TY - GEN
T1 - ConLogAI – Concept for an AI-enabled platform for construction logistics scheduling
AU - Gehring, Maximilian
AU - Brötzmann, Jascha
AU - Rüppel, Uwe
PY - 2025/7/1
Y1 - 2025/7/1
N2 - Construction logistics management plays a crucial role in the successful execution of construction projects. Building Information Modeling (BIM) supports scheduling processes by providing structured project data. The integration of Artificial Intelligence (AI) further enhances the impact of BIM by enabling automation, pattern recognition, and intelligent decision-making. Additionally, digital technologies such as the Internet of Things (IoT) can support data-driven adaptations during execution. This paper introduces a concept for a BIM-based, AI-enabled construction scheduling platform designed to address critical challenges in construction logistics planning. By leveraging BIM data as a foundation, the platform incorporates AI-driven semantic enrichment to derive task relationships and dependencies, while employing advanced scheduling algorithms to generate optimized execution plans. The proposed system aims to enable dynamic, resource-constrained scheduling and facilitate real-time adaptation to disruptions. A preliminary implementation validates the feasibility of the concept and highlights its potential to improve transparency, efficiency, and responsiveness in construction logistics management.
AB - Construction logistics management plays a crucial role in the successful execution of construction projects. Building Information Modeling (BIM) supports scheduling processes by providing structured project data. The integration of Artificial Intelligence (AI) further enhances the impact of BIM by enabling automation, pattern recognition, and intelligent decision-making. Additionally, digital technologies such as the Internet of Things (IoT) can support data-driven adaptations during execution. This paper introduces a concept for a BIM-based, AI-enabled construction scheduling platform designed to address critical challenges in construction logistics planning. By leveraging BIM data as a foundation, the platform incorporates AI-driven semantic enrichment to derive task relationships and dependencies, while employing advanced scheduling algorithms to generate optimized execution plans. The proposed system aims to enable dynamic, resource-constrained scheduling and facilitate real-time adaptation to disruptions. A preliminary implementation validates the feasibility of the concept and highlights its potential to improve transparency, efficiency, and responsiveness in construction logistics management.
KW - automated scheduling
KW - BIM
KW - construction logistics
KW - artificial intelligence
U2 - 10.17868/strath.00093231
DO - 10.17868/strath.00093231
M3 - Conference contribution book
SN - 9781914241826
BT - EG-ICE 2025
A2 - Moreno-Rangel, Alejandro
A2 - Kumar, Bimal
CY - Glasgow
T2 - EG-ICE 2025: International Workshop on Intelligent Computing in Engineering
Y2 - 1 July 2025 through 3 July 2025
ER -