Cost Prediction for Roads Construction using Machine Learning Models (original) (raw)
Related papers
2020
Inaccurate cost estimates have substantial effects on the final cost of construction projects and erode profits. Cost estimation at conceptual phase is a challenge as inadequate information is available. For this purpose, approaches for cost estimation have been explored thoroughly, however they are not employed extensively in practice. The main goal of this paper is to comparing the performance of various models in predicting the cost of construction projects at early conceptual phase in the project development. In this study, on the basis of the actual project data, three modeling algorithms such as random forest, support vector machine and artificial neural networks are used to forecast the construction cost of Ethiopian highway projects. The three models were then compared based on the outcomes of prediction and root mean square error. The findings revealed that random forest outperforms neural network and support vector machine in realizing better prediction accuracy. Based on ...
Machine Learning Modeling of Forest Road Construction Costs
Forests, 2021
The economics of the forestry enterprise are largely measured by their performance in road construction and management. The construction of forest roads requires tremendous capital outlays and usually constitutes a major component of the construction industry. The availability of cost estimation models assisting in the early stages of a project would therefore be of great help for timely costing of alternatives and more economical solutions. This study describes the development and application of such cost estimation models. First, the main cost elements and variables affecting total construction costs were determined for which the real-world data were derived from the project bids and an analysis of 300 segments of a three kilometer road constructed in the Hyrcanian Forests of Iran. Then, five state-of-the-art machine learning methods, i.e., linear regression (LR), K-Star, multilayer perceptron neural network (MLP), support vector machine (SVM), and Instance-based learning (IBL) we...
Road Construction Cost Prediction Models Based on Regression Analysis
The ability to predict the final cost of construction projects based on limited initial input data could be a very valuable tool for every project manager and/or construction enterprise. This paper focuses on the models of Trefor P. Williams and their application in Greek road construction projects. An overview and description of each model is provided and also their performance is assessed. These models can predict with satisfactory precision the cost at completion of road construction projects based on initial tender offers. The study applies these models in 28 selected highway construction project cases conducted in central and northern Greece and discusses their performance. The analysis of the models is taking place in various groups of sample projects, based on projects’ budgets and geographical locations.
Model for predicting cost of rural road projects in Thailand
IOP Conference Series: Materials Science and Engineering, 2019
Nowadays, construction cost plays an essential role in various projects, which are buildings, roads, railways, and bridges projects. Conceptual cost estimation in feasibility study is require high accuracy and less validation error especially in construction projects at early stages. The more improvement of estimation techniques, it would lead to the lesser problems of cost overrun, and extra expenses. This paper developed a cost estimation model for new constructed rural road projects. Considering the estimation methods for predicting the cost model, parametric method based on regression learner and NN method are applied. Previously, many researchers studied the cost applicable model by using various computer applications, so that this paper differed to compare these methods based on MATLAB. Accordingly, 44 road projects were compiled from DRR database, after that identifying the effective cost parameters referred on collected data. Subsequently, the data implementation process was...
Government Construction Project Budget Prediction Using Machine Learning
Journal of Advances in Information Technology, 2022
The construction industry could not avoid the technology disruptive era. Therefore, the Thai government has created a new policy and directed all departments to implement big data technology. Big data technology includes Machine Learning (ML). The present study attempts to predict over-budget construction projects using an ML algorithm. Data were collected from the comptroller general's department of Thailand for over-budget project cases. Information about 692 projects completed in Thailand in 2019, covering all types of construction projects, was collected and analyzed. ML, an analytical technique for big data technology, was used as a tool in this study. In addition, k-Nearest Neighbors (KNN), an ML algorithm, was used to classify over-budget projects. The input data have four attributes: department of project, construction site location, type of project, and methods of procurement; the output is a yes/no decision on whether a project has been over budget. The dataset was preprocessed for analysis and modeled using the KNN function in Python 3. According to the test results, the KNN model achieves an accuracy (precision) of 0.86. Finally, the developed model has demonstrated that it can be used to predict the over-budget construction projects for the Thai government.
