Construction costs forecasting: comparison of the accuracy of linear regression and support vector machine models

Petruseva, Silvana; Žileska-Pančovska, Valentina; Žujo, Vahida; Brkan-Vejzović, Aida

doi:10.17559/tv-20150116001543

Cited by 7 publications

(7 citation statements)

References 17 publications

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“…In addition, they require less memory space, are easy to use, and are generally less expensive. On the other hand, ANN-based models generally have a better level of accuracy in both regression and classification problems [27]. In addition, ANNs are preferable when the data does not adhere to loworder polynomial forms [28].…”

Section: Literature Analysismentioning

confidence: 99%

Estimating the Renovation Cost of Water, Sewage, and Gas Pipeline Networks: Multiple Regression Analysis to the Appraisal of a Reliable Cost Estimator for Urban Regeneration Works

De Mare,

Dolores,

Macchiaroli

2023

Buildings

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Water, sewerage, and gas infrastructures play a crucial role in optimising the housing quality of buildings and cities. On the other hand, water, sewer, and gas pipelines constantly need maintenance, checks, and repairs. These interventions require large budgets, and therefore scrupulous investment planning is necessary. In this study, Multiple Regression Analysis (MRA) is applied to estimate the urban renovation costs related to the works on water, sewage, and gas networks. The goal is to build a reliable cost estimator that is easy to apply and has a minimum number of explanatory variables. Four regressive models are tested: linear, linear-logarithmic, logarithmic-linear, and exponential. The analysis is implemented on two datasets of projects carried out in Italy: the first collects the data of 19 projects made in historical centres, while the second collects the data of 20 projects made in the peripheries. The variables that impact costs the most are selected. In terms of results, the estimated functions return an average error of 1.25% for historical centres and 1.00% for peripheral areas. The application shows that a differentiation of cost functions based on the urban context is relevant. Specifically, two different functions are detected: exponential for historical centres and linear for peripheral areas. In conclusion, we interpret that the exponential growth of costs in historical centres depends on a series of critical issues (logistical, architectural, etc.), present to a lesser extent in the peripheries, which complicate the execution of the interventions. The approach adopted, which led to the detection of cost functions differentiated based on the urban context, allows us to benefit from more accurate modelling that considers the places’ specificities.

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Section: Literature Analysismentioning

confidence: 99%

Estimating the Renovation Cost of Water, Sewage, and Gas Pipeline Networks: Multiple Regression Analysis to the Appraisal of a Reliable Cost Estimator for Urban Regeneration Works

De Mare,

Dolores,

Macchiaroli

2023

Buildings

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“…In addition, they are effective due to a well-defined mathematical expression, as well as the ability to explain the significance of each variable and the relationship between independent variables (Sodikov, 2005(Sodikov, , 2009. When there is only one predictor variable it is called simple linear regression (Meharie et al, 2022), and when there are several predictors, it is referred to as multiple linear regression (Petruseva et al, 2017;Meharie et al, 2022). Regression model equations can be expressed as follows (Kim et al, 2004;Sodikov, 2009):…”

Section: Cost Estimation Of Highway Bid Itemsmentioning

confidence: 99%

“…When there is only one predictor variable it is called simple linear regression (Meharie et al. , 2022), and when there are several predictors, it is referred to as multiple linear regression (Petruseva et al ., 2017; Meharie et al. , 2022).…”

