LSTM, WaveNet, and 2D CNN for nonlinear time history prediction of seismic responses
Predicting the nonlinear time-history responses of civil engineering structures under seismic
loading remains an essential task in earthquake engineering. This paper explores the …
loading remains an essential task in earthquake engineering. This paper explores the …
Fragility analyses of bridge structures using the logarithmic piecewise function-based probabilistic seismic demand model
Y Zhao, H Hu, L Bai, M Tang, H Chen, D Su - Sustainability, 2021 - mdpi.com
Seismic fragility analysis is an efficient method to evaluate the structural failure probability
during earthquake events. Among the existing fragility analysis methods, the probabilistic …
during earthquake events. Among the existing fragility analysis methods, the probabilistic …
High strength concrete compressive strength prediction using an evolutionary computational intelligence algorithm
The most crucial mechanical property of concrete is compression strength (CS). Insufficient
compressive strength can therefore result in severe failure, which can be beyond repair …
compressive strength can therefore result in severe failure, which can be beyond repair …
Structural probabilistic seismic risk analysis and damage prediction based on artificial neural network
DW Jia, ZY Wu - Structures, 2022 - Elsevier
A novel probabilistic seismic risk analysis (PSRA) methodology based on artificial neural
network (ANN) is introduced without lognormal assumption on the probabilistic seismic …
network (ANN) is introduced without lognormal assumption on the probabilistic seismic …
A deep reinforcement learning model for predictive maintenance planning of road assets: Integrating LCA and LCCA
Road maintenance planning is an integral part of road asset management. One of the main
challenges in Maintenance and Rehabilitation (M&R) practices is to determine maintenance …
challenges in Maintenance and Rehabilitation (M&R) practices is to determine maintenance …
Development of fragility functions of low-rise steel moment frame by artificial neural networks and identifying effective parameters using SHAP theory
Estimating analytical fragility functions requires high computational costs due to numerous
incremental non-linear dynamic analyses. This study employs a soft computing approach to …
incremental non-linear dynamic analyses. This study employs a soft computing approach to …
The development of fragility curves using calibrated probabilistic classifiers
E Saleh - Structures, 2024 - Elsevier
Performance-based earthquake engineering framework relies on the fragility curves for
seismic risk assessment. The objective of this study is to utilize calibrated probabilistic …
seismic risk assessment. The objective of this study is to utilize calibrated probabilistic …
Earthquake resistant design of framed reinforced concrete building using artificial intelligence model
Abstract Design of efficient earthquake (EQ) resistant building is challenging task to
earthquake engineers. The available methods for earthquake resistant design are complex …
earthquake engineers. The available methods for earthquake resistant design are complex …
Noise suppression using a near-source wavelet
Denoising becomes a nontrivial task when the noise and signal overlap in multiple domains,
such as time, frequency, and velocity. Fortunately, signal and noise waveforms, in general …
such as time, frequency, and velocity. Fortunately, signal and noise waveforms, in general …