Framework to Analyze Construction Labor Productivity Using Fuzzy Data Clustering and Multi-Criteria Decision-Making

Kazerooni, Matin; Raoufi, Mohammad; Fayek, Aminah Robinson

doi:10.1061/9780784482865.006

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…Gurmu and Aibinu [27] used two questionnaires and developed a scoring tool to identify and prioritize construction equipment management practices that increase productivity. Kazerooni et al [3] developed a systemic framework for ranking CLP factors according to their importance for CLP improvement by integrating MCDM with fuzzy data clustering. They suggested various improvement strategies based on the identified key factors.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This is performed by computing the CR of the matrix , which includes the fuzzy preference numbers of the relative importance of each SSC versus another. Based on the approach used by Kazerooni et al [3], matrix is defuzzified into two crisp matrices. The first matrix, , includes the most promising value of the fuzzy numbers of matrix , and the second matrix, , includes the geometric mean of the lower and upper bounds of the fuzzy numbers.…”

Section: Fuzzy Ahpmentioning

confidence: 99%

“…Some examples of CLP improvement strategies include performing weekly reviews of crew compositions to ensure crew mix is per plan, providing clear instructions to craftspeople on how to complete tasks prior to execution, and scheduling regular inspections by the owner team to reduce interventions during project execution. However, budget, time, and resource constraints force construction companies to carry out only a limited number of CLP improvement strategies [3]. Furthermore, in the complex environment of construction, CLP is affected by numerous factors that are mostly interconnected [4,5].…”

Section: Introductionmentioning

confidence: 99%

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Prioritizing Construction Labor Productivity Improvement Strategies Using Fuzzy Multi-Criteria Decision Making and Fuzzy Cognitive Maps

2021

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Construction labor productivity (CLP) is affected by various interconnected factors, such as crew motivation and working conditions. Improved CLP can benefit a construction project in many ways, such as a shortened project life cycle and lowering project cost. However, budget, time, and resource restrictions force companies to select and implement only a limited number of CLP improvement strategies. Therefore, a research gap exists regarding methods for supporting the selection of CLP improvement strategies for a given project by quantifying the impact of strategies on CLP with respect to interrelationships among CLP factors. This paper proposes a decision support model that integrates fuzzy multi-criteria decision making with fuzzy cognitive maps to prioritize CLP improvement strategies based on their impact on CLP, causal relationships among CLP factors, and project characteristics. The proposed model was applied to determine CLP improvement strategies for concrete-pouring activities in building projects as an illustrative example. This study contributes to the body of knowledge by providing a systematic approach for selecting appropriate CLP improvement strategies based on interrelationships among the factors affecting CLP and the impact of such strategies on CLP. The results are expected to support construction practitioners with identifying effective improvement strategies to enhance CLP in their projects.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Fuzzy Ahpmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prioritizing Construction Labor Productivity Improvement Strategies Using Fuzzy Multi-Criteria Decision Making and Fuzzy Cognitive Maps

2021

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Predictive model for construction labour productivity using hybrid feature selection and principal component analysis

et al. 2022

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Construction labour productivity (CLP) is affected by numerous variables made up of subjective and objective factors. Thus, CLP modeling and prediction is a complex task, leading to high computational cost and the risk of overfitting of data. This paper proposes a predictive model for CLP by integrating hybrid feature selection (HFS), as a combination of filter and wrapper methods, with principal component analysis (PCA). This developed HFS-PCA method reduces the dimensionality and complexity of CLP data and obtains better prediction performance by identifying the most predictive factors. Identified factors are utilized as inputs for various classification methods to predict CLP. Finally, prediction error of the classification methods with and without using the proposed HFS-PCA method are compared, and the most accurate classification method is selected to develop the CLP predictive model. Experimental results show that using HFS-PCA for CLP prediction leads to better performances compared with past studies.

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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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Framework to Analyze Construction Labor Productivity Using Fuzzy Data Clustering and Multi-Criteria Decision-Making

Cited by 3 publications

References 11 publications

Prioritizing Construction Labor Productivity Improvement Strategies Using Fuzzy Multi-Criteria Decision Making and Fuzzy Cognitive Maps

Prioritizing Construction Labor Productivity Improvement Strategies Using Fuzzy Multi-Criteria Decision Making and Fuzzy Cognitive Maps

Predictive model for construction labour productivity using hybrid feature selection and principal component analysis

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

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