Neural Network-Based Approaches for Predicting Construction Overruns with Sustainability Considerations

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F26%3A0199599" target="_blank" >RIV/00216305:26110/26:0199599 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2071-1050/17/16/7559" target="_blank" >https://www.mdpi.com/2071-1050/17/16/7559</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/su17167559" target="_blank" >10.3390/su17167559</a>

Result language

angličtina

Original language name

Neural Network-Based Approaches for Predicting Construction Overruns with Sustainability Considerations

Original language description

This research focuses on developing neural network-based models for predicting time and cost overruns in construction projects during the construction phase, incorporating sustainability considerations. Previous studies have identified seven key performance areas that affect the final outcome: productivity, quality, time, cost, safety, team satisfaction, and client satisfaction. Although the interconnections among these performance areas are recognized, their exact relationships and impacts are not fully understood. Hence, the utilization of a neural networks proves to be highly beneficial in predicting the outcome of future construction projects, as it can learn from data and identify patterns, without requiring a complete understanding of these mutual influences. The neural network was trained and tested on the data collected on five completed construction projects, each analyzed at three distinct stages of execution. A total of 182 experiments were conducted to explore different neural network architectures. The most effective configurations for predicting time and cost overruns were identified and evaluated, demonstrating the potential of neural network-based approaches to support more sustainable and proactive project management. The time overrun prediction model demonstrated high accuracy, achieving a MAPE of 10.93%, RMSE of 0.128, and correlation of 0.979. While the cost overrun model showed a lower predictive accuracy, its MAPE (166.76%), RMSE (0.4179), and correlation (0.936) values indicate potential for further refinement. These findings highlight the applicability of neural network-based approaches in construction project management and their potential to support more proactive and informed decision-making.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20101 - Civil engineering

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Sustainability

ISSN

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e-ISSN

2071-1050

Volume of the periodical

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Issue of the periodical within the volume

17

Country of publishing house

CH - SWITZERLAND

Number of pages

17

Pages from-to

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UT code for WoS article

001558335900001

EID of the result in the Scopus database

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