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AlChaer, E and Issa, C A (2020) Engineering Productivity Measurement: A Novel Approach. Journal of Construction Engineering and Management, 146(08).
Cai, S, Ma, Z, Skibniewski, M J, Bao, S and Wang, H (2020) Construction Automation and Robotics for High-Rise Buildings: Development Priorities and Key Challenges. Journal of Construction Engineering and Management, 146(08).
Chen, Y, Chen, S, Hu, C, Jin, L and Zheng, X (2020) Novel Probabilistic Cost Estimation Model Integrating Risk Allocation and Claim in Hydropower Project. Journal of Construction Engineering and Management, 146(08).
Han, Y, Yin, Z, Zhang, J, Jin, R and Yang, T (2020) Eye-Tracking Experimental Study Investigating the Influence Factors of Construction Safety Hazard Recognition. Journal of Construction Engineering and Management, 146(08).
Ibrahim, M W, Hanna, A S, Russell, J S, Abotaleb, I S and El-adaway, I H (2020) Quantitative Analysis of the Impacts of Out-of-Sequence Work on Project Performance. Journal of Construction Engineering and Management, 146(08).
Jiang, Y and Bai, Y (2020) Estimation of Construction Site Elevations Using Drone-Based Orthoimagery and Deep Learning. Journal of Construction Engineering and Management, 146(08).
Kim, J J, Miller, J A and Kim, S (2020) Cost Impacts of Change Orders due to Unforeseen Existing Conditions in Building Renovation Projects. Journal of Construction Engineering and Management, 146(08).
Li, H, Lv, L, Zuo, J, Su, L, Wang, L and Yuan, C (2020) Dynamic Reputation Incentive Mechanism for Urban Water Environment Treatment PPP Projects. Journal of Construction Engineering and Management, 146(08).
Li, J, Wang, H, Xie, Y and Zeng, W (2020) Human Error Identification and Analysis for Shield Machine Operation Using an Adapted TRACEr Method. Journal of Construction Engineering and Management, 146(08).
Ryu, J, Alwasel, A, Haas, C T and Abdel-Rahman, E (2020) Analysis of Relationships between Body Load and Training, Work Methods, and Work Rate: Overcoming the Novice Mason’s Risk Hump. Journal of Construction Engineering and Management, 146(08).
Sonmez, R, Aminbakhsh, S and Atan, T (2020) Activity Uncrashing Heuristic with Noncritical Activity Rescheduling Method for the Discrete Time-Cost Trade-Off Problem. Journal of Construction Engineering and Management, 146(08).
Tan, T, Lu, W, Tan, G, Xue, F, Chen, K, Xu, J, Wang, J and Gao, S (2020) Construction-Oriented Design for Manufacture and Assembly Guidelines. Journal of Construction Engineering and Management, 146(08).
Yin, X, Chen, Y, Bouferguene, A, Zaman, H, Al-Hussein, M and Russell, R (2020) Data-Driven Framework for Modeling Productivity of Closed-Circuit Television Recording Process for Sewer Pipes. Journal of Construction Engineering and Management, 146(08).
- Type: Journal Article
- Keywords: Closed-circuit television (CCTV); Sewer pipes; Machine learning; Productivity; Linear regression;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001885
- Abstract:
Closed-circuit television (CCTV) is widely used in North America for sewer pipe inspection due to several benefits, such as easy operation and lower upfront costs. To be useful, video footage needs to be collected according to specific standards, which makes the video recording process a time-consuming operation, especially when pipes have operational issues like debris or tree roots. As a result, because city managers are usually limited by the available budget, a good understanding of the overall requirements for CCTV sewer pipe inspection is necessary for efficient resource planning. In this respect, a framework is proposed to model the productivity of the CCTV video recording process by predicting the duration of the recording process based on selected variables. In order to predict the CCTV recording duration, a type of machine learning algorithm and a linear regression model are developed. To be more specific, the random sample consensus (RANSAC) algorithm has been used to extract the benchmark for the CCTV recording process. This algorithm is adopted to screen the data automatically, arriving at a function of the CCTV recording time with two variables (i.e., the total length of the pipe segment and the number of taps in the pipe). As a result, the original dataset that records the CCTV collection process is segmented into three parts: benchmark dataset and two types of outlier datasets. Subsequently, two linear regression models are developed on the outliers to predict the recording duration. Finally, all the developed models are integrated into a simulation model to mimic the recording duration components. The framework is validated by historical data. For the convenience of implementation of the model, the parameters within the model are adjustable to adapt to different situations (such as different seasons, regions, and countries). The contribution of the research lies in two-folds: (1) the CCTV recording process is thoroughly investigated and well-understood, which provides a decision-making basis for the future CCTV collection process; and (2) the proposed simulation model development procedure can be applied to other studies that require data segmentation operation to improve the performance of the simulation model.
Zhan, W and Pan, W (2020) Formulating Systemic Construction Productivity Enhancement Strategies. Journal of Construction Engineering and Management, 146(08).
Zhang, R P, Lingard, H and Oswald, D (2020) Impact of Supervisory Safety Communication on Safety Climate and Behavior in Construction Workgroups. Journal of Construction Engineering and Management, 146(08).
Zheng, J, Wen, Q and Qiang, M (2020) Understanding Demand for Project Manager Competences in the Construction Industry: Data Mining Approach. Journal of Construction Engineering and Management, 146(08).
Zhu, L, Cheung, S O, Gao, X, Li, Q and Liu, G (2020) Success DNA of a Record-Breaking Megaproject. Journal of Construction Engineering and Management, 146(08).