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Classification of Construction Roughcasting Activities by Random Forest Algorithm

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Titel: Classification of Construction Roughcasting Activities by Random Forest Algorithm
Autoren: İbrahim Karataş, Abdulkadir Budak
Quelle: Düzce Üniversitesi Bilim ve Teknoloji Dergisi, Vol 13, Iss 4, Pp 1494-1504 (2025)
Verlagsinformationen: Düzce University, 2025.
Publikationsjahr: 2025
Bestand: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Science
LCC:Science (General)
Schlagwörter: i̇nşaat yönetimi, sıva aktivitesi, aktivite tanıma, rastgele orman algoritması, i̇nşaat işçisi, construction management, roughcasting activity, activity recognition, random forest algorithm, construction labor, Technology, Engineering (General). Civil engineering (General), TA1-2040, Science, Science (General), Q1-390
Beschreibung: Effective monitoring and management of construction-site workers is crucial for optimal site management. While traditionally challenging, modern technological advancements have enabled more efficient site control methods. This study employs a machine learning approach using the Random Forest (RF) algorithm to predict roughcasting work activities in a real construction environment. Data was collected using sensors (accelerometer, gyroscope, and magnetometer) attached to a roughcast master's arm. The methodology involved data preprocessing, including missing data control and standardization, followed by task-based labeling. The data was segmented into windows of 100 data points with 50% overlap, and statistical features were extracted. Using an 80-20% train-test split, the RF model achieved an overall prediction accuracy of 88.86% across approximately 234,000 data points representing various activities: waiting (90%), roughcasting (96%), material preparation (86%), and lining (72%). The study, conducted in a real construction environment, focused specifically on roughcasting activities. This approach, utilizing worker-attached sensors and artificial intelligence, demonstrates potential for broader application across construction activities and represents a step toward technological adaptation in construction site management.
Publikationsart: article
Dateibeschreibung: electronic resource
Sprache: English
ISSN: 2148-2446
Relation: https://dergipark.org.tr/tr/download/article-file/4558119; https://doaj.org/toc/2148-2446
DOI: 10.29130/dubited.1628311
Zugangs-URL: https://doaj.org/article/7a2d378777544cef9cc65c3da0300720
Dokumentencode: edsdoj.7a2d378777544cef9cc65c3da0300720
Datenbank: Directory of Open Access Journals
Beschreibung
Abstract:Effective monitoring and management of construction-site workers is crucial for optimal site management. While traditionally challenging, modern technological advancements have enabled more efficient site control methods. This study employs a machine learning approach using the Random Forest (RF) algorithm to predict roughcasting work activities in a real construction environment. Data was collected using sensors (accelerometer, gyroscope, and magnetometer) attached to a roughcast master's arm. The methodology involved data preprocessing, including missing data control and standardization, followed by task-based labeling. The data was segmented into windows of 100 data points with 50% overlap, and statistical features were extracted. Using an 80-20% train-test split, the RF model achieved an overall prediction accuracy of 88.86% across approximately 234,000 data points representing various activities: waiting (90%), roughcasting (96%), material preparation (86%), and lining (72%). The study, conducted in a real construction environment, focused specifically on roughcasting activities. This approach, utilizing worker-attached sensors and artificial intelligence, demonstrates potential for broader application across construction activities and represents a step toward technological adaptation in construction site management.
ISSN:21482446
DOI:10.29130/dubited.1628311