Scholarlink

<p>The construction industry faces unprecedented challenges in meeting global housing demands, with conventional methods proving inadequate. Modular Integrated Construction (MiC) has emerged as a promising alternative, yet widespread adoption remains hindered by multiple interconnected factors. Previous studies have adopted narrow perspectives, focusing predominantly on barriers with limited holistic examination of drivers and strategies, and have lacked advanced multi-criteria prioritization methodologies. Additionally, they have inadequately addressed developing economies where housing deficits are most acute. This study provides a thorough review by identifying, categorizing, and prioritizing barriers, drivers, and strategies that affect MiC implementation, using the Frequency-based Spherical Fuzzy Analytical Hierarchical Process (SF-AHP). Methodology. The research employed scientometric analysis and a systematic literature review, following PRISMA guidelines. Analysis identified 71 barriers, 33 drivers, and 33 strategies across multiple categories. Knowledge Barriers emerged as most critical (20.79% category weight), with limited design and installation experience (B17: 4.73%) ranking highest globally. Economic drivers dominated enablers (19.32%), while waste reduction (D28: 6.79%) achieved maximum global weight. Technical strategies led implementation approaches (25.64%), with BIM technology (S19: 5.68%) ranking first. Correlation analysis (R²=0.812–0.950) was used as an internal diagnostic to examine ordinal consistency between frequency inputs and SF-AHP derived global weights. The study identified thematic gaps in life-cycle assessment and circular economy integration, contextual gaps in developing economies, and methodological gaps in empirical validation, providing evidence-based frameworks for global MiC adoption.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Systematic Review and Scientometric Analysis of Modular Integrated Construction with a Spherical Fuzzy AHP Framework

  • 期刊论文

  • 发表日期：

    2026年7月28日

查看全文
  • Abdulgafar Dotun Oyelola,
  • Ridwan Taiwo,
  • Molusiwa Stephan Ramabodu,
  • Samuel H.P. Chikafalimani,
  • Taofeek Adesola Nureni,
  • Azeez Arisekola Jimoh,
  • Hammed Olalekan Hussain,
  • Imoleayo Abraham Awodele,
  • Adesola Habeeb Adegoke

摘要

The construction industry faces unprecedented challenges in meeting global housing demands, with conventional methods proving inadequate. Modular Integrated Construction (MiC) has emerged as a promising alternative, yet widespread adoption remains hindered by multiple interconnected factors. Previous studies have adopted narrow perspectives, focusing predominantly on barriers with limited holistic examination of drivers and strategies, and have lacked advanced multi-criteria prioritization methodologies. Additionally, they have inadequately addressed developing economies where housing deficits are most acute. This study provides a thorough review by identifying, categorizing, and prioritizing barriers, drivers, and strategies that affect MiC implementation, using the Frequency-based Spherical Fuzzy Analytical Hierarchical Process (SF-AHP). Methodology. The research employed scientometric analysis and a systematic literature review, following PRISMA guidelines. Analysis identified 71 barriers, 33 drivers, and 33 strategies across multiple categories. Knowledge Barriers emerged as most critical (20.79% category weight), with limited design and installation experience (B17: 4.73%) ranking highest globally. Economic drivers dominated enablers (19.32%), while waste reduction (D28: 6.79%) achieved maximum global weight. Technical strategies led implementation approaches (25.64%), with BIM technology (S19: 5.68%) ranking first. Correlation analysis (R²=0.812–0.950) was used as an internal diagnostic to examine ordinal consistency between frequency inputs and SF-AHP derived global weights. The study identified thematic gaps in life-cycle assessment and circular economy integration, contextual gaps in developing economies, and methodological gaps in empirical validation, providing evidence-based frameworks for global MiC adoption.