Using Artificial Neural Networks to Model Bricklaying Productivity

Orsolya Bokor; Laura Florez Perez; Giovanni Pesce; Nima Gerami Seresht

doi:10.35490/EC3.2021.155

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 proceeding › Conference contribution › peer-review

1 Citation (Scopus)

25 Downloads (Pure)

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 language	English
Title of host publication	2021 European Conference on Computing in Construction
Publisher	European Council on Computing in Construction (EC3)
Pages	52-58
Number of pages	7
ISBN (Electronic)	9783907234549
DOIs	https://doi.org/10.35490/EC3.2021.155
Publication status	Published - 26 Jul 2021
Event	2021 European Conference of Computing in Construction (2021 EC³) - Online Conference, Rhodes, Greece Duration: 19 Jul 2021 → 28 Jul 2021 https://ec-3.org/conference2021/

Conference

Conference	2021 European Conference of Computing in Construction (2021 EC³)
Abbreviated title	2021 EC3
Country/Territory	Greece
City	Rhodes
Period	19/07/21 → 28/07/21
Internet address	https://ec-3.org/conference2021/

Keywords

Productivity
Artificial Neural Networks
Modelling
Bricklayer

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Cite this

@inproceedings{58ac539d0c8043a5ab6b788696935b3c,

title = "Using Artificial Neural Networks to Model Bricklaying Productivity",

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.",

keywords = "Productivity, Artificial Neural Networks, Modelling, Bricklayer",

author = "Orsolya Bokor and {Florez Perez}, Laura and Giovanni Pesce and {Gerami Seresht}, Nima",

year = "2021",

month = jul,

day = "26",

doi = "10.35490/EC3.2021.155",

language = "English",

pages = "52--58",

booktitle = "2021 European Conference on Computing in Construction",

publisher = "European Council on Computing in Construction (EC3)",

note = "2021 European Conference of Computing in Construction (2021 EC³), 2021 EC3 ; Conference date: 19-07-2021 Through 28-07-2021",

url = "https://ec-3.org/conference2021/",

}

Bokor, O, Florez Perez, L, Pesce, G & Gerami Seresht, N 2021, Using Artificial Neural Networks to Model Bricklaying Productivity. in 2021 European Conference on Computing in Construction. European Council on Computing in Construction (EC3), pp. 52-58, 2021 European Conference of Computing in Construction (2021 EC³), Rhodes, Greece, 19/07/21. https://doi.org/10.35490/EC3.2021.155

TY - GEN

T1 - Using Artificial Neural Networks to Model Bricklaying Productivity

AU - Bokor, Orsolya

AU - Florez Perez, Laura

AU - Pesce, Giovanni

AU - Gerami Seresht, Nima

PY - 2021/7/26

Y1 - 2021/7/26

N2 - 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.

AB - 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.

KW - Productivity

KW - Artificial Neural Networks

KW - Modelling

KW - Bricklayer

U2 - 10.35490/EC3.2021.155

DO - 10.35490/EC3.2021.155

M3 - Conference contribution

SP - 52

EP - 58

BT - 2021 European Conference on Computing in Construction

PB - European Council on Computing in Construction (EC3)

T2 - 2021 European Conference of Computing in Construction (2021 EC³)

Y2 - 19 July 2021 through 28 July 2021

ER -

Using Artificial Neural Networks to Model Bricklaying Productivity

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Conference

Keywords

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