İnşaatta Sıva Aktivitelerinin Rastgele Orman Algoritması ile Sınıflandırılması

Year 2025, Volume: 13 Issue: 4, 1494 - 1504, 30.10.2025

İbrahim Karataş , Abdulkadir Budak

https://doi.org/10.29130/dubited.1628311

Abstract

İnşaat sektöründe çalışanları kontrol etmek ve yönetmek, şantiye yönetiminin etkinliği için çok önemlidir. Geleneksel olarak şantiyelerde bu durum oldukça zordur. Ancak teknolojinin gelişmesiyle birlikte şantiyeler daha etkin bir şekilde kontrol edilebilmektedir. Bu çalışma, rastgele orman (RF) algoritması ile gerçek inşaat ortamındaki sıva işinin faaliyetlerini tahmin etmeyi amaçlamaktadır. Kara sıva inşaat ustasından koluna takılan bir sensör yardımıyla ivmeölçer, jiroskop ve manyetometre verileri toplanmıştır. Model için hazır hale getirilen veriler %80-20 oranında eğitim ve test verisi olarak ikiye ayrılmıştır. Eğitim verileri RF algoritması ile analiz edilmiştir. Modelin tahmini sonucunda elde edilen tahmin değerleri test verileri ile karşılaştırılarak modelin tahmin doğruluğu belirlenmiştir. Analizi sonucunda %88,86 genel tahmin doğruluğu elde edilmiştir.

Keywords

İnşaat yönetimi , Sıva aktivitesi , Aktivite tanıma , Rastgele orman algoritması , İnşaat işçisi

Classification of Construction Roughcasting Activities by Random Forest Algorithm

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.

Keywords

Construction Management , Roughcasting Activity , Activity Recognition , Random Forest Algorithm , Construction Labor

Ethical Statement

This study does not involve human or animal participants. All procedures followed scientific and ethical principles, and all referenced studies are appropriately cited.

Thanks

I would like to thank the workers who agreed to have sensors installed on their arms for this study.

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