Deep Learning for Accelerated Seismic Reliability Analysis of Transportation Networks

@article{Nabian2017DeepLF,
  title={Deep Learning for Accelerated Seismic Reliability Analysis of Transportation Networks},
  author={Mohammad Amin Nabian and Hadi Meidani},
  journal={Computer‐Aided Civil and Infrastructure Engineering},
  year={2017},
  volume={33}
}
  • M. A. NabianH. Meidani
  • Published 28 August 2017
  • Computer Science
  • Computer‐Aided Civil and Infrastructure Engineering
To optimize mitigation, preparedness, response, and recovery procedures for infrastructure systems, it is essential to use accurate and efficient means to evaluate system reliability against probabilistic events. The predominant approach to quantify the impact of natural disasters on infrastructure systems is the Monte Carlo approach, which still suffers from high computational cost, especially when applied to large systems. This article presents a deep learning framework for accelerating… 

Deep Learning–Based Retrofitting and Seismic Risk Assessment of Road Networks

A neural network surrogate model that allows rapid and accurate estimation of changes in traffic performance metrics due to bridge damage is developed, allowing decision-makers to evaluate the impact of retrofitting bridges in the system quickly.

Pre‐ and post‐earthquake regional loss assessment using deep learning

The paper introduces thorough numerical investigations of two hypothetical urban communities and proposes new frameworks for pre‐ and post‐earthquake regional loss assessments using deep learning methods to improve the accuracy of the response prediction of individual structures during the pre‐ earthquake loss assessment.

Image‐based post‐disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization

  • Xiao Liang
  • Computer Science
    Comput. Aided Civ. Infrastructure Eng.
  • 2019
A three‐level image‐based approach for post‐disaster inspection of the reinforced concrete bridge using deep learning with novel training strategies and a principled manner of such selection is proposed, with very promising results (well over 90% accuracies) and robustness are observed on all three‐ level deep learning models.

Post-Earthquake Assessment of Buildings Using Deep Learning

This study has proposed a CNN based autonomous damage detection model that has real-time application in the event of an earthquake.

Multi‐scale seismic reliability assessment of networks by centrality‐based selective recursive decomposition algorithm

A new algorithm utilizing network centrality, termed “centrality‐based selective recursive decomposition algorithm” (CS‐RDA), which groups components such that its modularity is maximized, by sequentially removing edges that have the highest level of betweenness centrality.

Identify seismically vulnerable unreinforced masonry buildings using deep learning

This work achieves the best overall accuracy reported to date, at 83.6%, in identifying unfinished U RM, finished URM, and non-URM buildings and performs extensive empirical analysis to establish synergistic parameters on the deep neural network, namely ResNeXt-101-FixRes.

Multiscale modeling of backward erosion piping in flood protection system infrastructure

This article presents a novel multiscale modeling approach to simulate the evolution of the backward erosion piping (BEP) process in flood protection systems (FPSs). A multiphase description of the

SCALODEEP: A Highly Generalized Deep Learning Framework for Real‐Time Earthquake Detection

An automatic earthquake detection framework based on a deep learning approach (SCALODEEP), which extracts high‐order features embedded in three‐component seismograms by encoding a time‐frequency representation of the data (scalogram) into a deep network with skip connections and achieves a superior generalization ability via a sophisticated network architecture.
...

References

SHOWING 1-10 OF 83 REFERENCES

Deep Learning for Accelerated Reliability Analysis of Infrastructure Networks

A deep learning framework for accelerating infrastructure system reliability analysis is presented and two distinct deep neural network surrogates are constructed and studied and highlight the effectiveness of the proposed approach in accelerating the transportation system two-terminal reliability analysis with extremely high prediction accuracy.

Transportation system modeling and applications in earthquake engineering

Abstract : Transportation networks constitute one class of major civil infrastructure systems that is a critical backbone of modern society. Physical damage and functional loss to transportation

Serviceability Assessment of a Municipal Water System Under Spatially Correlated Seismic Intensities

In this article, the serviceability of an infrastructure network system is evaluated with a model that considers spatial correlation in seismic intensity and demand and their usefulness for engineering decision making is evaluated.

Measuring the performance of transportation infrastructure systems in disasters: a comprehensive review

This paper provides a comprehensive overview of the literature on transportation infrastructure system performance in disasters. Specifically, it reviews those articles appearing in refereed

Seismic Retrofitting Manual for Highway Structures: Part 1 - Bridges

This manual, which is comprised of two parts, represents the most current state-of-practice in assessing the vulnerability of highway structures to the effects of earthquakes, and implementing

Matrix-based system reliability method and applications to bridge networks

...