In order to evaluate and estimate me cosEs oi prouuct~ plt, uu^u e vironment properly, an improved activity-based costing （ABC） model is presented. By utilizing the input-output analysis method, the complex consumpt...In order to evaluate and estimate me cosEs oi prouuct~ plt, uu^u e vironment properly, an improved activity-based costing （ABC） model is presented. By utilizing the input-output analysis method, the complex consumption relationships in a complex manufacturing environment are first expressed. The consumption characteristics （mainly presented by the activity rates） of all production activities are extracted by solving these relationships. Then with the con- sumption characteristics and operating parameters of these activities, the detailed cost consumption of a product in its manufacturing process is estimated. A case study is finally given based on the compressor products of a manufacturing company, and its effectiveness is shown. As the cost influ- ence of complex consumption relationships is fully considered, the limitation of traditional ABC method is overcome, and therefore a high accuracy in product cost estimation under the complex manufacturing environment can be achieved.展开更多
Objective:The aim of our study was to estimate the cost of colorectal cancer screening and to provide evidence for the cost control of colorectal cancer screening among general population in rural area of China.Method...Objective:The aim of our study was to estimate the cost of colorectal cancer screening and to provide evidence for the cost control of colorectal cancer screening among general population in rural area of China.Methods:We determined the net cost for colorectal cancer mass-screening in Jiashan County,and evaluated the cost-benefit and cost effectiveness.Results:The compliance rate of primary screening and intensive screening were 84.6% and 78.7%,respectively.In primary screening,the average cost for each individual was 27.2 yuan,and the average cost for identifying one high-risk individual was 180.5 yuan.The mean cost to diagnose one colorectal cancer patient was 42963.3 yuan.As for identification of adenoma,the average cost for each case was 4384.0 yuan.Based on the calculation,the average cost of reducing one colorectal cancer patient was 12768 yuan by conducting the mass-screening protocol.Conclusion:It was beneficial to do the cost-benefit analysis of colorectal cancer screening in area of high incidence.Based on the results of cost-benefit analysis,more efforts should be made to reduce the cost and to improve the efficiency of the colorectal cancer screening.展开更多
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...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%.展开更多
Software cost estimation is a crucial aspect of software project management,significantly impacting productivity and planning.This research investigates the impact of various feature selection techniques on software c...Software cost estimation is a crucial aspect of software project management,significantly impacting productivity and planning.This research investigates the impact of various feature selection techniques on software cost estimation accuracy using the CoCoMo NASA dataset,which comprises data from 93 unique software projects with 24 attributes.By applying multiple machine learning algorithms alongside three feature selection methods,this study aims to reduce data redundancy and enhance model accuracy.Our findings reveal that the principal component analysis(PCA)-based feature selection technique achieved the highest performance,underscoring the importance of optimal feature selection in improving software cost estimation accuracy.It is demonstrated that our proposed method outperforms the existing method while achieving the highest precision,accuracy,and recall rates.展开更多
Accurate software cost estimation in Global Software Development(GSD)remains challenging due to reliance on historical data and expert judgments.Traditional models,such as the Constructive Cost Model(COCOMO II),rely h...Accurate software cost estimation in Global Software Development(GSD)remains challenging due to reliance on historical data and expert judgments.Traditional models,such as the Constructive Cost Model(COCOMO II),rely heavily on historical and accurate data.In addition,expert judgment is required to set many input parameters,which can introduce subjectivity and variability in the estimation process.Consequently,there is a need to improve the current GSD models to mitigate reliance on historical data,subjectivity in expert judgment,inadequate consideration of GSD-based cost drivers and limited integration of modern technologies with cost overruns.This study introduces a novel hybrid model that synergizes the COCOMO II with Artificial Neural Networks(ANN)to address these challenges.The proposed hybrid model integrates additional GSD-based cost drivers identified through a systematic literature review and further vetted by industry experts.This article compares the effectiveness of the proposedmodelwith state-of-the-artmachine learning-basedmodels for software cost estimation.Evaluating the NASA 93 dataset by adopting twenty-six GSD-based cost drivers reveals that our hybrid model achieves superior accuracy,outperforming existing state-of-the-artmodels.The findings indicate the potential of combining COCOMO II,ANN,and additional GSD-based cost drivers to transform cost estimation in GSD.展开更多
