An ANN-Based Approach for Prediction of Sufficient Seismic Gap between Adjacent Buildings Prone to Earthquake-Induced Pounding

Seyed Mohammad Khatami,Robert Jankowski,Barbara Sołtysik,Rui Carneiro Barros,Hosein Naderpour,Seyed Mohammad Nazem Razavi

doi:10.3390/app10103591

Abstract

Earthquake-induced structural pounding may cause major damages to structures, and therefore it should be prevented. This study is focused on using an artificial neural network (ANN) method to determine the sufficient seismic gap in order to avoid collisions between two adjacent buildings during seismic excitations. Six lumped mass models of structures with a different number of stories (from one to six) have been considered in the study. The earthquake characteristics and the parameters of buildings have been defined as inputs in the ANN analysis. The required seismic gap preventing pounding has been firstly determined for specified structural arrangements and earthquake records. In order to validate the method for other structural parameters, the study has been further extended for buildings with different values of height, mass, and stiffness of each story. Finally, the parametric analysis has been conducted for various earthquakes scaled to different values of the peak ground acceleration (PGA). The results of the verification and validation analyses indicate that the determined seismic gaps are large enough to prevent structural collisions, and they are just appropriate for all different structural arrangements, seismic excitations, and structural parameters. The results of the parametric analysis show that the increase in the PGA of earthquake records leads to a substantial, nearly uniform, increase in the required seismic gap between structures. The above conclusions clearly indicate that the ANN method can be successfully used to determine the minimal distance between two adjacent buildings preventing their collisions during different seismic excitations.

Highlights

It is obviously seen that an insufficient seismic gap between adjacent structures may provide damages due to possible collisions between them during seismic excitations
The aim of the present paper is to show the results of the investigation focused on using an artificial neural network (ANN) method to conduct the evaluation of the optimum gap size between artificial neural network (ANN) method to conduct the evaluation of the optimum gap size between adjacent buildings, so as to prevent their collisions during seismic excitations
Using the ANN, with the schematic diagram of the model and the detailed flowchart of its algorithm shown in Figure 1, the required seismic gap preventing pounding between adjacent algorithm shown in Figure 1, the required seismic gap preventing pounding between adjacent structures has been determined for different structural arrangements and earthquake records

Summary

Introduction

It is obviously seen that an insufficient seismic gap between adjacent structures may provide damages due to possible collisions between them during seismic excitations. The phenomenon, which is often called “earthquake-induced structural pounding”, concerns mainly buildings and bridges [1,2,3,4,5,6] It occurs when the gap between structures, or structural members, cannot cover their relative movements, and the relative lateral displacement exceeds the seismic gap [7,8,9,10,11,12]. The estimation of a sufficient gap provides a safety zone between adjacent buildings, preventing structural damage often observed during previous earthquakes [13,14,15].

Objectives

Methods

Results

Conclusion