Kathford Journal of Engineering and Management
Construction cost estimation is crucial to a project's success, but because of the many variables that impact it, it is challenging to make an accurate prediction. Traditional methods are being used for preliminary cost estimation in the construction industry of Nepal. There still exists the problem of cost overrun, and time delay due to incorrect cost budgeting. This study aims to analyze a modern method of preliminary cost estimation in Nepal to prove its efficiency over the traditional method. In this work models such as Linear Regressor, Decision Tree Method, Random Forest method, Artificial Neural Networks, Support Vector Machine, Boost method, Extra tree method, Voting Regression, and Stacking method are used. Regarding the datasets, the buildings that were used are Educational Building, Commercial Building, Hospital Building, Residential Building, Public Building, Official Building, and Hotel Building having 0 to 2 basements ranging above 1 crore. The input features were taken from the literature review, and validated by expert opinion, and after successfully conducting pilot testing, the survey questionnaire was distributed among contractors and consultants. Data preprocessing was done and training and testing data sets were developed. The model was developed for nine algorithms. Mean absolute error (MAE), Mean square error (MSE), Root mean square error (RMSE), and R square value are used as evaluation metrics. In the evaluation of various regression models, three stand out as the most promising for predicting the target variable. The Decision Tree model exhibited remarkable performance with an MSE of 0.088575, an MAE of 0.104625, an RMSE of 0.297615, and an R 2 of 0.876170. Similarly, the Extra Tree model closely followed with an MSE of 0.088601, an MAE of 0.102909, an RMSE of 0.297659, and an R 2 of 0.876134. The Voting Model with an MSE of 0.105035, an MAE of 0.222807, an RMSE of 0.324091, and an R 2 of 0.853159. This study also opens the path for the exploration of other models and motivate to follow the trends of machine learning in the present era.
A taxonomy of machine learning techniques for construction cost estimation
Innovative Infrastructure Solutions, 2024
Construction projects require significant funding and are exposed to several risks. Public construction projects require a major proportion of the annual government budget. Their actual cost estimation concerns a known and existing problem for the construction sector, while several project failures in terms of budget extension can be documented around the world. Accurate construction cost predictions are essential in mitigating time-related risks and play a crucial role in the decision-making process for managers. Inaccurate cost estimations can result in investment project disruptions. Research about machine learning (ML) techniques regarding construction cost estimation is intensifying, which aims to develop new ML techniques or update existing ones. This article contains a systematic literature review of ML techniques for construction project cost estimation. This review included an in-depth analysis of 219 studies, which contain the most prominent machine learning techniques. This article attempts to define a classification of the identified ML techniques, with the following criteria: intelligent technique that was followed and the application domain. The taxonomy that was generated contains ML techniques about construction cost estimation and their application, which offers useful guidance for both researchers and practitioners.
Early cost estimating for road construction projects using multiple regression techniques
Australasian Journal of Construction Economics and Building, 2011
The objective of this study is to develop early cost estimating models for road construction projects using multiple regression techniques, based on 131 sets of data collected in the West Bank in Palestine. As the cost estimates are required at early stages of a project, considerations were given to the fact that the input data for the required regression model could be easily extracted from sketches or scope definition of the project. 11 regression models are developed to estimate the total cost of road construction project in US dollar; 5 of them include bid quantities as input variables and 6 include road length and road width. The coefficient of determination r2 for the developed models is ranging from 0.92 to 0.98 which indicate that the predicted values from a forecast models fit with the real-life data. The values of the mean absolute percentage error (MAPE) of the developed regression models are ranging from 13% to 31%, the results compare favorably with past researches whic...
Prediction Model for the Cost of Road Rehabilitation and Reconstruction Works
Journal of Construction Engineering and Management, 2014
This paper presents the development of prediction models for the unit costs of road works that could be applied to strategic planning of road works at the network level. A specialized data set was used, which was generated under a World Bank study that included 200 road work contracts from 14 countries in Europe and Central Asia (ECA) and signed between 2000 and 2010. Two techniques were used for model development: multiple regression analysis and artificial neural networks. Classification trees were used as an intermediate step to evaluate the correctness of the selected parameters. A total of 19 variables, divided into three groups (oil-price related, country-related, and project-related variables), were tested for their influence on unit cost of asphalt concrete (AC) and road rehabilitation and reconstruction (RRR) costs. The analysis results showed that the level of corruption and the economic environment in a country have a significant effect on both costs of AC and RRR. The resulting models could be particularly useful for the planning and optimization of work on road networks in ECA countries. However, the approach and methodology used for model developments may be applied generally.
Modern Management based on Big Data III
The development of roads has been one of the nation’s most essential infrastructural initiatives. It is an essential mode of transportation that plays an important role in our everyday lives. Because of its importance, the government has allotted large budgets in making roads in different parts of the country. The quantity and complexity of road construction projects have substantially expanded in recent years. Numerous novel methods and technology have been developed to facilitate road construction budgeting, planning, and decision-making. Using Artificial Neural Network (ANN), this study constructed a forecasting model to accurately anticipate the future costs of road improvements. Between 2017 and 2020, fifty (50) completed road projects from the Department of Public Works and Highways (DPWH) Regional Office XI were utilized by the researcher. The DPWH RO XI is one of the country’s largest implementing offices for constructing public roads catering the entire Davao Region. This r...