Application of MCDM Methods in Optimizing Measurement Accuracy for Geodetic Monitoring of Engineering Structure Displacements

Darko Anđić

doi:10.67334/jemsci11202629

Authors

Darko Anđić Real Estate Administration, Department of State Survey and Mapping, Podgorica, Montenegro Author https://orcid.org/0000-0001-8016-4061

DOI:

https://doi.org/10.67334/jemsci11202629

Keywords:

Engineering Structure Displacement, Atypical Geodetic Control Network, Multi-Criteria Decision-Making, Optimal Measurement Accuracy, Alternative Ranking Correlation

Abstract

The purpose of this study is to develop and evaluate a decision-making framework for selecting the optimal measurement accuracy of geodetic instruments used in engineering structure displacement monitoring. The study investigates whether integrating multiple Multi-Criteria Decision-Making (MCDM) methods with different preference structures can provide reliable and robust support for this complex engineering geodesy problem under an unfavourable geodetic control network configuration. To address this question, the proposed framework combines the SWING weighting method with four MCDM methods, namely Proximity Indexed Value (PIV), Preference Analysis for Reference Ideal Solution (PARIS), Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS), and COmplex PRoportional ASsessment (COPRAS). Six total station alternatives were evaluated using criterion functions representing measurement precision and reliability characteristics under four preference structures represented by ordered 8-tuples of criterion weights. The framework was validated through comparative analysis using TOPSIS as the benchmark method, while ranking robustness and consistency were further examined through a leave-one-out sensitivity analysis. The results demonstrate that all four MCDM methods successfully addressed the considered optimization problem and consistently identified the same two best-performing alternatives. The comparative analysis revealed a high degree of agreement between PIV, PARIS, COPRAS, and the TOPSIS benchmark, whereas MARCOS exhibited slightly greater ranking variability while preserving the identification of the optimal solutions. Furthermore, the sensitivity analysis showed that ranking stability was influenced more strongly by the adopted preference structure than by the particular MCDM method employed. These findings are significant because they establish a comprehensive and reproducible framework for optimizing measurement accuracy in engineering geodesy, demonstrate the applicability of four MCDM methods not previously employed for this optimization problem, and provide broader methodological insight into the importance of combining multiple preference structures, comparative analysis, and sensitivity analysis when evaluating MCDM performance.

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