Forecasting Construction Cost Using Artificial Neural Network for Road Projects in the Department of Public Works and Highways Region XI (original) (raw)
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Indian Journal of Science and Technology, 2017
Objectives: The main purpose of this study is to introduce modern technique to using artificial neural network for predicting the cost of structure works for highway project at the feasible study phase. Methods: Multi-layer perceptron trainings utilized back-propagation algorithm was used. In this study, the feasibility of ANNs approach for modeling these cost characters was inspected. A lot of problem in relation to ANNs construction such as internal parameters and the effect of ANNs geometry on the performance of ANNs models were inspected. Information on the relative importance of the variable's affecting on the cost parameters predictions was given and mathematical equations in order to estimating the cost of structure works for highway project were determined. Findings: One model was developed for the prediction the structure works cost of highway project. Data and information utilized in this model was collected from Stat Commission for Roads and Bridges in republic of Iraq. ANNs model have the ability to predict the cost for structure works for highway project with very good degree of accuracy equal to 93.19% and the coefficient of correlation (R) was 90.026%, Applications: Neural network has shows to be a promising approach for use in the initial phase of highway projects when typically only a limited or minus data and incompleted information set is ready for cost analysis.
USING THE ARTIFICIAL NEURAL NETWORKS FOR PRDICTING THE TOTAL COST OF HIGHWAY PROJECTS IN IRAQ
The main objective of this research is to introduce a new and alternative approach of using a neural network for cost estimation of the highway project at the early stage. The application of Artificial Neural Networks, as a modern technique, in Iraqi construction industry is necessary to ensure successful management, and many of the construction companies feel the need of such system in project management. One model was built for the prediction the total cost of highway project. The data used in this model was collected from State Commission for Roads and Bridges in Iraq. Multi-layer perceptron trainings using the back-propagation algorithm were used. It was found that Artificial Neural Networks (ANNs) have the ability to predict the total cost of highway project with a good degree of accuracy of the coefficient of correlation (R) was 84.95%, and average accuracy percentage of 90.7%.The ANNs model developed to study the impact of the internal network parameters on model performance indicated that ANNs performance was relatively insensitive to the number of hidden layer nodes, momentum term, and learning rate.
Time and Cost Analysis for Highway Road Construction Project Using Artificial Neural Networks
Journal of Construction Engineering and Project Management, 2015
Success of the construction companies is based on the successful completion of projects within the agreed cost and time limits. Artificial neural networks (ANN) have recently attracted much attention because of their ability to solve the qualitative and quantitative problems faced in the construction industry. For the estimation of cost and duration different ANN models were developed. The database consists of data collected from completed projects. The same data is normalised and used as inputs and targets for developing ANN models. The models are trained, tested and validated using MATLAB R2013a Software. The results obtained are the ANN predicted outputs which are compared with the actual data, from which deviation is calculated. For this purpose, two successfully completed highway road projects are considered. The Nftool (Neural network fitting tool) and Nntool (Neural network/ Data Manager) approaches are used in this study. Using Nftool with trainlm as training function and Nntool with trainbr as the training function, both the Projects A and B have been carried out. Statistical analysis is carried out for the developed models. The application of neural networks when forming a preliminary estimate, would reduce the time and cost of data processing. It helps the contractor to take the decision much easier.
Neural network models for actual cost prediction in Greek public highway projects
International Journal of Project Organisation and Management, 2019
Selected public Greek highway projects are examined in order to produce models to predict their actual construction cost based on data available at the bidding stage. Twenty highway projects, constructed in Greece, with similar type of available data were examined. Considering each project's attributes and the actual cost, correlation analysis is implemented, with the aid of SPSS. Correlation analysis identified the most predictive project variables. Additionally, the WEKA application, through its attribute selection function, highlighted the most efficient subset of variables. These selected variables through correlation analysis and WEKA are used as input neurons for neural network models. FANN Tool is used to construct neural network models. The optimum neural network model produced a mean squared error with a value of 7.68544E-05 and was based on budgeted cost, lowest awarding bid, technical work cost and electromechanical work cost.
