scholarly journals Using Intelligent Techniques in Construction Project Cost Estimation: 10-Year Survey

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Abdelrahman Osman Elfaki ◽  
Saleh Alatawi ◽  
Eyad Abushandi
Keyword(s):  
Cost Estimation ◽  
Construction Management ◽  
Research Effort ◽  
Construction Project ◽  
Construction Cost ◽  
Future Directions ◽  
Intelligent Technique ◽  
Research Gap ◽  
Construction Project Cost ◽  
Optimal Construction

Cost estimation is the most important preliminary process in any construction project. Therefore, construction cost estimation has the lion’s share of the research effort in construction management. In this paper, we have analysed and studied proposals for construction cost estimation for the last 10 years. To implement this survey, we have proposed and applied a methodology that consists of two parts. The first part concerns data collection, for which we have chosen special journals as sources for the surveyed proposals. The second part concerns the analysis of the proposals. To analyse each proposal, the following four questions have been set. Which intelligent technique is used? How have data been collected? How are the results validated? And which construction cost estimation factors have been used? From the results of this survey, two main contributions have been produced. The first contribution is the defining of the research gap in this area, which has not been fully covered by previous proposals of construction cost estimation. The second contribution of this survey is the proposal and highlighting of future directions for forthcoming proposals, aimed ultimately at finding the optimal construction cost estimation. Moreover, we consider the second part of our methodology as one of our contributions in this paper. This methodology has been proposed as a standard benchmark for construction cost estimation proposals.

scholarly journals Comparative Cost Estimation of Residential Building

2021 ◽  
Vol 9 (8) ◽  
pp. 2735-2739
Author(s):  
Prof. Amit Kale
Keyword(s):  
Construction Industry ◽  
Cost Estimation ◽  
Construction Management ◽  
Research Effort ◽  
Construction Project ◽  
Residential Building ◽  
Construction Cost ◽  
Building Construction ◽  
Estimation Methods ◽  
Comparative Cost

Abstract: A construction project of any building is mainly based on 3 important steps that are planning, Cost Estimation of the building and proper execution of construction of the building. Construction cost estimation has the lion’s share of the research effort in construction management. The Objective is to analyze the effectiveness of various cost estimation methods by comparing traditional and various online websites. This study will provide more accurate estimates that save time and minimize errors. The research conducted will be helpful for estimation of construction, also proving how the introduction of IT sector in construction industry is turning out to be beneficial. Keywords: Estimation, Construction Management, Online Websites, minimize errors

scholarly journals Application of Project Whole Process Cost Control in Construction Project Cost Audit

2021 ◽  
pp. 240-246
Author(s):  
Weiying Wu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Cost Estimation ◽  
Construction Project ◽  
Construction Cost ◽  
Grey Theory ◽  
Project Cost ◽  
Neural Network Method ◽  
Network Method ◽  
Construction Project Cost

This paper summarizes the theory of project cost, which paves the way for the basic theoretical system of construction project cost estimation. This paper expounds the function of project cost and the main factors affecting project cost. Secondly, the basic principles of BP neural network method and grey theory are described, which provides technical support for the establishment of construction cost estimation system model. In view of the shortcomings of BP neural network, such as slow convergence speed, easy to fall into local minimum and inaccurate prediction, this paper proposes an improved method to process the data of BP neural network input layer with grey one-time accumulation, and then use grey one-time subtraction to process the output layer. Finally, the optimization model based on grey BP neural network method is established to establish a more accurate knowledge framework system in order to solve the construction cost estimation.

scholarly journals Estimasi Biaya Konseptual Pada Jembatan Beton Bertulang Dengan Metode Indek Biaya

2017 ◽  
Vol 13 (2) ◽  
pp. 105
Author(s):  
Bagyo Mulyono ◽  
Paulus Setyo Nugroho
Keyword(s):  
Cost Estimation ◽  
Early Stage ◽  
Cost Model ◽  
Construction Project ◽  
Public Works ◽  
Cost Index ◽  
Reinforced Concrete Bridge ◽  
Construction Project Cost ◽  
The Cost ◽  
Available Information

<p class="DRAbstrak">Cost estimation is the art of estimating the amount of cost required for an activity based on available information. The conceptual cost estimate is an early stage in planning a construction project. This estimate provides the cost that must be budgeted for a construction project. Cost conceptual estimates have low accuracy because the time of calculation and available information is limited. This study aims to obtain a conceptual model of the conceptual cost of short-spaced bridges. The method used is the cost index. The cost index is a figure indicating the cost per m2 of bridges at a given time. The required data are contract documents and drawings design that are built in 2012 - 2015 in Banyumas residency area. Span of bridge 4 - 38.8 meters and width of bridge 2 - 7 meters with caisson  foundation. The data were obtained from Dinas Bina Marga and Public Works Agency. The results showed that the conceptual cost model of reinforced concrete bridge with caisson foundation was BJiL = (100.540.56t2-404.528.636,58t + 406.914.286.088,58) x P x W, with t = year, P = span bridge, and W = bridge width. The error value of validation of this model is 2.31%.</p>

scholarly journals Assessment of Construction Project Cost Estimating Accuracy in Egypt

