Developing and Optimizing Context-Specific Fuzzy Inference System-Based Construction Labor Productivity Models

Tsehayae, Abraham Assefa; Fayek, Aminah Robinson

doi:10.1061/(asce)co.1943-7862.0001127

Cited by 27 publications

(14 citation statements)

References 26 publications

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“…Gerami Seresht and Fayek [26] developed fuzzy system dynamics technique by integrating system dynamics and fuzzy logic to model the multifactor productivity of equipment-intensive activities. Tsehayae and Fayek [9] demonstrated the application of data-driven fuzzy clustering in the development of FIS. They then used genetic algorithm (GA)-based optimization to address the FIS limitation, which is the inability to learn from data.…”

Section: Literature Review On Construction Productivity Modelingmentioning

confidence: 99%

Section: Clp Data Identificationmentioning

confidence: 99%

“…In this study, the proposed methodology was used to predict and optimize CLP of concrete placing activities, using the data collected by Tsehayae and Fayek [9] in a previous study. Data were collected in Alberta, Canada, in four construction project contexts, including residential and commercial warehouse buildings, residential and commercial high-rise buildings, industrial buildings, and institutional buildings.…”

Section: Clp Data Identificationmentioning

confidence: 99%

“…Furthermore, the accurate prediction of CLP is essential for effective scheduling and planning prior to and during project execution [7]. CLP is a form of efficiency measure that is mainly defined as a ratio of units of output (i.e., project components) to units of input (e.g., labor work hours, and labor cost) or vice versa [7][8][9]. Many factors can potentially affect CLP, reducing the accuracy of a predictive model and imposing the risk of data overfitting [10].…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Artificial Intelligence HFS-RF-PSO Model for Construction Labor Productivity Prediction and Optimization

2021

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This paper presents a novel approach, using hybrid feature selection (HFS), machine learning (ML), and particle swarm optimization (PSO) to predict and optimize construction labor productivity (CLP). HFS selects factors that are most predictive of CLP to reduce the complexity of CLP data. Selected factors are used as inputs for four ML models for CLP prediction. The study results showed that random forest (RF) obtains better performance in mapping the relationship between CLP and selected factors affecting CLP, compared with the other three models. Finally, the integration of RF and PSO is developed to identify the maximum CLP value and the optimum value of each selected factor. This paper introduces a new hybrid model named HFS-RF-PSO that addresses the main limitation of existing CLP prediction studies, which is the lack of capacity to optimize CLP and its most predictive factors with respect to a construction company’s preferences, such as a targeted CLP. The major contribution of this paper is the development of the hybrid HFS-RF-PSO model as a novel approach for optimizing factors that influence CLP and identifying the maximum CLP value.

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Section: Literature Review On Construction Productivity Modelingmentioning

confidence: 99%

Section: Clp Data Identificationmentioning

confidence: 99%

Section: Clp Data Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hybrid Artificial Intelligence HFS-RF-PSO Model for Construction Labor Productivity Prediction and Optimization

2021

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“…These FISs were used to predict concreting labor productivity for four different project contexts (industrial, warehouse, high-rise, and institutional buildings). The FISs were optimized using genetic algorithms to improve both accuracy and interpretability, the latter of which allows users to understand the reasoning behind the models and their results (Tsehayae and Fayek 2016). These techniques were also applied to develop labor productivity prediction models for electrical and shutdown activities.…”

Section: Fuzzy Machine Learning and Fuzzy Optimization To Predict Conmentioning

confidence: 99%

Fuzzy Logic and Fuzzy Hybrid Techniques for Construction Engineering and Management

Fayek

2020

J. Constr. Eng. Manage.

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Construction engineering and management are vital for successful project execution, and both researchers and practitioners continually seek ways to improve construction processes. Fuzzy logic plays an important role in many construction engineering and management applications, which are reviewed in this paper. This paper discusses the limitations of fuzzy logic and how this theory has been combined with other modeling techniques to develop fuzzy hybrid techniques, and describes the aspects of construction problems and decision making that are most effectively modeled using these techniques. Fuzzy hybrid techniques that are most common in construction are presented and examples from construction literature and the author's research program are provided. The author shares her vision of future research in this area, which is based on her expertise and experiences collaborating with construction industry partners, who have helped shape her research program and its impact on industry. Finally, the author presents her thoughts on the challenges construction researchers face in translating research to practice and measuring its impact, and she discusses some potential solutions from her research program. This paper is based on the 2019 Peurifoy Construction Advancement Address, which the author presented in

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Evolution of studies in construction productivity: A systematic literature review (2006–2017)

Dixit

Mandal

Thanikal

et al. 2019

Ain Shams Engineering Journal

122

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Developing and Optimizing Context-Specific Fuzzy Inference System-Based Construction Labor Productivity Models

Cited by 27 publications

References 26 publications

Hybrid Artificial Intelligence HFS-RF-PSO Model for Construction Labor Productivity Prediction and Optimization

Hybrid Artificial Intelligence HFS-RF-PSO Model for Construction Labor Productivity Prediction and Optimization

Fuzzy Logic and Fuzzy Hybrid Techniques for Construction Engineering and Management

Evolution of studies in construction productivity: A systematic literature review (2006–2017)

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