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Ballesteros-Pérez, P, Sanz-Ablanedo, E, Soetanto, R, González-Cruz, M C, Larsen, G D and Cerezo-Narváez, A (2020) Duration and Cost Variability of Construction Activities: An Empirical Study. Journal of Construction Engineering and Management, 146(01).

Davila Delgado, J M, Oyedele, L, Bilal, M, Ajayi, A, Akanbi, L and Akinade, O (2020) Big Data Analytics System for Costing Power Transmission Projects. Journal of Construction Engineering and Management, 146(01).

Deng, H, Hong, H, Luo, D, Deng, Y and Su, C (2020) Automatic Indoor Construction Process Monitoring for Tiles Based on BIM and Computer Vision. Journal of Construction Engineering and Management, 146(01).

  • Type: Journal Article
  • Keywords: Computer vision; Building information modeling; Automatic progress monitoring; Project management;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001744
  • Abstract:
    For project managers, accurate, timely, and intuitive information is the key to successful decision-making during progress control at a construction site. This paper presents a method that combines computer vision with building information modeling (BIM) for automated progress monitoring of tiles. This method can automatically and accurately measure the in-built progress information of a construction site and transmit real-time progress information to the cloud in a visualized form. With the in-built and real-time progress information, project managers can know the progress of construction site in time and make decisions easily. The proposed method includes several modules. First, an image database is built with thousands of tile images, and a variety of local binary patterns (LBPs) feature extraction methods, and support vector machines (SVMs) are used to train a tile classifier with satisfactory performance, with an accuracy of 91.17%. The purpose of the first module is to construct a mathematical feature of an image and to train a classification algorithm according to this feature, so that tiles in images can be identified. The improved edge detection algorithm detects the boundaries of completed tiles in given images. Afterward, the boundary line coordinates are converted from image pixel coordinates to a real-world coordinate system through camera calibration. Next, using the information from the camera location and room profile information extracted from the BIM model, the actual tile area can be calculated automatically. Finally, the in-built progress is highlighted in the room floor plan, and the result is delivered to the BIM cloud simultaneously. The proposed method was tested at a real indoor construction site. The experimental results indicate that the method can effectively carry out real-time automatic quantity calculations.

El-adaway, I H, Ali, G G, Abotaleb, I S and Barber, H M (2020) Studying the Relationship between Stock Prices of Publicly Traded US Construction Companies and Gross Domestic Product: Preliminary Step toward Construction–Economy Nexus. Journal of Construction Engineering and Management, 146(01).

Elmousalami, H H (2020) Artificial Intelligence and Parametric Construction Cost Estimate Modeling: State-of-the-Art Review. Journal of Construction Engineering and Management, 146(01).

Gondia, A, Siam, A, El-Dakhakhni, W and Nassar, A H (2020) Machine Learning Algorithms for Construction Projects Delay Risk Prediction. Journal of Construction Engineering and Management, 146(01).

Halabya, A and El-Rayes, K (2020) Optimizing the Planning of Pedestrian Facilities Upgrade Projects to Maximize Accessibility for People with Disabilities. Journal of Construction Engineering and Management, 146(01).

He, C, McCabe, B, Jia, G and Sun, J (2020) Effects of Safety Climate and Safety Behavior on Safety Outcomes between Supervisors and Construction Workers. Journal of Construction Engineering and Management, 146(01).

Li, Y, Cao, L, Han, Y and Wei, J (2020) Development of a Conceptual Benchmarking Framework for Healthcare Facilities Management: Case Study of Shanghai Municipal Hospitals. Journal of Construction Engineering and Management, 146(01).

Maqsoom, A, Wazir, S J, Choudhry, R M, Thaheem, M J and Zahoor, H (2020) Influence of Perceived Fairness on Contractors’ Potential to Dispute: Moderating Effect of Engineering Ethics. Journal of Construction Engineering and Management, 146(01).

Newaz, M T, Davis, P, Jefferies, M and Pillay, M (2020) Examining the Psychological Contract as Mediator between the Safety Behavior of Supervisors and Workers on Construction Sites. Journal of Construction Engineering and Management, 146(01).

Pereira, E, Ali, M, Wu, L and Abourizk, S (2020) Distributed Simulation–Based Analytics Approach for Enhancing Safety Management Systems in Industrial Construction. Journal of Construction Engineering and Management, 146(01).

Signor, R, Love, P E D, Belarmino, A T N and Alfred Olatunji, O (2020) Detection of Collusive Tenders in Infrastructure Projects: Learning from Operation Car Wash. Journal of Construction Engineering and Management, 146(01).

Tawalare, A, Laishram, B and Thottathil, F (2020) Relational Partnership in Public Construction Organizations: Front-Line Employee Perspective. Journal of Construction Engineering and Management, 146(01).

Yuan, H and Yang, Y (2020) BIM Adoption under Government Subsidy: Technology Diffusion Perspective. Journal of Construction Engineering and Management, 146(01).