Cross-industry Review of Autonomous Alignment Technologies: Adaptation Potential for Modular Construction

Abstract

Module alignment in modular construction faces significant challenges due to reliance on manual labor and dimensional complexities, leading to project delays. While other industries have successfully implemented automated alignment technologies, the construction industry has lagged behind, particularly in modular construction. Despite extensive research on construction technologies, there is a notable lack of comprehensive reviews examining alignment technologies from various sectors that could be adapted for modular construction. This study aims to fill this knowledge gap by employing a mixed review approach to explore technologies capable of achieving autonomous module alignment. Analyzing 200 publications from 2006 to 2023, key findings reveal that computer vision systems used in port operations can achieve millimeter-level accuracy in positioning large components, even under challenging environmental conditions—capabilities that can be directly transferred to modular construction. Additionally, LiDAR (Light Detection and Ranging) technology shows promise for exceptional precision in spatial measurement and positioning, particularly valuable for complex module arrangements, while other sensor-based technologies like the Inertial Measurement Unit (IMU), ultrasonic sensor offer orientation tracking and reliable distance measurements, respectively, even in conditions where primary systems might struggle. The study recommends (1) adapting proven container positioning technologies for modular construction, (2) developing construction-specific alignment algorithms that combine computer vision and LiDAR capabilities, (3) implementing sensor-based guidance systems for crane operators, and (4) establishing industry standards for automated module alignment systems. These findings offer a roadmap for researchers and practitioners to advance autonomous alignment solutions in modular construction.

Publication DOI: https://doi.org/10.1016/j.jclepro.2025.145101
Divisions: College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Civil Engineering
College of Engineering & Physical Sciences
Aston University (General)
Additional Information: Copyright © 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( https://creativecommons.org/licenses/by/4.0/ ).
Uncontrolled Keywords: Artificial intelligence,Automation,Computer vision,Industry 4.0,LiDAR,Modular construction,Module alignment,Object detection,Renewable Energy, Sustainability and the Environment,General Environmental Science,Strategy and Management,Industrial and Manufacturing Engineering
Publication ISSN: 1879-1786
Last Modified: 01 Apr 2025 07:12
Date Deposited: 24 Feb 2025 10:06
Full Text Link:
Related URLs: https://www.sci ... 4512?via%3Dihub (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2025-03-01
Published Online Date: 2025-02-21
Accepted Date: 2025-02-20
Authors: Abdulai, Sulemana Fatoama
Zayed, Tarek
Wuni, Ibrahim Yahaya
Antwi Afari, Maxwell Fordjour (ORCID Profile 0000-0002-6812-7839)
Yussif, Abdul-Mugis

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