Important Factors for Construction Project Cost Estimating Using ANN

Cost estimation has its proven importance as one of essential factors for project success. The aim of this research is to predict the early project cost using neural network. Early project cost represents a key component in business unit decisions. The most important factors influencing on the parametric cost estimation in construction building projects in Gaza Strip were defined and investigated. A questionnaire survey and relative index ranking technique were used to conclude the most important factors. Fourteen most effective factors were identified. One hundred and six case studies from real executed construction project in Gaza Strip were collected for training and testing the model. The cases were prepared to be used in cost estimate neural networks model. Eighty percent of case studies were used to train and test the model. The remaining 20% was used for model verification. The results revealed the ability to the model to predict cost estimate to an acceptable degree of accuracy. The minimum squares error with 0.005 in training stage and 0.021 in testing stage were recorded.

Whole-Process Project Cost Management Based on Building Information Modeling (BIM)

The whole-process project cost management based on building information modeling(BIM)is a new management method,aiming to realize the comprehensive optimization and improvement of project cost management through the application of BIM technology.This paper summarizes and analyzes the whole-process project cost management based on BIM,aiming to explore its application and development prospects in the construction industry.Firstly,this paper introduces the role and advantages of BIM technology in engineering cost management,including information integration,data sharing,and collaborative work.Secondly,the paper analyzes the key technologies and methods of the whole-process project cost management based on BIM,including model construction,data management,and cost control.In addition,the paper also discusses the challenges and limitations of the whole-process BIM project cost management,such as the inconsistency of technical standards,personnel training,and consciousness change.Finally,the paper summarizes the advantages and development prospects of the whole-process project cost management based on BIM and puts forward the direction and suggestions for future research.Through the research of this paper,it can provide a reference for construction cost management and promote innovation and development in the construction industry.

Support Vector Machine Cost Estimation Model for Road Projects

A cost estimate is one of the most important steps in road project management. There are ranges of factors that mostly affect the final project cost. Many approaches were used to estimate project cost, which took into consideration probable project performance and risks. The aim is to improve the ability of construction managers to predict a parametric cost estimate for road projects using SVM （support vector machine）. The work is based on collecting historical road executed cases. The 12 factors were identified to be the most important factors affecting the cost-estimating model. A total of 70 case studies from historical data were divided randomly into three sets： training set includes 60 cases, cross validation set includes three cases and testing set includes seven cases. The built model was successfully able to predict project cost to the AP （accuracy performance） of 95%.

The Calculation Model for Operation Cost of Coal Resources Development Based on ANN

On the basis of analysis and selection of factors influencing operation cost of coal resources development, fuzzy set method and artificial neural network (ANN) were adopted to set up the classification analysis model of coal resources. The collected samples were classified by using this model. Meanwhile, the pattern recognition model for classifying of the coal resources was built according to the factors influencing operation cost. Based on the results achieved above, in the light of the theory of information diffusion, the calculation model for operation cost of coal resources development has been presented and applied in practice, showing that these models are reasonable.

Control and Management Measures of Highway Project Cost under the PPP Model

In recent years,China’s society and economy has been developing very rapidly,and various industries are showing a trend of vigorous development.The importance of control and management of project cost in highway project management is becoming more and more prominent.PPP model has been widely used in the control and management of highway project cost with its own unique advantages.The author analyzes the important advantages of the PPP model and the existing problems in the cost control and management of highway projects,and puts forward effective measures for the cost control and management of highway projects using the PPP model,in hope of helping with the smooth implementation of the work.

Research and Application of Construction Project Cost Control in Engineering Bidding Stage

In order to strengthen cost control in construction project bidding stage, this paper takes the price control in bidding stage as the main line, dedicated to determine the key point in the stage of cost control, and establishes the more objective and reasonable evaluation in view of the comprehensive evaluation method, and combined with the practical application of engineering to demonstrate. This paper not only improved the tendering and bidding stage method of cost control in construction project in our country, and improves the method system of engineering cost control.

