Using Artificial Neural Networks to Model Bricklaying Productivity

Orsolya Bokor*, Laura Florez Perez, Giovanni Pesce, Nima Gerami Seresht

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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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.
Original languageEnglish
Title of host publication2021 European Conference on Computing in Construction
PublisherEuropean Council on Computing in Construction (EC3)
Number of pages8
Publication statusE-pub ahead of print - 26 Jul 2021
Event2021 European Conference of Computing in Construction (2021 EC³) - Online Conference, Rhodes, Greece
Duration: 19 Jul 202128 Jul 2021
https://ec-3.org/conference2021/

Conference

Conference2021 European Conference of Computing in Construction (2021 EC³)
Abbreviated title2021 EC3
CountryGreece
CityRhodes
Period19/07/2128/07/21
Internet address

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