In project management,effective cost estimation is one of the most cru-cial activities to efficiently manage resources by predicting the required cost to fulfill a given task.However,finding the best estimation results i...In project management,effective cost estimation is one of the most cru-cial activities to efficiently manage resources by predicting the required cost to fulfill a given task.However,finding the best estimation results in software devel-opment is challenging.Thus,accurate estimation of software development efforts is always a concern for many companies.In this paper,we proposed a novel soft-ware development effort estimation model based both on constructive cost model II(COCOMO II)and the artificial neural network(ANN).An artificial neural net-work enhances the COCOMO model,and the value of the baseline effort constant A is calibrated to use it in the proposed model equation.Three state-of-the-art publicly available datasets are used for experiments.The backpropagation feed-forward procedure used a training set by iteratively processing and training a neural network.The proposed model is tested on the test set.The estimated effort is compared with the actual effort value.Experimental results show that the effort estimated by the proposed model is very close to the real effort,thus enhanced the reliability and improving the software effort estimation accuracy.展开更多
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 invest...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.展开更多
A mathematical model that makes use of data mining and soft computing techniques is proposed to estimate the software development effort. The proposed model works as follows: The parameters that have impact on the dev...A mathematical model that makes use of data mining and soft computing techniques is proposed to estimate the software development effort. The proposed model works as follows: The parameters that have impact on the development effort are divided into groups based on the distribution of their values in the available dataset. The linguistic terms are identified for the divided groups using fuzzy functions, and the parameters are fuzzified. The fuzzified parameters then adopt associative classification for generating association rules. The association rules depict the parameters influencing the software development effort. As the number of parameters that influence the effort is more, a large number of rules get generated and can reduce the complexity, the generated rules are filtered with respect to the metrics, support and confidence, which measures the strength of the rule. Genetic algorithm is then employed for selecting set of rules with high quality to improve the accuracy of the model. The datasets such as Nasa93, Cocomo81, Desharnais, Maxwell, and Finnish-v2 are used for evaluating the proposed model, and various evaluation metrics such as Mean Magnitude of Relative Error, Mean Absolute Residuals, Shepperd and MacDonell’s Standardized Accuracy, Enhanced Standardized Accuracy and Effect Size are adopted to substantiate the effectiveness of the proposed methods. The results infer that the accuracy of the model is influenced by the metrics support, confidence, and the number of association rules considered for effort prediction.展开更多
In modern society, a plastic part has its own important position. To the managers and decision makers in the field of plastic component, how to decide the manufacturing cost of the injection part or injection mold as...In modern society, a plastic part has its own important position. To the managers and decision makers in the field of plastic component, how to decide the manufacturing cost of the injection part or injection mold as quickly as possible is most valuable. Cost estimation formulae (CEF) are the most common method to evaluate the cost of injection part/mold, which is similar to our ordinary thinking. In this paper, a CEF method used by Dr. Weiyi Hu in Massachusetts University is first discussed. To the problems existed in the formulae, we propose an improved algorithm. This algorithm can be used to estimate the manufacturing cost of injection part/mold accurately in the early design stage.展开更多
This paper proposes a method for assembly cost estimation in actual manufacture during the design phase using artificial neural networks(ANN). It can support the designers in cost effectiveness, then help to control t...This paper proposes a method for assembly cost estimation in actual manufacture during the design phase using artificial neural networks(ANN). It can support the designers in cost effectiveness, then help to control the total cost. The method was used in the assembly cost estimation of the crucial parts of some railway stock products. As a comparison, we use the linear regression (LR) model in the same field. The result shows that ANN model performs better than the LR model in assembly cost estimation.展开更多