2021 ◽  
Vol 15 (1) ◽  
pp. 290-298
Author(s):  
Ahmed H. Ibrahim ◽  
Lamiaa M. Elshwadfy
Keyword(s):  
Construction Project ◽  
Relative Weight ◽  
Control Process ◽  
Construction Cost ◽  
Cost Estimate ◽  
Assessment Model ◽  
Success Factor ◽  
Cost Estimating ◽  
Construction Project Cost ◽  
The Cost

Background: The accuracy of the cost estimate is a key success factor for any construction project. It is the base for an effective tendering process. It can also be considered as the cornerstone of the cost control process. Objective: This paper aims to develop a model that can be used to assess the expected cost estimating accuracy of construction projects. This model is named as Construction Cost Estimate Accuracy Index (CCEAI). Methods: A questionnaire survey that contains fifteen factors clustered into four categories was carried out among 90 experts based on the construction cost estimate. Only sixty-six questionnaires were returned. The Analytical Hierarchy Process (AHP) was used to identify the relative weights of the different cost estimates. Results: The questionnaire results were analyzed using the AHP technique to calculate the relative weight for each of the input factors and categories. A Construction Cost Estimating Accuracy Assessment model (CCEAI) was developed based on the calculated relative weights. Then, three projects were used as case study applications to check the validity of the proposed model. The results showed that the CCEAI model is greatly reliable in predicting the expected accuracy of the cost estimate. Conclusion: The results of this research and the developed model are very important and can be considered as a powerful tool to predict and improve the expected accuracy of any future construction cost estimate.

scholarly journals An Algorithm for Construction Project Cost Forecast Based on Particle Swarm Optimization-Guided BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Dan Ye
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Bp Neural Network ◽  
Construction Project ◽  
Particle Swarm ◽  
Construction Cost ◽  
Cost Prediction ◽  
Swarm Optimization ◽  
Project Cost ◽  
Construction Project Cost

Construction project cost prediction is an important function in construction-related fields; it can provide an important basis for project feasibility study and design scheme comparison and selection, and its accuracy will directly affect the investment decision of the project. The successful realization of construction cost prediction can bring great convenience to the control and management of construction cost. The purpose of this paper is to study a fast, accurate, convenient, deducible, and rational construction project cost prediction method, to provide a basis for the cost management of the whole life cycle of the project. Therefore, this paper uses particle swarm optimization algorithm to improve BP neural network and proposes a novel construction project cost prediction algorithm based on particle swarm optimization-guided BP neural network. Aiming at the defects of BP neural network updating weights and thresholds with the gradient descent method, this paper uses the advantages of particle swarm optimization in the field of parameter optimization to optimize BP neural network with PSO algorithm. The structure of BP neural network weights and the threshold of each neuron in the coding, through intelligent search for each particle, find the most suitable weights and thresholds, so that the BP neural network has faster convergence speed, better generalization ability, and higher prediction precision. Simulation results also show that the proposed algorithm is competitive enough.

Construction engineering cost estimation based on artificial intelligence technology

2021 ◽  
pp. 1-9
Author(s):  
Weiying Wu ◽  
Hui Huang
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Cost Estimation ◽  
Construction Project ◽  
Basic Function ◽  
Project Cost ◽  
Rbf Network ◽  
Artificial Intelligence Technology ◽  
Construction Project Cost ◽  
Engineering Cost

The determination of construction project cost is one of the important contents of construction project management, but the estimation of construction project cost generally has the disadvantages of large errors and long preparation time. With the continuous development of computer science, artificial intelligence theory is one of the hot research topics. The purpose of this article is to study the construction cost estimation based on artificial intelligence technology. Based on the theoretical basis of artificial neural network, genetic algorithm, and engineering cost, this paper proposes an optimized radial basic function (RBF) model based on genetic algorithm (GA). The search feature combines the width, center, and hidden layer weights of the RBF network with genetic algorithms to self-correct, thereby greatly improving the accuracy of the model calculation results. In this paper, according to the model’s error (actual output-expected output), the four test samples were tested separately, and the error values obtained were 0.0125, 0.1009, –0.0791, and 0.0514. This shows the accuracy of the experimental results of the model [R] higher.

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