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Using Artificial Neural Networks to Model Bricklaying Productivity

Bokor, O; Florez-Perez, L; Pesce, G; Gerami Seresht, N; (2021) Using Artificial Neural Networks to Model Bricklaying Productivity. In: Proceedings of the 2021 European Conference on Computing in Construction. (pp. pp. 1-7). European Council on Computing in Construction (EC3) Green open access

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Abstract

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.

Type: Proceedings paper
Title: Using Artificial Neural Networks to Model Bricklaying Productivity
Event: 2021 European Conference on Computing in Construction
ISBN-13: 978-3-907234-54-9
Open access status: An open access version is available from UCL Discovery
DOI: 10.35490/ec3.2021.155
Publisher version: http://www.doi.org/10.35490/EC3.2021.155
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: artificial neural networks, bricklaying, construction, labour productivity, scheduling
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
URI: https://discovery.ucl.ac.uk/id/eprint/10134748
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