Aug 2026· Buildings· Vol 16, pp. 3352· 0 citations· 26 references
Abstract
To overcome the dual bottlenecks of scarce actual strong earthquake records and high computational costs of nonlinear time history analysis, this study proposes a fast prediction method for structural nonlinear response that integrates systematic seismic sample expansion and machine learning technology by studying mature methods in the industry. A total of 500 ground motion records were created through the application of the amplitude scaling approach. Subsequently, the development of the steel frame structure was carried out through the application of the Abaqus software(Abaqus 2021 Edition) for the purpose of carrying out the nonlinear time history analysis to obtain the maximum interstory drift ratio (IDR) as the target response parameter. The XGBoost model was optimized to obtain improved results through the application of various evaluation criteria. Subsequently, the Shapley Additive exPlanations (SHAP) tool was applied to “open the black box” model to obtain the coupled effect of the various parameters, including displacement-related intensity measures such as RMSD and PGD on the maximum IDR during significant structure deformations. The method developed within this research has the potential to be a powerful tool for the prediction of the seismic responses. The method can be used in many areas, including probabilistic seismic demand, fragility assessment, and rapid evaluation of earthquake damage.
The proposed AFELA–machine learning framework provides a computationally efficient, reliable, and interpretable approach for tunnel stability assessment in sloping rock mass conditions.
A. Kumar, V. Chauhan, Aayush Kumar et al.· Transportation Infrastructur...· 1 citation
Accurate prediction of the residual drift ratio of reinforced concrete bridge piers is challenging because conventional methods are computationally expensive, time-consuming, and unable to effectively capture complex nonlinear interactions among multiple influencing factors. To address these limitations, this study proposes an interpretable machine learning framework for predicting the residual drift ratio of reinforced concrete bridge piers. A comprehensive database containing 261 quasi-static experimental datasets was established, incorporating key structural and material parameters, including axial com-pression ratio, shear span ratio, stirrup ratio, longitudinal reinforcement ratio, material strengths, and geometric dimensions. Based on this database, six representative machine learning models were developed and systematically compared. Their predictive performance, robustness, and generalization capability were evaluated using multiple statistical metrics and Monte Carlo simulations. The results show that the CatBoost model consistently outperformed the other models, achieving an R2 value of 0.9629 on the test set while maintaining excellent stability under random data partitions. Furthermore, SHAP analysis was employed to interpret the trained model and quantify the contributions of individual input variables. Eight key factors influencing the residual drift ratio were identified, with the loading displacement ratio (θ) exhibiting the greatest influence. These findings demonstrate that the proposed framework provides an accurate, reliable, and interpretable tool for predicting the post-earthquake residual drift ratio of reinforced concrete bridge piers, offering valuable support for performance-based seismic design, post-earthquake damage assessment, and resilience-based bridge engineering.
Min Zhang, Xuefeng Zhang, Liang-Jun Li et al.· Buildings· 0 citations
Predicting the nonlinear seismic response of structures that have entered the plastic range under strong ground motions is severely constrained by data scarcity and computational cost. In this article, to address this dual challenge, we propose a physics-guided ensemble model based on Support Vector Regression. A finite element model of a single-story steel structure was created, and 500 nonlinear time-series analyses were generated using Incremental Dynamic Analysis for 50 different natural ground motions, at 10 levels of PGA intensity. Using an innovative feature engineering strategy, the 16 original ground motion parameters were decomposed into intensity, waveform and interaction features, thereby expanding the input space to 47 physically meaningful dimensions. Subsequently, the 35 features with the greatest information richness were extracted using a selection process based on mutual information. A systematic comparison with benchmark models demonstrated that Support Vector Regression (SVR) with Radial Basis Function (RBF) kernels offered significantly superior performance to Deep Learning with a reduced number of samples for this task, thus confirming the superiority of the structural risk minimization principle under conditions of limited data. Furthermore, the proposed two-level stacked ensemble achieved the lowest Mean Absolute Error among all evaluated models, demonstrating improved robustness in reducing prediction deviations and suppressing extreme errors in nonlinear seismic response estimation. These results demonstrate that the combination of physics-guided feature engineering and kernel-based learning provides an efficient surrogate approach for rapid seismic response prediction of steel structures under previously characterized ground-motion conditions.
