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1. Predictive modeling of rocking-induced settlement in shallow foundations using ensemble machine learning and neural networks

   https://doi.org/10.3389/fbuil.2024.1402619  

   Gajan, Sivapalan (June 2024, Frontiers in Built Environment)

   IntroductionThe objective of this study is to develop predictive models for rocking-induced permanent settlement in shallow foundations during earthquake loading using stacking, bagging and boosting ensemble machine learning (ML) and artificial neural network (ANN) models. MethodsThe ML models are developed using supervised learning technique and results obtained from rocking foundation experiments conducted on shaking tables and centrifuges. The overall performance of ML models are evaluated using k-fold cross validation tests and mean absolute percentage error (MAPE) and mean absolute error (MAE) in their predictions. ResultsThe performances of all six nonlinear ML models developed in this study are relatively consistent in terms of prediction accuracy with their average MAPE varying between 0.64 and 0.86 in final k-fold cross validation tests. DiscussionThe overall average MAE in predictions of all nonlinear ML models are smaller than 0.006, implying that the ML models developed in this study have the potential to predict permanent settlement of rocking foundations with reasonable accuracy in practical applications. 

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2. Prediction of Acceleration Amplification Ratio of Rocking Foundations Using Machine Learning and Deep Learning Models

   https://doi.org/10.3390/app132312791  

   Gajan, Sivapalan (December 2023, Applied Sciences)

   Experimental results reveal that rocking shallow foundations reduce earthquake-induced force and flexural displacement demands transmitted to structures and can be used as an effective geotechnical seismic isolation mechanism. This paper presents data-driven predictive models for maximum acceleration transmitted to structures founded on rocking shallow foundations during earthquake loading. Results from base-shaking experiments on rocking foundations have been utilized for the development of artificial neural network regression (ANN), k-nearest neighbors regression, support vector regression, random forest regression, adaptive boosting regression, and gradient boosting regression models. Acceleration amplification ratio, defined as the maximum acceleration at the center of gravity of a structure divided by the peak ground acceleration of the earthquake, is considered as the prediction parameter. For five out of six models developed in this study, the overall mean absolute percentage error in predictions in repeated k-fold cross validation tests vary between 0.128 and 0.145, with the ANN model being the most accurate and most consistent. The cross validation mean absolute error in predictions of all six models vary between 0.08 and 0.1, indicating that the maximum acceleration of structures supported by rocking foundations can be predicted within an average error limit of 8% to 10% of the peak ground acceleration of the earthquake. 

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3. RapidLiq: Software for Near-Real-Time Prediction of Soil Liquefaction

   https://doi.org/10.17603/ds2-4bka-y039  

   Geyin, Mertcan; Maurer, Brett (January 2021, Designsafe-CI)

   RapidLiq is a Windows software program for predicting liquefaction-induced ground failure using geospatial models, which are particularly suited for regional scale applications such as: (i) loss estimation and disaster simulation; (ii) city planning and policy development; (iii) emergency response; and (d) post-event reconnaissance (e.g., to remotely identify sites of interest). RapidLiq v1.0 includes four such models. One is a logistic regression model developed by Rashidian and Baise (2020), which has been adopted into United States Geological Survey (USGS) post-earthquake data products, but which is not often implemented by individuals owing to the geospatial variables that must be compiled. The other three models are machine and deep learning models (ML/DL) proposed by Geyin et al. (2021). These models are driven by algorithmic learning (benefiting from ML/DL insights) but pinned to a physical framework (benefiting from mechanics and the knowledge of regression modelers). While liquefaction is a physical phenomenon best predicted by mechanics, subsurface traits lack theoretical links to above-ground parameters, but correlate in complex, interconnected ways - a prime problem for ML/DL. All four models are described in an acompanying paper manuscript. All necessary predictor variables are compiled within RapidLiq, making user implementation trivial. The only required input is a ground motion raster easily downloaded within minutes of an earthquake, or available for enumerable future earthquake scenarios. This gives the software near-real-time capabilities, such that ground failure can be predicted at regional scale within minutes of an earthquake. The software outputs geotiff files mapping the probabilities of liquefaction-induced ground failure. These files may be viewed within the software or explored in greater detail using GIS or one of many free geotiff web explorers (e.g., http://app.geotiff.io/). The software also allows for tabular input, should a user wish to enter specific sites of interest and ground-motion parameters at those sites, rather than study the regional effects of an earthquake. RapidLiq v.1.0 operates in the contiguous U.S. and completes predictions within 10 seconds for most events. 

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4. Modeling of Rocking Induced Permanent Settlement of Shallow Foundations Using Machine Learning Algorithms

   https://doi.org/10.1061/9780784484685.061  

   Gajan, Sivapalan (March 2023, Geo-Congress 2023)

   The objective of this study is to develop data-driven predictive models for permanent settlement of rocking shallow foundations during seismic loading using multiple machine learning algorithms and supervised learning technique. Data from a rocking foundation database consisting of dynamic base shaking experiments conducted on centrifuges and shaking tables have been used for the development of k-nearest neighbors regression, support vector regression, and random forest regression models. Based on repeated k-fold cross validation tests of models and mean absolute percentage errors in their predictions, it is found that all three models perform better than a baseline multivariate linear regression model in terms of accuracy and variance in predictions. The average mean absolute errors in predictions of all three models are around 0.005 to 0.006, indicating that the rocking induced permanent settlement can be predicted within an average error limit of 0.5% to 0.6% of the width of the footing. 

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5. Using Machine Learning to Predict Collapse of Steel Structures Under Seismic Loading

   Gaire, Prakash; Ibarra, Luis (June 2024, Engineering Archive)

   This study applies machine learning (ML) models to predict the collapse limit state of steel moment resisting frame (SMRF) buildings, considering uncertainties in system parameters and input ground motion characteristics. Structural global collapse is affected by a large number of linear and nonlinear system parameters. One of the main goals of the study is to find the effectiveness of ML methods to predict collapse, as the number of system’s features is reduced. Because of the lack of sufficient experimental data, an ML approach is followed in which three code-compliant SMRF buildings of varying heights (2, 4 and 8 stories), are evaluated up to the collapse limit state, using nonlinear time history analyses. Variability in system parameters and ground motions, as well as potential correlation among some of the parameters, is considered to generate a database of more than 19,000 realizations of collapsed and non-collapsed systems. The ML models are trained and tested with this database, and the efficiency of the models is categorized using different metrics, such as accuracy, F1-score, precision, and recall. Six different ML classification-based techniques are employed to predict collapse, finding that boosting algorithms (eg, AdaBoost and XGBoost) are the best methods for collapse status classification of the evaluated structural systems. Permutation feature importance is applied to identify the main contributors to collapse. The ML models are then retrained using less features, considering first removal of nonlinear deteriorating parameters, and then removal of the hardening nonlinear parameters. 

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