Neural Networks in Civil Engineering: 1989–2000

TLDR

The first neural‑network application in civil/structural engineering appeared in a 1989 journal article. This review surveys neural‑network studies published in civil‑engineering journals since that initial 1989 paper. It covers applications across structural, construction, environmental, water‑resources, traffic, highway, and geotechnical engineering, noting that most use backpropagation while also examining newer models and hybrid methods with genetic algorithms, fuzzy logic, and wavelets. Most civil‑engineering neural‑network applications rely on the simple backpropagation algorithm.

Abstract

The first journal article on neural network application in civil/structural engineering was published by in this journal in 1989. This article reviews neural network articles published in archival research journals since then. The emphasis of the review is on the two fields of structural engineering and construction engineering and management. Neural networks articles published in other civil engineering areas are also reviewed, including environmental and water resources engineering, traffic engineering, highway engineering, and geotechnical engineering. The great majority of civil engineering applications of neural networks are based on the simple backpropagation algorithm. Applications of other recent, more powerful and efficient neural networks models are also reviewed. Recent works on integration of neural networks with other computing paradigms such as genetic algorithm, fuzzy logic, and wavelet to enhance the performance of neural network models are presented.