基于ANN贡献分析及GEP算法的地铁车站土建造价预测模型

科学地选取工程特征因素及预测方法对于构建一个好的造价预测模型十分关键。在选取的14个影响地铁车站土建造价特征因素中,利用神经网络贡献分析的变量选取方法,筛选出12个主要特征因素。并针对这选定的14个全部特征因素和12个主要特征因素分别组合BP神经网络和GEP 2种预测方法构建4个不同的造价预测模型,应用18组地铁车站土建造价和特征因素的历史数据进行实例探究,通过R2,MSE,RMSE和MaxRE 4个指标的评价,结果表明:用主要特征因素为模型输入变量能显著提高模型的预测精度,且和GEP算法组合建立的造价预测模型为最优。将主要特征因素选取和预测方法选取相结合构建求解的最优模型很好地解决了已有相关研究中选取特征因素主观性多科学性不足及未考虑特征因素选取对预测方法选取的影响问题。

Research on Intelligent Cost Estimation of Engineering Foundation Projects Based on CSIs Theory

Against the backdrop of rapid development in China’s construction and infrastructure sectors,discrepancies between project budgets and actual costs have become pronounced,manifesting in project overruns and suspensions,posing significant challenges.To address inaccuracies in investment targets and operational complexities,this study focuses on a beam-bridge construction project in a district of Shijiazhuang city as a case study.Drawing upon historical analogs,the project employs a Work Breakdown Structure(WBS)to decompose the engineering works.Building on theories of Cost Significant(CS)and Whole Life Costing(WLC),the study constructs Cost Significant Items(CSIs)and develops a CNN-BiLSTM-Attention neural network for nonlinear prediction.By identifying significant cost drivers in engineering projects,this paper presents a streamlined cost estimation method that significantly reduces computational burdens,simplifies data collection processes,and optimizes data analysis and forecasting,thereby enhancing prediction accuracy.Finally,validation with real-world cost fluctuation data demonstrates minor errors,meeting predictive requirements across project execution phases.

Artificial neural network-based repair and maintenance cost estimation model for rice combine harvesters

This research proposes an artificial neural network(ANN)-based repair and maintenance(R&M)cost estimation model for agricultural machinery.The proposed ANN model can achieve high estimation accuracy with small data requirement.In the study,the proposed ANN model is implemented to estimate the R&M costs using a sample of locally-made rice combine harvesters.The model inputs are geographical regions,harvest area,and curve fitting coefficients related to historical cost data;and the ANN output is the estimated R&M cost.Multilayer feed-forward is adopted as the processing algorithm and Levenberg-Marquardt backpropagation learning as the training algorithm.The R&M costs are estimated using the ANN-based model,and results are compared with those of conventional mathematical estimation model.The results reveal that the percentage error between the conventional and ANN-based estimation models is below 1%,indicating the proposed ANN model’s high predictive accuracy.The proposed ANN-based model is useful for setting the service rates of agricultural machinery,given the significance of R&M cost in profitability.The novelty of this research lies in the use of curve-fitting coefficients in the ANN-based estimation model to improve estimation accuracy.Besides,the proposed ANN model could be further developed into web-based applications using a programming language to enable ease of use and greater user accessibility.Moreover,with minor modifications,the ANN estimation model is also applicable to other geographical areas and tractors or combine harvesters of different countries of origin.

Limitations of Cost Estimation Using Building Information Modeling in Poland

BIM （building information modeling） is a technological innovation, not only during the design process, but also during the planning and preparation stages of a construction project, as it also supports making investment decisions. An innovation which is comparable, if only slightly less significant, was the transition from using 2D systems to the 3D structural model design. The article outlines the advantages of using BIM in the preparatory stages of a construction project. It also presents benefits which relate to the employment of the BIM system in cost estimation process. The article describes the Zuzia BIM system which uses the BIM model, as this system has just been created in Poland for the purpose of construction cost estimation. The preparation of the bill of quantities is automated in this system and this has been achieved on the basis of data directly obtained from virtual models of buildings, which were carried out thanks to the collaboration of various design sectors. The article authors, using their own experience, present difficulties which can be encountered by cost estimators in Poland when calculating the value of a building with the help of the BIM concept. The article shows the design errors that prevent or hinder takeoff automatic calculation based on BIM model. Design errors shown in the article are for example reinforcement bars have been defined by a designer as elements hollow in the middle or as one element for the whole building, one type of elements assigned as few different or incorrect defining of elements in relation to the type of works.

