Machine learning techniques applied to construction: A hybrid bibliometric analysis of advances and future directions

Jose Garcia, Gabriel Villavicencio, Francisco Altimiras, Broderick Crawford, Ricardo Soto, Vinicius Minatogawa, Matheus Franco, David Martínez-Muñoz, Víctor Yepes

Research output: Contribution to journalReview articlepeer-review

Abstract

Complex industrial problems coupled with the availability of a more robust computing infrastructure present many challenges and opportunities for machine learning (ML) in the construction industry. This paper reviews the ML techniques applied to the construction industry, mainly to identify areas of application and future projection in this industry. Studies from 2015 to 2022 were analyzed to assess the latest applications of ML techniques in construction. A methodology was proposed that automatically identifies topics through the analysis of abstracts using the Bidirectional Encoder Representations from Transformers technique to select main topics manually subsequently. Relevant categories of machine learning applications in construction were identified and analyzed, including applications in concrete technology, retaining wall design, pavement engineering, tunneling, and construction management. Multiple techniques were discussed, including various supervised, deep, and evolutionary ML algorithms. This review study provides future guidelines to researchers regarding ML applications in construction.

Original languageEnglish
Article number104532
JournalAutomation in Construction
Volume142
DOIs
StatePublished - Oct 2022
Externally publishedYes

Keywords

  • BERT
  • Concretes
  • Construction
  • Construction management
  • Machine learning
  • Pavements
  • Retaining walls
  • Tunnels

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