The use of Fenton＇s reagent （Fe^2＋/H2O2） and Fenton-like reagents containing transition metals of Cu（Ⅱ）, Zn（Ⅱ）, Co（Ⅱ）, and Mn（Ⅱ） for an alum sludge conditioning to improve its dewaterability was invest...The use of Fenton＇s reagent （Fe^2＋/H2O2） and Fenton-like reagents containing transition metals of Cu（Ⅱ）, Zn（Ⅱ）, Co（Ⅱ）, and Mn（Ⅱ） for an alum sludge conditioning to improve its dewaterability was investigated. The results obtained were compared with those obtained from conditioning the same alum sludge using cationic and anionic polymers. Experimental results show that Fenton＇s reagent was the best among the Fenton and Fenton-like reagents for the alum sludge conditioning. A considerable effectiveness of capillary suction time （CST） reduction efficiency of 47% can be achieved under test conditions of Fe^2＋/H2O2 = 20/125 mg/g DS （dry solid） and pH 6.0. The observation of floc-like particles after Fenton＇s reagent conditioning of alum sludge suggested that the mechanism of Fenton＇s reagent conditioning was different from that of polymer conditioning. In spite of the lower efficiency in the CST reduction of Fenton＇s reagent in alum sludge conditioning compared to that of polymer conditioning, Fenton＇s reagent offers a more environmentally safe option. Tiffs study provided an example of proactive treatment engineering, which is aimed at seeking a safe alternative to the use of polymers in sludge conditioning towards achieving a more sustainable sludge management strategy.展开更多
In this work, we introduce a method of fingerprint directional image partitioning based on GA. According to the fingerprint topology, A set of dynamic partition masks and a cost estimating function are designed to gui...In this work, we introduce a method of fingerprint directional image partitioning based on GA. According to the fingerprint topology, A set of dynamic partition masks and a cost estimating function are designed to guide the partitioning procedure. Finding best fitted mask application is converted to an functional optimizing problem, and we give out a GA solution to the problem. At last, we discuss the application of the proposed method in Fingerprint Classification.展开更多
Drilling costs of ultra-deepwell is the significant part of development investment,and accurate prediction of drilling costs plays an important role in reasonable budgeting and overall control of development cost.In o...Drilling costs of ultra-deepwell is the significant part of development investment,and accurate prediction of drilling costs plays an important role in reasonable budgeting and overall control of development cost.In order to improve the prediction accuracy of ultra-deep well drilling costs,the item and the dominant factors of drilling costs in Tarim oilfield are analyzed.Then,those factors of drilling costs are separated into categorical variables and numerous variables.Finally,a BP neural networkmodel with drilling costs as the output is established,and hyper-parameters(initial weights and bias)of the BP neural network is optimized by genetic algorithm(GA).Through training and validation of themodel,a reliable prediction model of ultra-deep well drilling costs is achieved.The average relative error between prediction and actual values is 3.26%.Compared with other models,the root mean square error is reduced by 25.38%.The prediction results of the proposed model are reliable,and the model is efficient,which can provide supporting for the drilling costs control and budget planning of ultra-deep wells.展开更多
Refinery complexity quantifies the sophistication and capital intensity of a refinery and has found widespread application in facility classification, cost estimation, sales price models, and other uses. Despite the u...Refinery complexity quantifies the sophistication and capital intensity of a refinery and has found widespread application in facility classification, cost estimation, sales price models, and other uses. Despite the ubiquity and widespread use of refining complexity, however, surprisingly little material has been written on its applications. The pur- pose of this review is to describe the primary applications of refinery complexity and some recent extensions. A secondary purpose of this review is to provide a framework that unifies complexity applications and suggests avenues for future research. Examples illustrate the applications considered.展开更多
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,prov...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.展开更多
The complexity of the IH-635 Managed Lanes Project, located in Dallas County, Texas, posed several technical and constructive challenges, leading to the adoption of solutions different from the traditional. Two altern...The complexity of the IH-635 Managed Lanes Project, located in Dallas County, Texas, posed several technical and constructive challenges, leading to the adoption of solutions different from the traditional. Two alternative solutions for the pier cap on one of the bridge crossings over IH-35E in the IH-635 project were analyzed in this case study, a cast-in-place post-tensioned concrete cap and an innovative prefabricated steel-concrete com- posite cap. The approach was to use an estimation of direct costs for material and labor and consideration of con- struction time schedules. A supplementary numerical modeling confirmed that both alternatives behave elasti- cally under imposed loads. The direct cost of material and labor for the two alternatives were close. However, the composite alternative required 13 days less construction time, resulting in substantial cost savings from traffic closing in the very busy traffic corridor. Traffic closing costs were substantially higher than the direct costs, especially for the post-tensioned cap. The quantification of the benefits allows more confidence in the utilization of the composites caps, leading to faster completion of bridge projects and substantial economic savings.展开更多