Model of Cost-benefit Flow in Six Green Projects in China

From the point of cost-benefit flow and with the Six Green Projects in China as the case and background, it is criticized that the current natural resource and environmental management and the policies in China have some problems unsolved since the model of cost-benefit flow is still centralized and mainly in an administrative way in spite of many efforts made theoretically and practically. It is suggested that a new model based on market-oriented economy from the point of cost-benefit flow with Six Green Projects Management and Policies System consist of complete natural reserve (NR). Management system included environmental nature reserves in addition to biodiversity nature reserve, paid using nature resources, an environmental conservation and construction industry and renewable resource production incentive system that turn the direct administration and operation to management and services. The detail figure of the new model of cost-benefit flow is provided and the main points related are discussed in this paper.

A Review of Energy-Related Cost Issues and Prediction Models in Cloud Computing Environments

With the expansion of cloud computing,optimizing the energy efficiency and cost of the cloud paradigm is considered significantly important,since it directly affects providers’revenue and customers’payment.Thus,providing prediction information of the cloud services can be very beneficial for the service providers,as they need to carefully predict their business growths and efficiently manage their resources.To optimize the use of cloud services,predictive mechanisms can be applied to improve resource utilization and reduce energy-related costs.However,such mechanisms need to be provided with energy awareness not only at the level of the Physical Machine(PM)but also at the level of the Virtual Machine(VM)in order to make improved cost decisions.Therefore,this paper presents a comprehensive literature review on the subject of energy-related cost issues and prediction models in cloud computing environments,along with an overall discussion of the closely related works.The outcomes of this research can be used and incorporated by predictive resource management techniques to make improved cost decisions assisted with energy awareness and leverage cloud resources efficiently.

Software Cost Estimation Approaches: A Survey

The software cost estimation aims to predict the most realistic effort that is required to finish a software project and so it is critical to the success of a software project management. A Software Cost Estimation affects nearly all management activities, including project bidding, resource allocation and project planning. It is affected by a number of factors, such as implementation efficiency, as well as how much the various reviews and studies completed prior to the software development stage cost. Accurate cost estimation will help us to complete the project on time and within budget. Accurate estimation is important because it has led to extensive research into the methods of software cost estimation. Some important software cost estimation methods have been studied in this research work. In addition, we have set out own criteria, which has been used to compare all the different selected methods. We have also given a score for each evaluation criteria, so that we can compare the different methods numerically for cost estimation. Our observations have shown that it is best to use a number of different estimating techniques or cost models, and then compare the results before determining the reasons for any of the large variations. None of the methods are necessarily better or worse than the others. We found, in fact, that their strengths and weaknesses often complement each other. Therefore, the main conclusion is that there is no one single technique that is best for every situation, and the results of a number of different approaches need to be carefully considered to discover what is the most likely to produce estimates that are realistic.

A Survey on Software Cost Estimation Techniques

The ability to accurately estimate the cost needed to complete a specific project has been a challenge over the past decades. For a successful software project, accurate prediction of the cost, time and effort is a very much essential task. This paper presents a systematic review of different models used for software cost estimation which includes algorithmic methods, non-algorithmic methods and learning-oriented methods. The models considered in this review include both the traditional and the recent approaches for software cost estimation. The main objective of this paper is to provide an overview of software cost estimation models and summarize their strengths, weakness, accuracy, amount of data needed, and validation techniques used. Our findings show, in general, neural network based models outperforms other cost estimation techniques. However, no one technique fits every problem and we recommend practitioners to search for the model that best fit their needs.

Manufacturing feature-based cost estimation of cast parts

Seventy percent of new product cost is committed in the design phase. Therefore, the cost needs to be minimized. Incorporating accurate cost estimation capabilities into manufacturing processes can help industries to minimize the cost and optimize the design. The casting process has been employed as a key process to manufacture parts having the advantage of low cost and customized products in foundry industry. This paper presents a cost estimation system for the casting process based on the design features, which incorporates the casting information at the design stage of castings. It aims to deliver the best cost-effective choice for casting design features of a product. Two kinds of knowledge, namely economical knowledge(describing the relation between the casting design considerations and the cost) and technical knowledge(involving the material of the casting features) were structured in this research work. On the basis of the minimum product cost, casting material selection can be carried out in detail. The developed system has been validated through an industrial case study.

