Problems in the field of construction management are sophisticated, highly uncertain, and context-dependent. Thus, the application of artificial intelligence (AI) to tackle such problems can be a promising research direction. Considering the features and advantages of each AI technique, this research integrates various prevalent advanced approaches to establish a novel decision support system that utilizes Least Squares Support Vector Machine (LS-SVM), Differential Evolution (DE), Adaptive Time Function (ATF), and Fuzzy Logic (FL). At the first stage, LS-SVM is incorporated with DE to create Evolutionary Least Squares Support Vector Machine Inference System (ELSIS) in which LS-SVM is utilized as a supervised learning method used for regression analysis/ classification in high dimensional space and Differential Evolution is employed to identify the optimal set of tuning parameters. At the second stage, ATF is integrated into ELSIS to establish Adaptive Time-Dependent Evolutionary Least Squares Support Vector Machine Inference System (ELSIST). In ELSIST, ATF is deployed to deal with the unbalanced nature of time series data. At the final stage, ELSIS incorporates FL to develop Evolutionary Fuzzy Least Squares Support Vector Machine Inference System (EFLSIS) in which FL aims at facilitating the system capability of approximate reasoning and coping with vague information. Experimental results obtained from system applications demonstrate that the newly established inference system can be a highly beneficial for decision-makers when solving various problems in the field of construction management.

Problems in the field of construction management are sophisticated, highly uncertain, and context-dependent. Thus, the application of artificial intelligence (AI) to tackle such problems can be a promising research direction. Considering the features and advantages of each AI technique, this research integrates various prevalent advanced approaches to establish a novel decision support system that utilizes Least Squares Support Vector Machine (LS-SVM), Differential Evolution (DE), Adaptive Time Function (ATF), and Fuzzy Logic (FL). At the first stage, LS-SVM is incorporated with DE to create Evolutionary Least Squares Support Vector Machine Inference System (ELSIS) in which LS-SVM is utilized as a supervised learning method used for regression analysis/ classification in high dimensional space and Differential Evolution is employed to identify the optimal set of tuning parameters. At the second stage, ATF is integrated into ELSIS to establish Adaptive Time-Dependent Evolutionary Least Squares Support Vector Machine Inference System (ELSIST). In ELSIST, ATF is deployed to deal with the unbalanced nature of time series data. At the final stage, ELSIS incorporates FL to develop Evolutionary Fuzzy Least Squares Support Vector Machine Inference System (EFLSIS) in which FL aims at facilitating the system capability of approximate reasoning and coping with vague information. Experimental results obtained from system applications demonstrate that the newly established inference system can be a highly beneficial for decision-makers when solving various problems in the field of construction management.

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