Nowadays, clients require solutions that are efficient during the service life as a whole. In this view, maintenance has functional value and greatly contributes to increasing quality in environments. With reference t...Nowadays, clients require solutions that are efficient during the service life as a whole. In this view, maintenance has functional value and greatly contributes to increasing quality in environments. With reference to maintenance phase and residual value of building products upon dismantling, the present manuscript investigates a set of technological packages of quality and technical value referred to two alternative facade systems available for recovery actions on Italian council buildings--the ETICS （external thermal insulation composite system） and the external cladding system--complying with one thermal-insulation strategy, but different in terms of service life duration. Preservation of technical value and value increases are strictly related to maintenance quality and efficiency. Volume of investments in maintenance is not the only parameter to be considered to assess facade-system-management quality. Time-accurate economic assessment requires the entity to be related to periodical checks on performance quality in the building system and in its parts. Effective technical data to draw up consistent estimates and appraisals are rarely available in literature. With all that stated above, the present manuscript aims at introducing technical data related to drawing up reliable cost estimates and effective appraisals in terms of economics, quality and environmental sustainability. Conclusions will be drawn from the end of the manuscript.展开更多
A multi-layer adaptive optimizing parameters algorithm is developed forimproving least squares support vector machines (LS-SVM) , and a military aircraft life-cycle-cost(LCC) intelligent estimation model is proposed b...A multi-layer adaptive optimizing parameters algorithm is developed forimproving least squares support vector machines (LS-SVM) , and a military aircraft life-cycle-cost(LCC) intelligent estimation model is proposed based on the improved LS-SVM. The intelligent costestimation process is divided into three steps in the model. In the first step, a cost-drive-factorneeds to be selected, which is significant for cost estimation. In the second step, militaryaircraft training samples within costs and cost-drive-factor set are obtained by the LS-SVM. Thenthe model can be used for new type aircraft cost estimation. Chinese military aircraft costs areestimated in the paper. The results show that the estimated costs by the new model are closer to thetrue costs than that of the traditionally used methods.展开更多
Different forest fires causing different degrees of effects occur in fire-sensitive forests due to various reasons such as climate change.Useful as well as harmful aspects of forest fires are a multi-disciplinary rese...Different forest fires causing different degrees of effects occur in fire-sensitive forests due to various reasons such as climate change.Useful as well as harmful aspects of forest fires are a multi-disciplinary research topic.Geographical information systems(GIS)and remote sensing(RS)methods offer a number of benefits for researchers and operators in the field of forest fire research.The present study analyses timber pricing based on forest contractor demands of post-salvage logging processes.The effect of timber obtained from compartment units on producers’pricing policy was modelled.Sapadere forest fire area(2500 ha)located in Antalya in Turkey was selected as the main study area.Topography parameters(aspect,slope and slope position),stand types(diameter class and crown closure),and burn severity were analyzed together using GIS and R software packages.A multi-linear regression model(R^(2)=0.752)demonstrated that factors that had the most impact on pricing were slope position,aspect,stand age,crown closure and burn severity.This model can be used to estimate salvage logging prices in Calabrian pine(Pinus brutia Ten.)stands with similar parameters.Forest administrators and contractors may readily address the unit price of timber by estimating approximate costs in a given forest area for which they are going to bid.This will help reduce operational planning times of harvesting procedures in burned stands.展开更多
With the striking rise in penetration of Cloud Computing,energy consumption is considered as one of the key cost factors that need to be managed within cloud providers’infrastructures.Subsequently,recent approaches a...With the striking rise in penetration of Cloud Computing,energy consumption is considered as one of the key cost factors that need to be managed within cloud providers’infrastructures.Subsequently,recent approaches and strategies based on reactive and proactive methods have been developed for managing cloud computing resources,where the energy consumption and the operational costs are minimized.However,to make better cost decisions in these strategies,the performance and energy awareness should be supported at both Physical Machine(PM)and Virtual Machine(VM)levels.Therefore,in this paper,a novel hybrid approach is proposed,which jointly considered the prediction of performance variation,energy consumption and cost of heterogeneous VMs.This approach aims to integrate auto-scaling with live migration as well as maintain the expected level of service performance,in which the power consumption and resource usage are utilized for estimating the VMs’total cost.Specifically,the service performance variation is handled by detecting the underloaded and overloaded PMs;thereby,the decision(s)is made in a cost-effective manner.Detailed testbed evaluation demonstrates that the proposed approach not only predicts the VMs workload and consumption of power but also estimates the overall cost of live migration and auto-scaling during service operation,with a high prediction accuracy on the basis of historical workload patterns.展开更多
