Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
 
Session Overview
Session
Data Analysis, Simulation, & Resilience 2
Time:
Wednesday, 28/July/2021:
1:00pm - 2:30pm

Session Chair: Alessandro Carbonari

Subtopic: Machine Learning


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Presentations
1:00pm - 1:20pm

DETERMINING CONSTRUCTION METHOD PATTERNS TO AUTOMATE AND OPTIMISE SCHEDULING – A GRAPH-BASED APPROACH

Ying Hong1, Vahan Hovhannisyan2, Haiyan Xie3, Ioannis Brilakis1

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.



1:20pm - 1:40pm

Using Artificial Neural Networks to Model Bricklaying Productivity

Orsolya Bokor1, Laura Florez-Perez2, Giovanni Pesce1, Nima Gerami Seresht1

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.



 
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