Predicting the Final Cost of Iraqi Construction Project Using Artificial Neural Network (ANN)
Indian Journal of Science and Technology, 2019
Objectives: It is very hard estimated the budget of construction projects at the first step of the building because of the limited data about the project at this step. Developing a mathematical equation to estimate the budget of the Iraqi construction project at initial step is the aim of this study. Method: It involves using of Artificial Neural Network (ANN) to develop the mathematical equation. The researchers collected the information about cost for 501 sets project for the duration (2005-2015). The total costs of 25 activity of construction work such as (excavation the foundation works, Landfill works, filling with sub-base works, Construction works under moisture proof layer, Construction works above moisture proof layer, Construction works of sections, ordinary concrete for walkways, reinforced concrete foundation, reinforced concrete column, reinforced concrete lintel, reinforced concrete slabs, reinforced concrete beams, reinforced concrete stair, reinforced concrete for the sun bumper, plaster finishing works, cement finishing works, Plastic Paints, Pentellite paints, pigment color, Stone packaging, Works of placing marble, Ceramic works for floor, Ceramic works for walls, Flattening (two opposite layers of lime), Flattening (Tiling)) are utilized for cost prediction. Findings: The results of the correlation factor equal to (100%), the percentage of error equal (5.81%) and amount of precision was (94.19%) which indicated that the artificial neural network gives very good performance in prediction construction cost. Applications: ANN is proved useful in estimating the costs of construction well in advance especially when the data are incomplete or limited.
Cost estimation of highway projects in developing countries: Artificial neural network approach
2005
Abstract: Cost estimation of highway projects with high accuracy at the conceptual phase of project development is crucial for planning and feasibility studies. However, a number of difficulties arise when conducting cost estimation during the conceptual phase. Major problems faced are lack of preliminary information, lack of database of road works costs, data missingness, lack of an appropriate cost estimation methods, and the involvement of uncertainties.
Conceptual Cost Estimate of Libyan Highway Projects Using Artificial Neural Network
It is well known that decisions at early stages of a construction project have great impact on subsequent project performance. Conceptual cost estimate is a challenging task that is done with limited information at the early stages of a project life where many factors affecting the project costs are still unknown. The objective of this paper is to support decision makers in predicting the conceptual cost of highway construction projects in Libya. Initially, the factors that significantly influence highway construction are identified. Then, an artificial neural network model is developed for predicting the cost. The network is trained and tested with a total of 67 projects historical data. Training of the model is administered via back-propagation algorithm. The model is coded ad implemented using MATLAB® to facilitate its use. An optimization module is also added to the Neural Network model with the objective of minimizing the error of the predicted cost. The model is then validated and the results show better predictions of conceptual cost of highway projects in Libya.
Estimating the Optimum Duration of Road Projects Using Neural Network Model
International Journal of Engineering and Technology, 2017
The aim of this study to predictedthe duration of road projects in republic of Iraq. Historical data was adopted for (99) projects for interval between 2000 to 2017 from Roads and Bridges Directorate (RBD). Artificial Neural Network (ANN)model used to estimate the duration usingsix variables (length of road, No.of lane, No.of intersection, volume of earth, type of pavement and furniture level). The methodology used in this study included two important parts, the first part, reviewing the literature of the subject (estimating the duration of the road projects), and the second part, used of a program neuframe v.4 to build the models of neural networks to estimate the duration of road project. The results showed strong correlation between actual duration and predict duration by (90.6%), minimizes testing error (3.2%) and training error (4.9%). The MAPE and Average Accuracy Percentage generated by ANN model were found to be(25.73 %) and(74.27%) respectively. Therefore, it can be concluded that ANNs model show very good agreement with the actual measurements.
Construction Cost Estimation for Government Building Using Artificial Neural Network Technique
2021
The construction bidding competition required effective precision to prevent losses in the bidding process, especially in the public sector. The bidders must have an estimate of the construction cost before the bidding. There are two widely used methods for construction cost estimation: 1) a rough estimation with an advantage of quick construction estimation cost and a disadvantage of a high price tolerance, and 2) a detailed estimation with an advantage of more accurate estimation of construction costs, and disadvantages of the need for a complete construction plan and time-consuming. Considering these disadvantages, research on the government construction cost estimation model was conducted by using the Artificial Neural Network (ANN) technique of forecasting modeling. The study's results showed that the model consisted of two hidden layers which each layer has ten and eight nodes, respectively, with the best Root Mean Square Error (RMSE) value ± 0.331 million Baht. When the new data set was tested for validity, the R 2 equal to 0.914 proving the accuracy of the forecasting model as an alternative for government bidding participants to reduce the tolerances and to spend less time to estimate construction costs more efficiently.
The main objective of this research is to introduce a new and alternative approach of using a neural network for cost estimation of the expressway project at the early stage. A preliminary literature survey and data collection have identified the problem and led to the formulation of the research hypothesis that there is a weakness in estimating the cost of the expressway construction projects because the current available techniques are poor and suffer some disadvantages such as being traditional, aged, slow and uncertain. Besides, the need for a modern efficient construction cost estimation techniques that have more advantages such as being modern, fast, accurate, flexible and easy to use is of value. Also, the application of Artificial Neural Networks, as a modern technique, in Iraqi construction industry is necessary to ensure successful management, and many of the construction companies feel the need of such system in project management