Incorporating global search capability of a genetic algorithm into neural computing to model seismic records and soil test data

Abstract

In this study, a genetic algorithm with global searching capability was incorporated into a neural network calculating process to obtain a highly reliable model for predicting peak ground acceleration, which is the key element in evaluating earthquake response and in establishing a seismic design standard. In addition to three seismic parameters (i.e. local magnitude, focal distance, and epicentre depth), this study included two geological conditions (i.e. standard penetration test value and shearwave velocity) in the input to reflect the site response adequately. Based on the earthquake records and soil test data from 86 checking stations, within 24 seismic subdivision zones in the Taiwan area, the computational results show that using a combination of a neural network and genetic algorithm can achieve a higher performance compared with solely using a neural network model. Furthermore, a weight-based model was developed for predicting peak ground acceleration at an unmonitored site to represent each subdivision zone. The results show that three subdivision zones have higher horizontal peak ground accelerations than the seismic design value as required in the building code. The obtained information might be helpful in relevant engineering applications for the studied region, and the proposed method for treating this type of nonlinear seismic data might be applicable in other areas of interest worldwide.

A Modified Artificial Bee Colony Algorithm for Scheduling Optimization of Multi-aisle AS/RS System

“…The boundaries of all dimensions are [0, 1], and the maximum and velocity is 0.1 and minimum velocity is −0.1, the solutions are converted to desecrate solutions of AS/RS problem by rank of order (ROV) after each updating. In GA algorithm, crossover probability = 0.95, mutation probability = 0.1 [ 13 ]. In the crossover stage, some tasks may appear twice, and some others may be missing in the offspring solutions.…”

Earthquake Prediction using Hybrid Machine Learning Techniques

“…Rafiei et al was interested in giving early alarm weeks before maximum earthquake event using classification algorithm (CA) combining with mathematical optimization algorithm (OA) whose role is to find the location of the Earthquake with maximum magnitude [29]. Kerh et al used a genetic algorithm combined with a neural network to evaluate the response of the Earthquake in Taiwan that produces high results comparing to neural network model only [30]. Mirrashid et al used the system of adaptive neuro-fuzzy inference to predict the coming earthquakes with magnitude 5.5 or higher developed by three algorithms subtractive clustering (SC), grid partition (GP), and fuzzy Cmeans (FCM).…”

Fracture acoustic emission signals identification of stay cables in bridge engineering application using deep transfer learning and wavelet analysis

“…The number of the samples in this portion is called batch. Updating parameter in one batch is named an iteration and updating the parameters of all training samples is called an epoch (Kerh et al, 2017). The parameter update strategy is described as below:…”

“…The implementation of artificial neural networks (ANNs) with ML or DL method enables computers to perform time-consuming and labor-intensive identification by learning from experience. A number of studies have introduced DL techniques could effectively improve the classification accuracy in civil engineering application (Gao & Mosalam, 2018;Wang et al, 2018;Kerh et al, 2017). As one of the DL techniques, convolutional neural network (CNN) was developed to solve handwritten-digits recognition tasks around 1990s (LeCun et al, 1989).…”

“…Its recent wide applications are attributed to the great development of computer hardware and the boosting from ImageNet Visual Recognition Challenge (Simonyan & Zisserman, 2014). Compared with BP neural network-based shallow learning, no manual feature extraction are required in CNN-based deep learning (Kerh et al, 2017;Rafiei et al, 2017). CNN models are becoming increasingly deeper to improve performance, achieve high accuracy, robustness, and adaptability in image classification (Krizhevsky et al, 2012;Mayrbaurl & Camo, 2004).…”