基金Supported by the National Natural Science Foundation of China(No.61074136)the National Science and Technology Major Project of China(No.2009ZX04014)
文摘In order to evaluate and estimate me cosEs oi prouuct~ plt, uu^u e vironment properly, an improved activity-based costing （ABC） model is presented. By utilizing the input-output analysis method, the complex consumption relationships in a complex manufacturing environment are first expressed. The consumption characteristics （mainly presented by the activity rates） of all production activities are extracted by solving these relationships. Then with the con- sumption characteristics and operating parameters of these activities, the detailed cost consumption of a product in its manufacturing process is estimated. A case study is finally given based on the compressor products of a manufacturing company, and its effectiveness is shown. As the cost influ- ence of complex consumption relationships is fully considered, the limitation of traditional ABC method is overcome, and therefore a high accuracy in product cost estimation under the complex manufacturing environment can be achieved.
文摘Objective:The aim of our study was to estimate the cost of colorectal cancer screening and to provide evidence for the cost control of colorectal cancer screening among general population in rural area of China.Methods:We determined the net cost for colorectal cancer mass-screening in Jiashan County,and evaluated the cost-benefit and cost effectiveness.Results:The compliance rate of primary screening and intensive screening were 84.6% and 78.7%,respectively.In primary screening,the average cost for each individual was 27.2 yuan,and the average cost for identifying one high-risk individual was 180.5 yuan.The mean cost to diagnose one colorectal cancer patient was 42963.3 yuan.As for identification of adenoma,the average cost for each case was 4384.0 yuan.Based on the calculation,the average cost of reducing one colorectal cancer patient was 12768 yuan by conducting the mass-screening protocol.Conclusion:It was beneficial to do the cost-benefit analysis of colorectal cancer screening in area of high incidence.Based on the results of cost-benefit analysis,more efforts should be made to reduce the cost and to improve the efficiency of the colorectal cancer screening.
文摘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%.
文摘Software cost estimation is a crucial aspect of software project management,significantly impacting productivity and planning.This research investigates the impact of various feature selection techniques on software cost estimation accuracy using the CoCoMo NASA dataset,which comprises data from 93 unique software projects with 24 attributes.By applying multiple machine learning algorithms alongside three feature selection methods,this study aims to reduce data redundancy and enhance model accuracy.Our findings reveal that the principal component analysis(PCA)-based feature selection technique achieved the highest performance,underscoring the importance of optimal feature selection in improving software cost estimation accuracy.It is demonstrated that our proposed method outperforms the existing method while achieving the highest precision,accuracy,and recall rates.
文摘Accurate software cost estimation in Global Software Development(GSD)remains challenging due to reliance on historical data and expert judgments.Traditional models,such as the Constructive Cost Model(COCOMO II),rely heavily on historical and accurate data.In addition,expert judgment is required to set many input parameters,which can introduce subjectivity and variability in the estimation process.Consequently,there is a need to improve the current GSD models to mitigate reliance on historical data,subjectivity in expert judgment,inadequate consideration of GSD-based cost drivers and limited integration of modern technologies with cost overruns.This study introduces a novel hybrid model that synergizes the COCOMO II with Artificial Neural Networks(ANN)to address these challenges.The proposed hybrid model integrates additional GSD-based cost drivers identified through a systematic literature review and further vetted by industry experts.This article compares the effectiveness of the proposedmodelwith state-of-the-artmachine learning-basedmodels for software cost estimation.Evaluating the NASA 93 dataset by adopting twenty-six GSD-based cost drivers reveals that our hybrid model achieves superior accuracy,outperforming existing state-of-the-artmodels.The findings indicate the potential of combining COCOMO II,ANN,and additional GSD-based cost drivers to transform cost estimation in GSD.
