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Title: BIM4D-based scheduling for assembling and lifting in precast-enabled construction
Authors: Huang, Lihui
Pradhan, Roshan
Dutta, Souravik
Cai, Yiyu
Keywords: Engineering::Mechanical engineering
Issue Date: 2022
Source: Huang, L., Pradhan, R., Dutta, S. & Cai, Y. (2022). BIM4D-based scheduling for assembling and lifting in precast-enabled construction. Automation in Construction, 133, 103999-.
Journal: Automation in Construction 
Abstract: This research addresses the problem of assembly scheduling in crane-assisted precast construction while considering issues of building layout interference and optimal crane lifting. Traditionally, assembly scheduling and lifting path planning are treated as two separate issues due to their distinct natures. The current work introduces an approach that combines them for precast construction planning to achieve a comprehensive and cost-effective solution. A BIM4D-based Intelligent Assembly Scheduler (BIAS) is designed in conjunction with the Computer-Aided Lifting Planner developed at Nanyang Technological University, Singapore. BIM4D is the Building Information Modeling (BIM) with the time dimension (i.e., scheduling information). Our scheduler takes an inbuilt timeframe for selected precast elements from BIM4D as input and outputs the micro-schedule in terms of the assembly sequence for these precast elements. This problem is solved using multi-objective optimization. Given a group of precast elements and their BIM4D timeframe, the micro-scheduling is determined based on (1) the relative importance of the elements’ physical properties, (2) the interference (neighbouring relation) among the elements’ positions, and (3) collision-free lifting paths of the elements. A Multi-level Elitist Genetic Algorithm (MEGA) is proposed to determine the optimal sequence taking into consideration of both assembling and lifting for the elements. A case study is performed with the BIM4D data of a residential building. The results of the case study demonstrate BIAS's efficiency and effectiveness for BIM4D based construction scheduling.
ISSN: 0926-5805
DOI: 10.1016/j.autcon.2021.103999
Schools: School of Mechanical and Aerospace Engineering 
Interdisciplinary Graduate School (IGS) 
Research Centres: Surbana Jurong-NTU Corporate Laboratory
Energy Research Institute @ NTU (ERI@N) 
Rights: © 2021 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:ERI@N Journal Articles
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