Search results

The objective of the study is to examine the response of reinforced concrete (RC) structures subjected to Near-Fault Ground Motions (NFGM) and highlight the importance of considering various factors including the influence of the relative geographical position of near-fault sites that can affect the structural response during an earthquake.

In this paper, the response of a four-storey RC building subjected to NFGMs with varied characteristics like hanging wall and footwall in conjunction with directivity and the effect of pulse-like ground motions with rupture direction are investigated to understand the combined influence of these factors on the behavior of the structure. Furthermore, the capacity and demand of the structural element are investigated for computing the performance ratio.

Results from this study indicate that the most unfavorable combinations for structural damage due to near-fault ground motion are the hanging wall with forward rupture, the fault normal component of ground motions, and pulse-like ground motions with forward directivity.

The results from this study provide valuable insight into the response of RC structures subjected to NFGM and highlight the importance of considering various factors that can affect the structural response during an earthquake. Moreover, the computation of capacity and demand of the critical beam indicates exceedance of desired limits, resulting in the early deterioration of the structural elements. Finally, the analytical analysis from the present study confirms that the hanging wall with forward ruptures, pulse-like motions, and fling steps are the most unfavorable combinations for seismic structural damage.

Near-fault pulse-like ground motions have distinct and very severe effects on reinforced concrete (RC) structures. However, there is a paucity of recorded data from Near-Fault Ground Motions (NFGMs), and thus forecasting the dynamic seismic response of structures, using conventional techniques, under such intense ground motions has remained a challenge.

The present study utilizes a 2D finite element model of an RC structure subjected to near-fault pulse-like ground motions with a focus on the storey drift ratio (SDR) as the key demand parameter. Five machine learning classifiers (MLCs), namely decision tree, k-nearest neighbor, random forest, support vector machine and Naïve Bayes classifier , were evaluated to classify the damage states of the RC structure.

The results such as confusion matrix, accuracy and mean square error indicate that the Naïve Bayes classifier model outperforms other MLCs with 80.0% accuracy. Furthermore, three MLC models with accuracy greater than 75% were trained using a voting classifier to enhance the performance score of the models. Finally, a sensitivity analysis was performed to evaluate the model's resilience and dependability.

The objective of the current study is to predict the nonlinear storey drift demand for low-rise RC structures using machine learning techniques, instead of labor-intensive nonlinear dynamic analysis.