基金This work was supported by the Technology development Program of MSS[No.S3033853].
文摘In project management,effective cost estimation is one of the most cru-cial activities to efficiently manage resources by predicting the required cost to fulfill a given task.However,finding the best estimation results in software devel-opment is challenging.Thus,accurate estimation of software development efforts is always a concern for many companies.In this paper,we proposed a novel soft-ware development effort estimation model based both on constructive cost model II(COCOMO II)and the artificial neural network(ANN).An artificial neural net-work enhances the COCOMO model,and the value of the baseline effort constant A is calibrated to use it in the proposed model equation.Three state-of-the-art publicly available datasets are used for experiments.The backpropagation feed-forward procedure used a training set by iteratively processing and training a neural network.The proposed model is tested on the test set.The estimated effort is compared with the actual effort value.Experimental results show that the effort estimated by the proposed model is very close to the real effort,thus enhanced the reliability and improving the software effort estimation accuracy.
文摘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.
文摘A mathematical model that makes use of data mining and soft computing techniques is proposed to estimate the software development effort. The proposed model works as follows: The parameters that have impact on the development effort are divided into groups based on the distribution of their values in the available dataset. The linguistic terms are identified for the divided groups using fuzzy functions, and the parameters are fuzzified. The fuzzified parameters then adopt associative classification for generating association rules. The association rules depict the parameters influencing the software development effort. As the number of parameters that influence the effort is more, a large number of rules get generated and can reduce the complexity, the generated rules are filtered with respect to the metrics, support and confidence, which measures the strength of the rule. Genetic algorithm is then employed for selecting set of rules with high quality to improve the accuracy of the model. The datasets such as Nasa93, Cocomo81, Desharnais, Maxwell, and Finnish-v2 are used for evaluating the proposed model, and various evaluation metrics such as Mean Magnitude of Relative Error, Mean Absolute Residuals, Shepperd and MacDonell’s Standardized Accuracy, Enhanced Standardized Accuracy and Effect Size are adopted to substantiate the effectiveness of the proposed methods. The results infer that the accuracy of the model is influenced by the metrics support, confidence, and the number of association rules considered for effort prediction.
文摘In modern society, a plastic part has its own important position. To the managers and decision makers in the field of plastic component, how to decide the manufacturing cost of the injection part or injection mold as quickly as possible is most valuable. Cost estimation formulae (CEF) are the most common method to evaluate the cost of injection part/mold, which is similar to our ordinary thinking. In this paper, a CEF method used by Dr. Weiyi Hu in Massachusetts University is first discussed. To the problems existed in the formulae, we propose an improved algorithm. This algorithm can be used to estimate the manufacturing cost of injection part/mold accurately in the early design stage.
文摘This paper proposes a method for assembly cost estimation in actual manufacture during the design phase using artificial neural networks(ANN). It can support the designers in cost effectiveness, then help to control the total cost. The method was used in the assembly cost estimation of the crucial parts of some railway stock products. As a comparison, we use the linear regression (LR) model in the same field. The result shows that ANN model performs better than the LR model in assembly cost estimation.
基金The first author would like to appreciate Ministry of Higher Education, Missions Department, Egypt for the fi- nancial support granted through Channel Scheme Mission.
文摘The use of Fenton＇s reagent （Fe^2＋/H2O2） and Fenton-like reagents containing transition metals of Cu（Ⅱ）, Zn（Ⅱ）, Co（Ⅱ）, and Mn（Ⅱ） for an alum sludge conditioning to improve its dewaterability was investigated. The results obtained were compared with those obtained from conditioning the same alum sludge using cationic and anionic polymers. Experimental results show that Fenton＇s reagent was the best among the Fenton and Fenton-like reagents for the alum sludge conditioning. A considerable effectiveness of capillary suction time （CST） reduction efficiency of 47% can be achieved under test conditions of Fe^2＋/H2O2 = 20/125 mg/g DS （dry solid） and pH 6.0. The observation of floc-like particles after Fenton＇s reagent conditioning of alum sludge suggested that the mechanism of Fenton＇s reagent conditioning was different from that of polymer conditioning. In spite of the lower efficiency in the CST reduction of Fenton＇s reagent in alum sludge conditioning compared to that of polymer conditioning, Fenton＇s reagent offers a more environmentally safe option. Tiffs study provided an example of proactive treatment engineering, which is aimed at seeking a safe alternative to the use of polymers in sludge conditioning towards achieving a more sustainable sludge management strategy.
