Cost and Material Quantities Prediction Models for the Construction of Underground Metro Stations

Abstract

This paper addresses the gap in the scientific literature regarding construction cost estimates for the construction of underground metro stations. It provides preliminary cost estimation models using linear regression for use by the Greek underground metro public transport authority for planning future extensions to the Athens and Thessaloniki networks. At the same time, it contributes to the body of knowledge by proposing material quantity prediction models and presents a two-stage preliminary cost estimation model for the construction of civil engineering works of underground metro stations. Stage one uses the construction cost budgets of six metro stations in Greece to develop a multilinear regression equation for the prediction of the overall cost for construction of civil engineering works; stage two provides estimates of material quantities using linear regression, key quantity ratios, and artificial neural networks. The data analyzed are from the prior measurements of quantities for the construction of the Chaidari to Piraeus extension of the Athens Metro Line 3. After comparing the actual values of costs and quantities with the corresponding predictions, acceptable discrepancies are observed. All models provide estimates within ±25% discrepancies, which are acceptable at the conceptual planning phase in order to initiate project funding quests.

Knowledge‐enhanced graph neural networks for construction material quantity estimation of reinforced concrete buildings

“…The research on CMQ estimation using data‐driven methods can be traced back to the 1990s (Singh, 1991) and continues to advance recently (Antoniou et al., 2023). The common methods used in relevant research include artificial neural network (ANN) (Adeli & Wu, 1998; Beljkaš et al., 2020; Dimitriou et al., 2018), support vector machine (Idowu & Lam, 2020), and regression analysis (Fragkakis et al., 2015; Son et al., 2013).…”

“…The common methods used in relevant research include artificial neural network (ANN) (Adeli & Wu, 1998; Beljkaš et al., 2020; Dimitriou et al., 2018), support vector machine (Idowu & Lam, 2020), and regression analysis (Fragkakis et al., 2015; Son et al., 2013). The research targets include buildings (García de Soto et al., 2014; Idowu & Lam, 2020), bridges (Beljkaš et al., 2020; Kovačević et al., 2023), and civil infrastructures (Antoniou et al., 2023; Fragkakis et al., 2015).…”

“…Category 3: The CMQ can be estimated using AI‐based or data‐driven methods (Antoniou et al., 2023; Idowu & Lam, 2020; Kovačević et al., 2023), which allows a good balance to be achieved between accuracy and efficiency. They have been widely used as surrogate models to evaluate the objective functions of evolutionary algorithms (Chugh et al., 2019; Jin et al., 2019).…”

“…The fundamental paradigm of data‐driven CMQ estimation methods lies in three aspects (Antoniou et al., 2023; Beljkaš et al., 2020; Dimitriou et al., 2018; Fragkakis et al., 2015; García de Soto et al., 2014; Idowu & Lam, 2020; Kovačević et al., 2023; Singh, 1991; Son et al., 2013; Yeh, 1998). (1) Real‐world engineering cases are collected, and CMQs are used as label data.…”

Machine-Learning-Based Consumption Estimation of Prestressed Steel for Prestressed Concrete Bridge Construction

“…In addition, the availability of information about the resources needed for the construction of bridges in the initial stages of the project enables the entire project to be seen more comprehensively to provide initial construction cost estimates for funding acquisition purposes and also from the aspect of maintenance through the analysis of the whole life cycle of the project. Recent thorough reviews of the literature and content evaluations of construction cost prediction models by Tayefeh Hashemi et al (2020) and Antoniou et al (2023) have shown that numerous models offering cost estimates and/or material consumption estimates at various design stages have been provided by researchers, mostly for buildings, but also for power generation or network construction, road construction, rail and road tunnels, and bridges [5,6].…”