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2025 | OriginalPaper | Buchkapitel

Forecasting Construction Cost of Pipelaying Projects Using Backpropagation Artificial Neural Network and Multiple Linear Regression

verfasst von : Norrodin V. Melog, Dante L. Silva, Russell L. Diona, Kevin Lawrence M. de Jesus

Erschienen in: Proceedings of the 4th International Civil Engineering and Architecture Conference

Verlag: Springer Nature Singapore

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Abstract

A crucial component of growth in infrastructure is estimating construction costs (CC) for pipelaying projects (PP) related to water distribution networks, which guarantees the effective and long-term provision of safe drinking water to communities. In this paper, an artificial neural network (ANN) and multiple linear regression (MLR) model was developed for predicting construction cost for pipelaying projects. The governing model (GM) has a model structure of 9-20-1 (input-hidden-output) with an R = 0.99992. The findings revealed that the ANN-based network was 13.127 times better than the MLR model, based on its MAPE of 3.214 and 42.194%, for ANN and MLR, respectively. The best network also has the lowest Akaike Information Criterion (AIC) among the simulated network structures indicating that it is the best network. The relative importance (RI) of the independent variables including the length, diameter, material type, hydrotesting works, disinfection works, demolition works, restoration works, duration delay, and liquidated damages were calculated utilizing the Garson’s algorithm (GA). It was seen using GA to compute the relative importance of each parameter that the order of influence is seen as restoration works (RW) > length > demolition works (DeW) > material type (MT) > diameter > disinfection works (DiW) > hydrotesting works (HW) > duration delay (D%) > liquidated damages (LD) wherein the restoration works is the most influential parameter. The findings of the study could be used as a reference for better planning and managing pipelaying project activities.

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Metadaten
Titel
Forecasting Construction Cost of Pipelaying Projects Using Backpropagation Artificial Neural Network and Multiple Linear Regression
verfasst von
Norrodin V. Melog
Dante L. Silva
Russell L. Diona
Kevin Lawrence M. de Jesus
Copyright-Jahr
2025
Verlag
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-97-5477-9_57