author = {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‐Pierre 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},

url = {https://doi.org/10.1785/0220190291},

doi = {10.1785/0220190291},

issn = {0895-0695},

year  = {2020},

date = {2020-02-19},

journal = {Seismological Research Letters},

volume = {91},

number = {4},

pages = {2024-2034},

abstract = {Following the Ridgecrest earthquake sequence, consisting of an M 6.4 foreshock and M 7.1 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 7.1 mainshock. This article presents data products gathered by the team, which are now published and publicly accessible. The data products presented herein include ground‐based observations using Global Positioning System trackers, digital cameras, and hand‐measuring devices, as well as unmanned aerial vehicle‐based 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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1177/8755293019878137,

title = {Classifying earthquake damage to buildings using machine learning},

author = {Sujith Mangalathu and Han Sun and Chukwuebuka C. Nweke and Zhengxiang Yi and Henry V. Burton},

url = {https://doi.org/10.1177/8755293019878137},

doi = {10.1177/8755293019878137},

year  = {2020},

date = {2020-01-29},

journal = {Earthquake Spectra},

volume = {36},

number = {1},

pages = {183-208},

abstract = {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 s, 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.},

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pubstate = {published},

tppubtype = {article}

}