Abstract

Due to the increased service life, environmental corrosion, unreasonable construction, and other issues, local defects inevitably exist in civil structures, which affect the structural performance and can lead to structural failure. However, research on grout defect identification of precast reinforced concrete frame structures with rebars spliced by sleeves faces great challenges owing to the complexity of the problem. This study presents a multiple-variable spatiotemporal regression model algorithm to identify local defects based on structural vibration responses collected using a sensor network. First, numerical simulations were carried out on precast beam–column connection models by comparing the identification results based on a single-variable regression model, two-variable spatial regression model, and two-variable spatiotemporal regression model; furthermore, a multiple-variable spatiotemporal regression model was proposed and robustness analysis of the damage indicator was carried out. Then, to explore the validity of the proposed method, a nondestructive vibration experiment was considered on a half-scaled, two-floor, precast concrete frame structure with column rebars spliced by defective grout sleeves. The results show that local defects were successfully identified based on a multiple-variable spatiotemporal regression model.

Highlights

  • Due to overloading, environmental corrosion, material aging, operating loads, fatigue, and other unexpected events, engineering structures inevitably sustain different kinds of defects that can lead to structural failure

  • All acceleration response data from cases 1–9 was analyzed based on the three linear regression models

  • Based on Multiple-Variable Regression Model defect was in the column and near node 4, which was consistent with the defect design

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Summary

Introduction

Environmental corrosion, material aging, operating loads, fatigue, and other unexpected events, engineering structures inevitably sustain different kinds of defects that can lead to structural failure. Structural physical parameters will be altered due to structural damage, which will result in a difference in the corresponding dynamic characteristics (e.g., natural frequency, mode curvature, and strain mode), through which structural damage can be identified [6,7,8,9]. Several studies have focused on damage identification based on structural global responses. Based on natural frequency responses, Sha et al [10,11,12] carried out damage detection in beams, aluminum samples, and a soil box according to numerical simulations and experiments. Cui et al [16] defined a damage-detection method according to strain modes under ambient excitation, and experiments on Sensors 2020, 20, 3264; doi:10.3390/s20113264 www.mdpi.com/journal/sensors

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