Analyzing the Impact of Different Factors on Software Cost Estimation in Today’s Scenario

Software cost estimation is a main concern of the software industry. However, the fact is also that in today’s scenario, software industries are more interested in other issues like new technologies in the market, shorter development time, skill shortage etc. They are actually deviating from critical issues to routine issues. Today, people expect high quality products at very low costs and same is the goal of software engineering. An accuracy in software cost estimation has a direct impact on company’s reputation and also affects the software investment decisions. Accurate cost estimation can minimize the unnecessary costs and increase the productivity and efficiency of the company. The objective of this paper is to identify the existing methods of software cost estimation prevailing in the market and analyzing some of the important factors impacting the software cost estimation process. In order to achieve the objective, a survey was conducted to find out: ● Nature of projects that companies prefer. ● Impact of training on employees in software cost estimation. ● How many people review the estimated cost? ● How much risk buffer the company keeps for future prospects?

Intelligent Cost Modeling Based on Soft Computing for Avionics Systems

In parametric cost estimating, objections to using statistical Cost Estimating Relationships （CERs） and parametric models include problems of low statistical significance due to limited data points, biases in the underlying data, and lack of robustness. Soft Computing （SC） technologies are used for building intelligent cost models. The SC models are systemically evaluated based on their training and prediction of the historical cost data of airborne avionics systems. Results indicating the strengths and weakness of each model are presented. In general, the intelligent cost models have higher prediction precision, better data adaptability, and stronger self-learning capability than the regression CERs.

Fine-Grained Work Element Standardization for Project Effort Estimation

Traditional project effort estimation utilizes development models that span the entire project life cycle and thus culminates estimation errors. This exploratory research is the first attempt to break each project activity down to smaller work elements. There are eight work elements, each of which is being defined and symbolized with visually distinct shape. The purpose is to standardize the operations associated with the development process in the form of a visual symbolic flow map. Hence, developers can streamline their work systematically. Project effort estimation can be determined based on these standard work elements that not only help identify essential cost drivers for estimation, but also reduce latency cost to enhance estimation efficiency. Benefits of the proposed work element scheme are project visibility, better control for immediate pay-off and, in long term management, standardization for software process automation.

Price Analysis of Water Supply for South-to-North Water Transfer Project

The factors influencing the water supply price of a hydraulic project include natural conditions and economic policies. This paper thoroughly demonstrates the water price of South-to-North Water Transfer Project from the viewpoint of economic policies. It is considered that if the project is assigned as a profitable one and built depending on commercial loan from bank completely or mostly, the water price will be too high to be undertaken by users,and if the project places the public good at first while considering the economic benefit, its investment mainly relies on the state (national or local governments) appropriation and self-raised funds and a little from the bank loan on favorable terms, the price determined according to the principle of satisfying the cost and reasonable profit will be relatively lower and can be undertaken by the users in the North China where water shortage is serious. The problem of higher water price of agricultural irrigation to the north of the Yellow River can be tackled by taking measures such as "compensating agriculture by industry" according to foreign practical experiences and relevant suggestions.

OSS Effort Expense Optimization Based on Wiener Process Model and GA

Various open source software are managed by using several bug tracking systems. In particular, the open source software extends to the cloud service and edge computing. Recently, OSF Edge Computing Group is launched by OpenStack. There are big data behind the internet services such as cloud and edge computing. Then, it is important to consider the impact of big data in order to assess the reliability of open source software. Various optimal software release problems have been proposed by specific researchers. In the typical optimal software release problems, the cost parameters are defined as the known parameter. However, it is difficult to decide the cost parameter because of the uncertainty. The purpose of our research is to estimate the effort parameters included in our models. In this paper, we propose an estimation method of effort parameter by using the genetic algorithm. Then, we show the estimation method in section 3. Moreover, we analyze actual data to show numerical examples for the estimation method of effort parameter. As the research results, we found that the OSS managers would be able to comprehend the human resources required before the OSS project in advance by using our method.