Procjena troškova izgradnje AB i prednapetih betonskih mostova primjenom strojnog učenja

Stjepan Lakusic

doi:10.14256/jce.2738.2019

Abstract

Seven state-of-the-art machine learning techniques for estimation of construction costs of reinforced-concrete and prestressed concrete bridges are investigated in this paper, including artificial neural networks (ANN) and ensembles of ANNs, regression tree ensembles (random forests, boosted and bagged regression trees), support vector regression (SVR) method, and Gaussian process regression (GPR). A database of construction costs and design characteristics for 181 reinforced-concrete and prestressed-concrete bridges is created for model training and evaluation.

Highlights

There are currently more than two million bridges in operation worldwide, and their number is constantly increasing [1]
Several state-of-the-art machine learning techniques for estimation of construction costs of RC and PC bridges are compared including artificial neural network (ANN) and ensembles of ANNs, regression tree ensembles, Support vector regression (SVR), and Gaußsche Regressionsprozess (GPR)
This paper gives a comprehensive overview of the state-of-the-art machine learning methods that can be used for estimating these costs, including MLP-ANN, ensembles of MLP-ANNs, regression tree ensembles, SVR with RBF kernel, and GPR with exponential, squared exponential, Matern, and rational quadratic covariance functions

Summary

Introduction

There are currently more than two million bridges in operation worldwide, and their number is constantly increasing [1]. Based on the analysis of 258 transport infrastructure projects worth $90 billion (U.S.), it was found that in the vast majority of projects actual costs were significantly higher than initially estimated, e.g. 34 % higher on an average for bridges and tunnels [6]. This underestimation is obviously not an error, it is prone to subjectivity, and may potentially introduce biases in the decision making process [6]. Chou et al studied models based on multiple regression analysis, CBR and ANNs, to predict bid prices for bridge construction projects in Taiwan [17]. Some of the proposed models, such as GPR, have not been, to the best of our knowledge, previously used for estimating construction costs of transport infrastructure projects

Methods

Results

Conclusion