• Structural Engineering and Mechanics
• /
• 제83권3호
• /
• pp.401-413
• /
• 2022

The collapse of civil infrastructure due to natural disasters results in financial losses and many casualties. In particular, the recent increase in earthquake activities has highlighted on the importance of assessing the seismic performance and predicting the seismic risk of a structure. However, the nonlinear behavior of a structure and the uncertainty in ground motion complicate the accurate seismic response prediction of a structure. Artificial intelligence can overcome these limitations to reasonably predict the nonlinear behavior of structures. In this study, a deep learning-based algorithm was developed to estimate the time-history seismic response of bridge structures. The proposed deep neural network was trained using structural and ground motion parameters. The performance of the seismic response prediction algorithm showed the similar phase and magnitude to those of the time-history analysis in a single-degree-of-freedom system that exhibits nonlinear behavior as a main structural element. Then, the proposed algorithm was expanded to predict the seismic response and fragility prediction of a bridge system. The proposed deep neural network reasonably predicted the nonlinear seismic behavior of piers and bearings for approximately 93% and 87% of the test dataset, respectively. The results of the study also demonstrated that the proposed algorithm can be utilized to assess the seismic fragility of bridge components and system.

• 한국지진공학회논문집
• /
• 제22권2호
• /
• pp.77-86
• /
• 2018

This study develops an empirical prediction equation of spectral acceleration responses of earthquakes which can induce structural damages. Ground motion records representing hazards of low-to-moderate seismic regions were selected and organized with several influential factors affecting the response spectra. The empirical equation and estimator coefficients for acceleration response spectra were then proposed using a robust nonlinear optimization coupled with a regression analysis. For analytical verification of the prediction equation, response spectra used for low-to-moderate seismic regions were estimated and the predicted results were comparatively evaluated with measured response spectra. As a result, the predicted shapes of response spectra can simulate the graphical shapes of measured data with high accuracy and most of predicted results are distributed inside range of correlation of variation (COV) of 30% from perfectly correlated lines.

• Earthquakes and Structures
• /
• 제25권6호
• /
• pp.469-479
• /
• 2023

A probabilistic seismic damage analysis is an essential procedure to identify seismically vulnerable structures, prioritize the seismic retrofit, and ultimately minimize the overall seismic risk. To assess the seismic risk of multiple structures within a region, a large number of nonlinear time-history structural analyses must be conducted and studied. As a result, each assessment requires high computing resources. To overcome this limitation, we explore a deep learning-based metamodel to enable the prediction of the mean and the standard deviation of the seismic damage distribution of track-on steel-plate girder railway bridges in Korea considering the geometric variation. For machine learning training, nonlinear dynamic time-history analyses are performed to generate 800 high-fidelity datasets on the seismic response. Through intensive trial and error, the study is concentrated on developing an optimal machine learning architecture with the pre-identified variables of the physical configuration of the bridge. Additionally, the prediction performance of the proposed method is compared with a previous, well-defined, response surface model. Finally, the statistical testing results indicate that the overall performance of the deep-learning model is improved compared to the response surface model, as its errors are reduced by as much as 61%. In conclusion, the model proposed in this study can be effectively deployed for the seismic fragility and risk assessment of a region with a large number of structures.

• 한국공간구조학회논문집
• /
• 제24권2호
• /
• pp.83-90
• /
• 2024

Machine learning is widely applied to various engineering fields. In structural engineering area, machine learning is generally used to predict structural responses of building structures. The aging deterioration of reinforced concrete structure affects its structural behavior. Therefore, the aging deterioration of R.C. structure should be consider to exactly predict seismic responses of the structure. In this study, the machine learning based seismic response prediction model was developed. To this end, four machine learning algorithms were employed and prediction performance of each algorithm was compared. A 3-story coupled shear wall structure was selected as an example structure for numerical simulation. Artificial ground motions were generated based on domestic site characteristics. Elastic modulus, damping ratio and density were changed to considering concrete degradation due to chloride penetration and carbonation, etc. Various intensity measures were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks and extreme gradient boosting algorithms present good prediction performance.

• Structural Engineering and Mechanics
• /
• 제69권6호
• /
• pp.677-691
• /
• 2019

In recent years, interest is growing in the engineering community on the experimental assessment and the theoretical prediction of the out-of-plane (OOP) seismic response of unreinforced masonry (URM) infills, which are widespread in Reinforced Concrete (RC) buildings in Europe and in the Mediterranean area. In the literature, some mechanical-based models for the prediction of the entire OOP force-displacement response have been formulated and proposed. However, the small number of experimental tests currently available has not allowed, up to current times, a robust and reliable evaluation of the predictive capacity of such response models. To enrich the currently available experimental database, six pure OOP tests on URM infills in RC frames were carried out at the Department of Structures for Engineering and Architecture of the University of Naples Federico II. Test specimens were built with the same materials and were different only for the thickness of the infill walls and for the number of their edges mortared to the confining elements of the RC frames. In this paper, the results of these experimental tests are briefly recalled. The main aim of this study is comparing the experimental response of test specimens with the prediction of mechanical models presented in the literature, in order to assess their effectiveness and contribute to the definition of a robust and reliable model for the evaluation of the OOP seismic response of URM infill walls.

