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Data Analysis, Simulation, & Resilience 2
Subtopic: Machine Learning
1:00pm - 1:20pm
DETERMINING CONSTRUCTION METHOD PATTERNS TO AUTOMATE AND OPTIMISE SCHEDULING – A GRAPH-BASED APPROACH
1University of Cambridge, United Kingdom; 2nPlan, United Kingdom; 3Illinois State University, Illinois, United States
Creating quality construction schedules to mitigate delays often relies on experience. The lack of dissemination of historic decision reasoning makes it harder. This study proposes a graph-based method to find the time- and risk-efficient construction method patterns from historic projects to help schedulers improve productivity and accuracy. The method leverages schedule data obtained from a Tier-1 contractor and validates for excavation activities. The results indicate that the most time-efficient excavation activities can be done in 0.6% of total project time. The proposed method can help industry professionals standardise scheduling guidelines and automate the generation of construction schedules for critical subtasks.
External Resource: https://ec-3.org/publications/conferences/2021/paper/?id=165
1:20pm - 1:40pm
Using Artificial Neural Networks to Model Bricklaying Productivity
1Northumbria University, United Kingdom; 2University College London, United Kingdom
The pre-planning phase prior to construction is crucial for ensuring an effective and efficient project delivery. Realistic productivity rates forecasted during pre-planning are essential for accurate schedules, cost calculation, and resource allocation. To obtain such productivity rates, the relationships between various factors and productivity need to be understood. Artificial neural networks (ANNs) are suitable for modelling these complex interactions typical of construction activities, and can be used to assist project managers to produce suitable solutions for estimating productivity. This paper presents the steps of determining the network configurations of an ANN model for bricklaying productivity.
External Resource: https://ec-3.org/publications/conferences/2021/paper/?id=155
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