{"title":"Remote measurement of reinforcing bar spacing and length from an oblique photograph using a novel perspective correction technique","authors":"Jun Su Park, Jae Young Kang, Hyo Seon Park","doi":"10.1111/mice.13533","DOIUrl":null,"url":null,"abstract":"The dimensional inspection of reinforcing bars at construction sites prior to concrete pouring is essential to ensure structural integrity. However, this process has traditionally relied on manual tape measurements, which are labor‐intensive, unsafe, and prone to human error. To address these limitations, this study introduces a novel method for remotely inspecting the spacings and lengths of reinforcing bars using a single oblique photograph and a new perspective correction technique. This method transforms oblique images into vertical images using constraints based on four specific vectors that must be perpendicular to one another, thereby eliminating the need for the four‐point correspondence required by existing methods. This improvement enhances the practicality of the proposed method. A validation experiment conducted at an apartment construction site yielded a mean absolute error of 5.12 mm in measuring the spacing and length of reinforcing bars, demonstrating field‐level accuracy in compliance with American Concrete Institute 117 and the Gagemaker's Rule from US military standard 120.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"30 1","pages":""},"PeriodicalIF":8.5000,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer-Aided Civil and Infrastructure Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1111/mice.13533","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
The dimensional inspection of reinforcing bars at construction sites prior to concrete pouring is essential to ensure structural integrity. However, this process has traditionally relied on manual tape measurements, which are labor‐intensive, unsafe, and prone to human error. To address these limitations, this study introduces a novel method for remotely inspecting the spacings and lengths of reinforcing bars using a single oblique photograph and a new perspective correction technique. This method transforms oblique images into vertical images using constraints based on four specific vectors that must be perpendicular to one another, thereby eliminating the need for the four‐point correspondence required by existing methods. This improvement enhances the practicality of the proposed method. A validation experiment conducted at an apartment construction site yielded a mean absolute error of 5.12 mm in measuring the spacing and length of reinforcing bars, demonstrating field‐level accuracy in compliance with American Concrete Institute 117 and the Gagemaker's Rule from US military standard 120.
期刊介绍:
Computer-Aided Civil and Infrastructure Engineering stands as a scholarly, peer-reviewed archival journal, serving as a vital link between advancements in computer technology and civil and infrastructure engineering. The journal serves as a distinctive platform for the publication of original articles, spotlighting novel computational techniques and inventive applications of computers. Specifically, it concentrates on recent progress in computer and information technologies, fostering the development and application of emerging computing paradigms.
Encompassing a broad scope, the journal addresses bridge, construction, environmental, highway, geotechnical, structural, transportation, and water resources engineering. It extends its reach to the management of infrastructure systems, covering domains such as highways, bridges, pavements, airports, and utilities. The journal delves into areas like artificial intelligence, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, internet-based technologies, knowledge discovery and engineering, machine learning, mobile computing, multimedia technologies, networking, neural network computing, optimization and search, parallel processing, robotics, smart structures, software engineering, virtual reality, and visualization techniques.