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<p>Engineering initiatives typically face challenges in accurately establishing the financial sustainability of a certain model or expansion. Conventional cost evaluation methods could find it challenging to deal with the variability and complexity of real-world occurrences. By integrating data analysis, fuzzy logic, and the Analytic Hierarchy Process (AHP), this study gives a more precise and dependable way of figuring out engineering cost viability. The recommended method begins by classifying the cost-feasible assessment issue into an ordering of sub-criterion, criteria, and choices using the AHP framework. Historical data are utilized to determine the relative scales for the criteria. Fuzzy logic is nevertheless included in the AHP architecture to get over the inherent uncertainty and inaccuracy of cost analysis. Fuzzy sets and member characteristics are used to indicate the uncertainty in estimates of costs and provide the conditions for depicting the method of determining decisions in greater detail. To remove noise, outliers, and irrelevant data from the acquired data, the Wavelet transform (WT) is employed after the data set has been collected. It is produced, structured, and biased against particular features using Principal Component Analysis (PCA). Data mining methods are used to evaluate past expenditure data and identify pertinent patterns and correlations to be able to increase the efficacy of the Analytic Hierarchy Process Hybrid Fuzzy Clustering (AHP-HFC) model, as described in this study. The effectiveness of the improved adaptive AHPHFC is compared with traditional cost-estimating methods using quantitative analyses.The outcomes show that the suggested technique is more accurate, efficient, and capable of making decisions for evaluating architectural cost viability.</p>

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An Intelligent Evaluation Method of Engineering Cost Feasibility Model Based on AHP Method

  • 期刊论文

  • 发表日期：

    2026年4月29日

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  • Yang Liu

摘要

Engineering initiatives typically face challenges in accurately establishing the financial sustainability of a certain model or expansion. Conventional cost evaluation methods could find it challenging to deal with the variability and complexity of real-world occurrences. By integrating data analysis, fuzzy logic, and the Analytic Hierarchy Process (AHP), this study gives a more precise and dependable way of figuring out engineering cost viability. The recommended method begins by classifying the cost-feasible assessment issue into an ordering of sub-criterion, criteria, and choices using the AHP framework. Historical data are utilized to determine the relative scales for the criteria. Fuzzy logic is nevertheless included in the AHP architecture to get over the inherent uncertainty and inaccuracy of cost analysis. Fuzzy sets and member characteristics are used to indicate the uncertainty in estimates of costs and provide the conditions for depicting the method of determining decisions in greater detail. To remove noise, outliers, and irrelevant data from the acquired data, the Wavelet transform (WT) is employed after the data set has been collected. It is produced, structured, and biased against particular features using Principal Component Analysis (PCA). Data mining methods are used to evaluate past expenditure data and identify pertinent patterns and correlations to be able to increase the efficacy of the Analytic Hierarchy Process Hybrid Fuzzy Clustering (AHP-HFC) model, as described in this study. The effectiveness of the improved adaptive AHPHFC is compared with traditional cost-estimating methods using quantitative analyses.The outcomes show that the suggested technique is more accurate, efficient, and capable of making decisions for evaluating architectural cost viability.