A BIM-based intelligent approach to rebar layout optimization for reinforced concrete columns
Year: 2024
Type: article
Abstract: Current conventional rebar practices in the architecture, engineering, and construction (AEC) industry rely on manual methods, which are tedious, time-consuming, inefficient, and error-prone, often resulting in significant rebar waste. The Design-Bid-Build (DBB) project delivery method further fragments stakeholder collaboration, exacerbating these inefficiencies. In contrast, the Integrated Project Delivery (IPD) approach, combined with Building Information Modeling (BIM), enhances coordination and reduces both rework and material waste. Despite these advancements, engineers still manually model rebar in BIM, highlighting the urgent need to shift to intelligent, automatic BIM-based practices. Existing rebar layout optimization and collision detection methods often require complex programming skills, limiting their accessibility and usability. While Dynamo and Navisworks offer promising solutions, their utilization in this domain remains limited. This study proposes a BIM-based algorithm for intelligent rebar layout optimization using Dynamo, coupled with automatic clash detection via Navisworks. The developed Dynamo script and clash detection process were validated using the rebar arrangement of a continuous column, and a standardized rebar summary sheet was created to support the script. The results demonstrated that the framework significantly reduced working hours compared to manual modeling and achieved high accuracy in rebar modeling. This approach enables engineers to efficiently generate optimized, clash-free rebar layouts, transforming conventional practices and advancing smart, sustainable construction.
Source: Journal of Building Engineering
Institutions: Kyung Hee University, University of Indonesia
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,320
Awards: G2997181003, G6798656709
OpenAlex ID: W4405518359
