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Chen, L, Manley, K, Lewis, J, Helfer, F and Widen, K (2018) Procurement and Governance Choices for Collaborative Infrastructure Projects. Journal of Construction Engineering and Management, 144(08).

Cho, C, Kim, K, Park, J and Cho, Y K (2018) Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures. Journal of Construction Engineering and Management, 144(08).

  • Type: Journal Article
  • Keywords: Temporary structures; Smart monitoring; Machine learning; Finite element model (FEM); Scaffold;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001535
  • Abstract:
    As temporary structures, scaffolds have essential roles to hold workers, materials, and equipment throughout construction activities. However, because a safety inspection for scaffolds is primarily visual and labor intensive, the OSHA standards related to scaffolds are frequently violated. Improper management of scaffolds has caused scaffolding collapses that have a potentially detrimental effect and liability on workers’ lives. This paper discusses the significance of scaffolding collapses and explores a method to perform scaffolding monitoring. To establish an integrated method, this research cross-connects various components (e.g., strain data, finite element model (FEM)-based structural analysis, machine learning, and an actual scaffold) in the presented framework. More specifically, this framework for a smart monitoring system is involved with: (1) developing a wireless strain sensing module for data collection, (2) modeling an FEM and learning data for failure mechanisms through FEM to characterize scaffold behaviors under certain loading conditions, and (3) investigating a machine-learning algorithm (i.e., support vector machine) for decision making. The FEM simulation analyzes a scaffolding to calculate strain values for each scaffolding column from randomly generated 1,200 load cases. Load-related strain data form training and testing sets for the machine-learning algorithm that enables the distinguishing of scaffolding conditions such as safe, over-turning, uneven-settlement, and over-loading conditions. In the experimental validation, the developed wireless strain sensing modules perform the real-time strain measurement and the machine-learning algorithm to successfully estimate the status of the scaffolding structure with 97.66% accuracy on average. The proposed method could escalate a monitoring paradigm for temporary structures from a labor-intensive manual inspection to a systematic real-time approach.

Faghih, S A M and Kashani, H (2018) Forecasting Construction Material Prices Using Vector Error Correction Model. Journal of Construction Engineering and Management, 144(08).

Hosseini, M R, Maghrebi, M, Akbarnezhad, A, Martek, I and Arashpour, M (2018) Analysis of Citation Networks in Building Information Modeling Research. Journal of Construction Engineering and Management, 144(08).

Ji, W and AbouRizk, S M (2018) Data-Driven Simulation Model for Quality-Induced Rework Cost Estimation and Control Using Absorbing Markov Chains. Journal of Construction Engineering and Management, 144(08).

Ji, W, AbouRizk, S M, Zaïane, O R and Li, Y (2018) Complexity Analysis Approach for Prefabricated Construction Products Using Uncertain Data Clustering. Journal of Construction Engineering and Management, 144(08).

Kim, K, Cho, Y and Kim, K (2018) BIM-Driven Automated Decision Support System for Safety Planning of Temporary Structures. Journal of Construction Engineering and Management, 144(08).

Lee, J (2018) Value Engineering for Defect Prevention on Building Façade. Journal of Construction Engineering and Management, 144(08).

Maemura, Y, Kim, E and Ozawa, K (2018) Root Causes of Recurring Contractual Conflicts in International Construction Projects: Five Case Studies from Vietnam. Journal of Construction Engineering and Management, 144(08).

Pomares, J C, González, A and Saura, P (2018) Simple and Resistant Construction Built with Concrete Voussoirs for Developing Countries. Journal of Construction Engineering and Management, 144(08).

Qian, Q and Zhang, L (2018) Impact of Regulatory Focus on Choice of Project-Governance Modes: Role of Tolerance of Opportunistic Behavior. Journal of Construction Engineering and Management, 144(08).

Siebelink, S, Voordijk, J T and Adriaanse, A (2018) Developing and Testing a Tool to Evaluate BIM Maturity: Sectoral Analysis in the Dutch Construction Industry. Journal of Construction Engineering and Management, 144(08).

Su, C T, Santoro, M C and Mendes, A B (2018) Constructive Heuristics for Project Scheduling Resource Availability Cost Problem with Tardiness. Journal of Construction Engineering and Management, 144(08).

Tabish, S Z S and Jha, K N (2018) Beyond the Iron Triangle in Public Construction Projects. Journal of Construction Engineering and Management, 144(08).

Techera, U, Hallowell, M, Littlejohn, R and Rajendran, S (2018) Measuring and Predicting Fatigue in Construction: Empirical Field Study. Journal of Construction Engineering and Management, 144(08).

Wang, X, Huang, X, Luo, Y, Pei, J and Xu, M (2018) Improving Workplace Hazard Identification Performance Using Data Mining. Journal of Construction Engineering and Management, 144(08).

Wu, W, Mayo, G, McCuen, T L, Issa, R R A and Smith, D K (2018) Building Information Modeling Body of Knowledge. I: Background, Framework, and Initial Development. Journal of Construction Engineering and Management, 144(08).

Wu, W, Mayo, G, McCuen, T L, Issa, R R A and Smith, D K (2018) Building Information Modeling Body of Knowledge. II: Consensus Building and Use Cases. Journal of Construction Engineering and Management, 144(08).

Zuluaga, C M, Albert, A and Arroyo, P (2018) Protecting Bridge Maintenance Workers from Falls: Evaluation and Selection of Compatible Fall Protection Supplementary Devices. Journal of Construction Engineering and Management, 144(08).