Bibliometric mapping of MCDM methods in AEC industry: identifying trends for sustainable development

This study investigates integrating multi-criteria decision-making (MCDM) approaches to improve sustainability in the architecture, engineering, and construction (AEC) industry. Through a systemic literature analysis done through the Scopus database and the visualization of key elements through VOSViewer, the study examines the applications of MCDM in sustainable construction, with particular attention to material selection, contractor procurement, waste management, risk assessment, and technological integration, which are essential elements towards sustainable development of the AEC industry. The results highlight how common and successful hybrid MCDM and MADM methods are. These approaches provide all-encompassing answers to challenging problems, from project mitigation to material procurement. The evaluation emphasizes the value of MCDM methods in supporting sustainable practices across the construction lifecycle, streamlining supply chains, and enabling well-informed decision-making. In the end, this study emphasizes how critical it is to carry out further research and apply MCDM frameworks to promote sustainable development in the construction sector and balance development objectives with the protection of the environment and the welfare of society.

Authors

Institutions

Publication Details

Journal
Journal of Environmental Engineering and Landscape Management
Published
2025-04-23
DOI
https://doi.org/10.3846/jeelm.2025.23569
Citations
3
Primary Topic
Multi-Criteria Decision Making
Type
article
Field-Weighted Citation Impact
6.32
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Bibliometric mapping of MCDM methods in AEC industry: identifying trends for sustainable development

Zenonas Turskis, Jūratė Šliogerienė, Thilina Ganganath Weerakoon
3 citations
Journal of Environmental Engineering and Landscape Management
Multi-Criteria Decision Making
6.32
article

Bibliometric mapping of MCDM methods in AEC industry: identifying trends for sustainable development

Zenonas Turskis, Jūratė Šliogerienė, Thilina Ganganath Weerakoon
article en
3 citations

Abstract

This study investigates integrating multi-criteria decision-making (MCDM) approaches to improve sustainability in the architecture, engineering, and construction (AEC) industry. Through a systemic literature analysis done through the Scopus database and the visualization of key elements through VOSViewer, the study examines the applications of MCDM in sustainable construction, with particular attention to material selection, contractor procurement, waste management, risk assessment, and technological integration, which are essential elements towards sustainable development of the AEC industry. The results highlight how common and successful hybrid MCDM and MADM methods are. These approaches provide all-encompassing answers to challenging problems, from project mitigation to material procurement. The evaluation emphasizes the value of MCDM methods in supporting sustainable practices across the construction lifecycle, streamlining supply chains, and enabling well-informed decision-making. In the end, this study emphasizes how critical it is to carry out further research and apply MCDM frameworks to promote sustainable development in the construction sector and balance development objectives with the protection of the environment and the welfare of society.

Journal of Environmental Engineering and Landscape ManagementVol. 33(2)
Vilnius Gediminas Technical University (LT)
Openalex Percentile: Top 4%
Multi-Criteria Decision Making
6.32
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.