• Structural Engineering and Mechanics
• /
• 제90권2호
• /
• pp.189-208
• /
• 2024

Artificial intelligence is one of the efficient methods that can be developed to simulate nonlinear behavior and predict the response of building structures. In this regard, an adaptive method based on optimization algorithms is used to train the TSK model of the fuzzy inference system to estimate the seismic behavior of building structures based on analytical data. The optimization algorithm is implemented to determine the parameters of the TSK model based on the minimization of prediction error for the training data set. The adaptive training is designed on the feedback of the results of previous time steps, in which three training cases of 2, 5, and 10 previous time steps were used. The training data is collected from the results of nonlinear time history analysis under 100 ground motion records with different seismic properties. Also, 10 records were used to test the inference system. The performance of the proposed inference system is evaluated on two 3 and 20-story models of nonlinear steel moment frame. The results show that the inference system of the TSK model by combining the optimization method is an efficient computational method for predicting the response of nonlinear structures. Meanwhile, the multi-vers optimization (MVO) algorithm is more accurate in determining the optimal parameters of the TSK model. Also, the accuracy of the results increases significantly with increasing the number of previous steps.

• 한국공간구조학회논문집
• /
• 제24권2호
• /
• pp.91-99
• /
• 2024

In this paper, machine learning models were applied to predict the seismic response of steel frame structures. Both geometric and material nonlinearities were considered in the structural analysis, and nonlinear inelastic dynamic analysis was performed. The ground acceleration response of the El Centro earthquake was applied to obtain the displacement of the top floor, which was used as the dataset for the machine learning methods. Learning was performed using two methods: Decision Tree and Random Forest, and their efficiency was demonstrated through application to 2-story and 6-story 3-D steel frame structure examples.

• Steel and Composite Structures
• /
• 제34권5호
• /
• pp.743-767
• /
• 2020

Due to the impressive flexural performance, enhanced compressive strength and more constrained crack propagation, Fibre-reinforced concrete (FRC) have been widely employed in the construction application. Majority of experimental studies have focused on the seismic behavior of FRC columns. Based on the valid experimental data obtained from the previous studies, the current study has evaluated the seismic response and compressive strength of FRC rectangular columns while following hybrid metaheuristic techniques. Due to the non-linearity of seismic data, Adaptive neuro-fuzzy inference system (ANFIS) has been incorporated with metaheuristic algorithms. 317 different datasets from FRC column tests has been applied as one database in order to determine the most influential factor on the ultimate strengths of FRC rectangular columns subjected to the simulated seismic loading. ANFIS has been used with the incorporation of Particle Swarm Optimization (PSO) and Genetic algorithm (GA). For the analysis of the attained results, Extreme learning machine (ELM) as an authentic prediction method has been concurrently used. The variable selection procedure is to choose the most dominant parameters affecting the ultimate strengths of FRC rectangular columns subjected to simulated seismic loading. Accordingly, the results have shown that ANFIS-PSO has successfully predicted the seismic lateral load with R² = 0.857 and 0.902 for the test and train phase, respectively, nominated as the lateral load prediction estimator. On the other hand, in case of compressive strength prediction, ELM is to predict the compressive strength with R² = 0.657 and 0.862 for test and train phase, respectively. The results have shown that the seismic lateral force trend is more predictable than the compressive strength of FRC rectangular columns, in which the best results belong to the lateral force prediction. Compressive strength prediction has illustrated a significant deviation above 40 Mpa which could be related to the considerable non-linearity and possible empirical shortcomings. Finally, employing ANFIS-GA and ANFIS-PSO techniques to evaluate the seismic response of FRC are a promising reliable approach to be replaced for high cost and time-consuming experimental tests.

• Earthquakes and Structures
• /
• 제6권2호
• /
• pp.141-161
• /
• 2014

This article presents a new generation of empirical ground motion models for the prediction of response spectral accelerations in soil conditions, specifically developed for the Vrancea intermediate-depth seismic source. The strong ground motion database from which the ground motion prediction model is derived consists of over 800 horizontal components of acceleration recorded from nine Vrancea intermediate-depth seismic events as well as from other seventeen intermediate-depth earthquakes produced in other seismically active regions in the world. Among the main features of the new ground motion model are the prediction of spectral ordinates values (besides the prediction of the peak ground acceleration), the extension of the magnitudes range applicability, the use of consistent metrics (epicentral distance) for this type of seismic source, the extension of the distance range applicability to 300 km, the partition of total standard deviation in intra- and inter-event standard deviations and the use of a national strong ground motion database more than two times larger than in the previous studies. The results suggest that this model is an improvement of the previous generation of ground motion prediction models and can be properly employed in the analysis of the seismic hazard of Romania.

• Structural Engineering and Mechanics
• /
• 제43권3호
• /
• pp.287-309
• /
• 2012

Precast segmental bridge columns (PSBC) are alternatives for monolithic cast-in-situ concrete columns in bridge substructures, with fast construction speed and structural durability. The analytical tool for common use is demonstrated applicable for seismic performance prediction of PSBCs through experiment conducted earlier. Then the analytical program was used for parameter optimization of PSBC configurations under reversal cyclic loading. Shear strength by pushover analysis was compared with theoretical prediction. Moreover, seismic response of PSBC with energy dissipation (ED) bars was compared with its no ED bar counterpart under three history ground acceleration records. The investigation shows that appropriate ED bar and post-tensioned tendon arrangement is important for higher lateral bearing capacity and good ductility performance of PSBCs.