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Antunes, R, González, V A, Walsh, K, Rojas, O, O’Sullivan, M and Odeh, I (2018) Benchmarking Project-Driven Production in Construction Using Productivity Function: Capacity and Cycle Time. Journal of Construction Engineering and Management, 144(03).
Atherinis, D, Bakowski, B, Velcek, M and Moon, S (2018) Developing and Laboratory Testing a Smart System for Automated Falsework Inspection in Construction. Journal of Construction Engineering and Management, 144(03).
Castillo, T, Alarcón, L F and Salvatierra, J L (2018) Effects of Last Planner System Practices on Social Networks and the Performance of Construction Projects. Journal of Construction Engineering and Management, 144(03).
Chiang, Y, Wong, F K and Liang, S (2018) Fatal Construction Accidents in Hong Kong. Journal of Construction Engineering and Management, 144(03).
Darwish, M, Elsayed, A Y and Nassar, K (2018) Design and Constructability of a Novel Funicular Arched Steel Truss Falsework. Journal of Construction Engineering and Management, 144(03).
Huo, T, Ren, H, Cai, W, Shen, G Q, Liu, B, Zhu, M and Wu, H (2018) Measurement and Dependence Analysis of Cost Overruns in Megatransport Infrastructure Projects: Case Study in Hong Kong. Journal of Construction Engineering and Management, 144(03).
Jang, W, Yu, G, Jung, W, Kim, D and Han, S H (2018) Financial Conflict Resolution for Public-Private Partnership Projects Using a Three-Phase Game Framework. Journal of Construction Engineering and Management, 144(03).
Liao, P, Shi, H, Su, Y and Luo, X (2018) Development of Data-Driven Influence Model to Relate the Workplace Environment to Human Error. Journal of Construction Engineering and Management, 144(03).
Shrestha, P and Behzadan, A H (2018) Chaos Theory–Inspired Evolutionary Method to Refine Imperfect Sensor Data for Data-Driven Construction Simulation. Journal of Construction Engineering and Management, 144(03).
- Type: Journal Article
- Keywords: Construction simulation; Activity sequence; Chaos theory; Genetic algorithm; Fuzzy data; Sensor networks; Quantitative methods;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001441
- Abstract:
In construction planning, inherent uncertainties in activity sequences and variations of work packages can often cause deviations from project plans. In order to effectively study and preempt scenarios that may lead to undesired project time and cost overruns, several construction domain-specific simulation platforms have been designed and introduced in the past. Although transitioning to simulation-based decision-making has great potential to streamline project conceptualization and early planning, the inability of simulations to evolve with the real system can significantly limit their applicability and render them unreliable for construction-phase decision-making. This issue has been identified as one of the grand challenges to industry-wide adoption of simulation models throughout the lifecycle of construction and infrastructure projects. A potential solution to this problem is to equip simulations with sensing systems that interact with and collect project data in runtime. This approach, however, requires meticulous effort to procure, set up, operate, synchronize, calibrate, and maintain sensing devices over a large project area. This practical challenge can potentially hinder the ability of simulation systems to adapt and remain relevant for decision-making. Moreover, sensor readings are often noisy and imperfect. If used as inputs to a simulation model, this noise in sensor data can create volatility in simulation outputs. Chaos theory describes how small variations in input can cause high output errors even in simple systems. To this end, this paper presents an evolutionary algorithm to process and significantly reduce noise in imperfect sensor data captured by low-cost consumer-grade sensors. The main contribution of this work to the body of knowledge is a scientific methodology of refining imperfect (noisy) sensor data and producing clean datasets that can be used to generate more stable simulation input models. This methodology was validated in a field experiment in which simulation models were created using noisy (imperfect) as well as refined sensor data. The output of each simulation model was compared with ground truth values. Analysis of results shows that using refined sensor data to generate simulation models significantly improves the accuracy and reliability of the simulation output.
Tatum, C B ( (2018) Construction Engineering Research: Integration and Innovation. Journal of Construction Engineering and Management, 144(03).
Tatum, C B ( (2018) Learning Construction Engineering: Why, What, and How?. Journal of Construction Engineering and Management, 144(03).