Section: Introductionmentioning

confidence: 99%

Conceptual cost estimation of highway bid items – A systematic literature review

Awuku

Asa

Baffoe-Twum

et al. 2022

ECAM

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PurposeChallenges associated with ensuring the accuracy and reliability of cost estimation of highway construction bid items are of significant interest to state highway transportation agencies. Even with the existing research undertaken on the subject, the problem of inaccurate estimation of highway bid items still exists. This paper aims to assess the accuracy of the cost estimation methods employed in the selected studies to provide insights into how well they perform empirically. Additionally, this research seeks to identify, synthesize and assess the impact of the factors affecting highway unit prices because they affect the total cost of highway construction costs.Design/methodology/approachThis paper systematically searched, selected and reviewed 105 papers from Scopus, Google Scholar, American Society of Civil Engineers (ASCE), Transportation Research Board (TRB) and Science Direct (SD) on conceptual cost estimation of highway bid items. This study used content and nonparametric statistical analyses to determine research trends, identify, categorize the factors influencing highway unit prices and assess the combined performance of conceptual cost prediction models.FindingsFindings from the trend analysis showed that between 1983 and 2019 North America, Asia, Europe and the Middle East contributed the most to improving highway cost estimation research. Aggregating the quantitative results and weighting the findings using each study's sample size revealed that the average error between the actual and the estimated project costs of Monte-Carlo simulation models (5.49%) performed better compared to the Bayesian model (5.95%), support vector machines (6.03%), case-based reasoning (11.69%), artificial neural networks (12.62%) and regression models (13.96%). This paper identified 41 factors and was grouped into three categories, namely: (1) factors relating to project characteristics; (2) organizational factors and (3) estimate factors based on the common classification used in the selected papers. The mean ranking analysis showed that most of the selected papers used project-specific factors more when estimating highway construction bid items than the other factors.Originality/valueThis paper contributes to the body of knowledge by analyzing and comparing the performance of highway cost estimation models, identifying and categorizing a comprehensive list of cost drivers to stimulate future studies in improving highway construction cost estimates.

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“…Linear regression is a method for modeling the relationship between one or more independent variables and one dependent variable (Petruseva et al , 2017). When there is only one independent variable and the relationship is linear it is called simple linear regression.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The developed model was able to ECAM 29,7 predict the cost road construction project with 95% prediction accuracy by using seventy project cases. Petruseva et al (2017) applied and compared two predictive models: MLR and SVM model using 75 datasets to forecast the cost of construction projects. Comparison of the model results showed that the SVM forecast was substantially more reliable than the MLR.…”

Section: Support Vector Machinesmentioning

confidence: 99%

Application of stacking ensemble machine learning algorithm in predicting the cost of highway construction projects

Meharie

Mengesha

Gariy

et al. 2021

ECAM

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PurposeThe purpose of this study to apply stacking ensemble machine learning algorithm for predicting the cost of highway construction projects.Design/methodology/approachThe proposed stacking ensemble model was developed by combining three distinct base predictive models automatically and optimally: linear regression, support vector machine and artificial neural network models using gradient boosting algorithm as meta-regressor.FindingsThe findings reveal that the proposed model predicted the final project cost with a very small prediction error value. This implies that the difference between predicted and actual cost was quite small. A comparison of the results of the models revealed that in all performance metrics, the stacking ensemble model outperforms the sole ones. The stacking ensemble cost model produces 86.8, 87.8 and 5.6 percent more accurate results than linear regression, vector machine support, and neural network models, respectively, based on the root mean square error values.Research limitations/implicationsThe study shows how stacking ensemble machine learning algorithm applies to predict the cost of construction projects. The estimators or practitioners can use the new model as an effectual and reliable tool for predicting the cost of Ethiopian highway construction projects at the preliminary stage.Originality/valueThe study provides insight into the machine learning algorithm application in forecasting the cost of future highway construction projects in Ethiopia.

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Construction costs forecasting: comparison of the accuracy of linear regression and support vector machine models

Cited by 7 publications

References 17 publications

Estimating the Renovation Cost of Water, Sewage, and Gas Pipeline Networks: Multiple Regression Analysis to the Appraisal of a Reliable Cost Estimator for Urban Regeneration Works

Estimating the Renovation Cost of Water, Sewage, and Gas Pipeline Networks: Multiple Regression Analysis to the Appraisal of a Reliable Cost Estimator for Urban Regeneration Works

Conceptual cost estimation of highway bid items – A systematic literature review

Application of stacking ensemble machine learning algorithm in predicting the cost of highway construction projects

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