Buildings and infrastructural amenities are foundational to urban growth and the overall welfare of societies. These structures, crucial as they are, often face challenges to their integrity due to manufacturing imperfections, environmental adversities, and various external pressures. Conventional approaches for pinpointing defects within these structures have exhibited certain constraints, particularly in terms of accuracy and operational efficiency. In response, this paper furnishes an all-encompassing exploration of the wavelet transform’s role in detecting and thoroughly analyzing structural anomalies in buildings. This method, with its intricate signal processing capabilities, stands out in recognizing and analyzing discrepancies across a spectrum of frequency bands and scales, offering a nuanced tool for structural insight. The paper probe the integration of the wavelet transform within the domain of building structures, with a particular emphasis on structural health monitoring (SHM). The objective of this study is to shed light on the transformative potential, inherent strengths, and possible limitations of the wavelet transform within the domain of SHM. Navigating through methodologies such as Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT), Multi-resolution Analysis (MRA), and several burgeoning techniques, this review meticulously unravels their foundational underpinnings, avant-garde applications, and their profound relevance in contemporary civil engineering practices.
Original languageEnglish
Title of host publicationLecture Notes in Civil Engineering
Subtitle of host publicationbook
PublisherSpringer
ChapterChapter 43
Pages420-428
Number of pages9
Volume474
ISBN (Print)978-981971513-8
DOIs
Publication statusPublished - 14 Mar 2024

Publication series

NameProceedings of the 2nd International Conference on Advanced Civil Engineering and Smart Structures
Volume474
ISSN (Print)2366-2557
ISSN (Electronic)2366-2565

    ASJC Scopus subject areas

  • Civil and Structural Engineering

ID: 55355966