Work

A Stacking Ensemble-Based Multi-Channel CNN Strategy for High-Accuracy Damage Assessment in Mega-Sub Controlled Structures

Year: 2025

Type: article

Abstract: The Mega-Sub Controlled Structure System (MSCSS) represents an innovative category of seismic-resistant super high-rise building structural systems, and exploring its damage mechanisms and identification methods is crucial. Nonetheless, the prevailing methodologies for establishing criteria for structural damage are deficient in providing a lucid and comprehensible representation of the actual damage sustained by edifices during seismic events. To address these challenges, the present study develops a finite element model of the MSCSS, conducts nonlinear time-history analyses to assess the MSCSS’s response to prolonged seismic motion records, and evaluates its damage progression. Moreover, considering the genuine damage conditions experienced by the MSCSS, damage working scenarios under seismic forces were formulated to delineate the damage patterns. A convolutional neural network recognition framework based on stacking ensemble learning is proposed for extracting damage features from the temporal response of structural systems and achieving damage classification. This framework accounts for the temporal and spatial interrelations among sensors distributed at disparate locations within the structure and addresses the issue of data imbalance arising from a limited quantity of damaged samples. The research results indicate that the proposed method achieves an accuracy of over 98% in dealing with damage in imbalanced datasets, while also demonstrating remarkable robustness.

Source: Buildings

DOI: 10.3390/buildings15111775

Authors: Zheng Wei, Xinwei Wang, Buqiao Fan, Muhammad Moman Shahzad

Institutions: Northwestern Polytechnical University, North China University of Water Resources and Electric Power, National University of Sciences and Technology

Language: English

Topic: Structural Health Monitoring Techniques

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137