Wan-Qi Zheng, Aifu Sun, Han-Wei Wang et al.· Buildings· 0 citations
A systematic engineering framework is presented that translates established machine-learning techniques into a noise-resilient deep neural network (NR-DNN) for post-earthquake building assessment using noisy strain sensor data. The proposed model is built upon four major mechanisms: (1) noise‑aware training using a multiplicative synthetic noise model (calibration error, thermal drift, random perturbations), (2) dropout, (3) Bayesian hyperparameter tuning with K‑fold cross‑validation (CV), and (4) ensemble averaging. An internal ablation study is performed to show that simultaneous incorporation of these mechanisms yields the best results. Random Forest (RF) is used to identify the best locations for strain monitoring. The Performance of the model is investigated on two SMRF case studies using nonlinear time history analyses (NTHAs) of 58 ground motion records at immediate occupancy (IO) and life safety (LS) performance levels, plus 43 out-of-sample collapse level records. The model predicts full field strains with a limited number of strain sensors under highly nonlinear structural responses caused by unseen out-of-sample earthquake excitations. Compared with a conventional DNN, the proposed model reduces root mean squared error (RMSE) by up to 90 % under noisy conditions and maintains high damage state classification accuracy. The sensitivity analyses validate the framework's stability both under varying noise levels and under threshold variations in damage state classification. The results confirm that, unlike a DNN trained without appropriate noise-handling mechanisms, the proposed NR-DNN model remains numerically stable under noisy conditions. This validation is numerical, based on a synthetic noise model; experimental field validation is left for future work.
Vahid Mokarram· Turkish Journal of Civil Eng...· 0 citations
With the continuous development of drilling technology, accurately predicting mechanical penetration rates is particularly important for improving operational efficiency and reducing costs. Existing methods often struggle to provide reliable predictions when faced with complex geological conditions and variable drilling environments because they primarily rely on traditional models and fail to adequately consider various influencing factors and their nonlinear relationships. To ad-dress these issues, this paper proposes a mechanical penetration rate prediction model based on committee machines. This model effectively captures the variability characteristics of mechanical penetration rates by integrating multiple expert models while employing wavelet filtering methods to denoise the data to enhance data quality. In the application case, this paper collects relevant drilling parameter data based on a vertical well in a specific block. The evaluation of the model shows that it performs excellently in key indicators such as mean square error, coefficient of determination, root mean square error, and mean absolute error, particularly demonstrating a high predictive capability and stability by explaining 97.19% of data variability. The advantage of the constructed model lies in its strong ensemble learning ability, which not only enhances the prediction accuracy of mechanical penetration rates but also helps to deepen the understanding of the dynamic changes in the drilling process, providing effective support for subsequent drilling optimization and resource development.
Tao Cai, Huai-Yan Qi, Xue-Wu Yang et al.· Journal of Physics, Conferen...· 0 citations
This study presents three advanced machine learning models: the evolutionary Gaussian process inference model, the artificial satellite search algorithm–moment balance machine (ASSA-MBM), and the Operation Rain Forest (ORF), which are designed to predict the maximum reinforcement load in geosynthetic-reinforced soil structures. These models were developed to enhance both predictive accuracy and model interpretability by incorporating state-of-the-art optimization algorithms and explainable machine learning frameworks. A comprehensive evaluation was conducted using 10-fold cross-validation, and the proposed models were benchmarked against previously developed AI models from literature, as well as traditional and semiempirical approaches such as Rankine, Coulomb, and
K
-stiffness. Among the proposed models, ASSA-MBM consistently achieved the best performance, recording the lowest testing root mean squared error (0.617), the highest correlation coefficient (
R
=
0.918
), and the highest reference index (
RI
=
0.951
). Additionally, the ORF model offers transparency by generating mathematical regression equations, which are crucial in geotechnical engineering.
Min-Yuan Cheng, Akhmad F. K. Khitam, Jia-Wang Liou· Journal of computing in civi...· 0 citations
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