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Al-Humaidi, H M (2016) Construction Projects Bid or Not Bid Approach Using the Fuzzy Technique for Order Preference by Similarity FTOPSIS Method. Journal of Construction Engineering and Management, 142(12).

Bhaumik, P K (2016) Developing and Using a New Family of Project S-Curves Using Early and Late Shape Parameters. Journal of Construction Engineering and Management, 142(12).

Cheng, E W L (2016) Intentions to Form Project Partnering in Hong Kong: Application of the Theory of Planned Behavior. Journal of Construction Engineering and Management, 142(12).

Choi, Y S, Lim, I, Kim, T, Cho, H and Kang, K (2016) Case Study of the Core Structure Succeeding Method for Tall Building Construction. Journal of Construction Engineering and Management, 142(12).

Chong, D, Wang, Y, Chen, L and Yu, B (2016) Modeling and Validation of Energy Consumption in Asphalt Mixture Production. Journal of Construction Engineering and Management, 142(12).

Doan, D T and Chinda, T (2016) Modeling Construction and Demolition Waste Recycling Program in Bangkok: Benefit and Cost Analysis. Journal of Construction Engineering and Management, 142(12).

Elbarkouky, M M G, Fayek, A R, Siraj, N B and Sadeghi, N (2016) Fuzzy Arithmetic Risk Analysis Approach to Determine Construction Project Contingency. Journal of Construction Engineering and Management, 142(12).

Firouzi, A, Yang, W and Li, C (2016) Prediction of Total Cost of Construction Project with Dependent Cost Items. Journal of Construction Engineering and Management, 142(12).

Hassan, M E, Kandil, A, Senouci, A and Al-Derham, H (2016) Organizational Behavior Attributes and Sustainable Construction Adoption: An Econometric Analysis Using Data from Qatar. Journal of Construction Engineering and Management, 142(12).

Hyari, K H, Tarawneh, Z S and Katkhuda, H N (2016) Detection Model for Unbalanced Pricing in Construction Projects: A Risk-Based Approach. Journal of Construction Engineering and Management, 142(12).

Li, S, Cai, H and Kamat, V R (2016) Integrating Natural Language Processing and Spatial Reasoning for Utility Compliance Checking. Journal of Construction Engineering and Management, 142(12).

  • Type: Journal Article
  • Keywords: Underground utility; Compliance checking; Natural language processing (NLP); Spatial reasoning; Geographical information system (GIS); Information technologies;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001199
  • Abstract:
    Underground utility incidents, such as utility conflicts and utility strikes, result in time and cost overruns in construction projects, property damages, environmental pollution, personnel injuries, and fatalities. A main cause of recurrent utility incidents is the noncompliance with the spatial configurations between utilities and their surroundings. Utility specifications usually contain textual descriptions of the spatial configurations. However, detection of spatial defects, according to the textual descriptions, is difficult and time consuming. This deficiency is because of the lack of spatial cognition in many rule-checking systems to process massive amounts of data. This study aims to automate utility compliance checking by integrating natural language processing (NLP) and spatial reasoning. NLP algorithm translates the textual descriptions of spatial configurations into computer-processable spatial rules. Spatial reasoning executes the extracted spatial rules following a logical order in a geographical information system (GIS) to identify noncompliance. The intellectual contribution of this study is twofold. First, complex spatial rules are retrieved automatically from textual data with their hierarchies classified, which provides the inputs and indicates the sequence of rule execution in spatial reasoning. Second, semantic spatial relations are modeled on the basis of their metric and topological implications, enabling the automatic execution of multiple spatial rules. Experiments were conducted to test this framework. The average precision, recall, and combination of the two (F-measure) achieved by the NLP algorithm for extracting spatial rules are 87.88%, 79.09%, and 83.25%, respectively. In addition, the spatial reasoning mechanism also was found to be a powerful tool for compliance checking under various scenarios.

M. Said, H M and Lucko, G (2016) Float Types in Construction Spatial Scheduling. Journal of Construction Engineering and Management, 142(12).

Namian, M, Albert, A, Zuluaga, C M and Behm, M (2016) Role of Safety Training: Impact on Hazard Recognition and Safety Risk Perception. Journal of Construction Engineering and Management, 142(12).

Patel, D A, Kikani, K D and Jha, K N (2016) Hazard Assessment Using Consistent Fuzzy Preference Relations Approach. Journal of Construction Engineering and Management, 142(12).

Pishdad-Bozorgi, P, de la Garza, J M and Austin, R B (2016) Readiness Assessment for Flash Tracking. Journal of Construction Engineering and Management, 142(12).

Sadeghi, N, Fayek, A R and Gerami Seresht, N (2016) A Fuzzy Discrete Event Simulation Framework for Construction Applications: Improving the Simulation Time Advancement. Journal of Construction Engineering and Management, 142(12).