文摘In this work, we introduce a method of fingerprint directional image partitioning based on GA. According to the fingerprint topology, A set of dynamic partition masks and a cost estimating function are designed to guide the partitioning procedure. Finding best fitted mask application is converted to an functional optimizing problem, and we give out a GA solution to the problem. At last, we discuss the application of the proposed method in Fingerprint Classification.
基金supported by the Science and Technology Innovation Foundation of CNPC“Multiscale Flow Law and Flow Field Coupling Study of Tight Sandstone Gas Reservoir”(2016D-5007-0208)13th Five-Year National Major Project“Multistage Fracturing Effect and Production of Fuling Shale Gas HorizontalWell Law Analysis Research”(2016ZX05060-009).
文摘Drilling costs of ultra-deepwell is the significant part of development investment,and accurate prediction of drilling costs plays an important role in reasonable budgeting and overall control of development cost.In order to improve the prediction accuracy of ultra-deep well drilling costs,the item and the dominant factors of drilling costs in Tarim oilfield are analyzed.Then,those factors of drilling costs are separated into categorical variables and numerous variables.Finally,a BP neural networkmodel with drilling costs as the output is established,and hyper-parameters(initial weights and bias)of the BP neural network is optimized by genetic algorithm(GA).Through training and validation of themodel,a reliable prediction model of ultra-deep well drilling costs is achieved.The average relative error between prediction and actual values is 3.26%.Compared with other models,the root mean square error is reduced by 25.38%.The prediction results of the proposed model are reliable,and the model is efficient,which can provide supporting for the drilling costs control and budget planning of ultra-deep wells.
文摘Refinery complexity quantifies the sophistication and capital intensity of a refinery and has found widespread application in facility classification, cost estimation, sales price models, and other uses. Despite the ubiquity and widespread use of refining complexity, however, surprisingly little material has been written on its applications. The pur- pose of this review is to describe the primary applications of refinery complexity and some recent extensions. A secondary purpose of this review is to provide a framework that unifies complexity applications and suggests avenues for future research. Examples illustrate the applications considered.
文摘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.
基金herein for allowing the use of various data from the LBJ Project in the development of this paper
文摘The complexity of the IH-635 Managed Lanes Project, located in Dallas County, Texas, posed several technical and constructive challenges, leading to the adoption of solutions different from the traditional. Two alternative solutions for the pier cap on one of the bridge crossings over IH-35E in the IH-635 project were analyzed in this case study, a cast-in-place post-tensioned concrete cap and an innovative prefabricated steel-concrete com- posite cap. The approach was to use an estimation of direct costs for material and labor and consideration of con- struction time schedules. A supplementary numerical modeling confirmed that both alternatives behave elasti- cally under imposed loads. The direct cost of material and labor for the two alternatives were close. However, the composite alternative required 13 days less construction time, resulting in substantial cost savings from traffic closing in the very busy traffic corridor. Traffic closing costs were substantially higher than the direct costs, especially for the post-tensioned cap. The quantification of the benefits allows more confidence in the utilization of the composites caps, leading to faster completion of bridge projects and substantial economic savings.
文摘Nowadays, clients require solutions that are efficient during the service life as a whole. In this view, maintenance has functional value and greatly contributes to increasing quality in environments. With reference to maintenance phase and residual value of building products upon dismantling, the present manuscript investigates a set of technological packages of quality and technical value referred to two alternative facade systems available for recovery actions on Italian council buildings--the ETICS （external thermal insulation composite system） and the external cladding system--complying with one thermal-insulation strategy, but different in terms of service life duration. Preservation of technical value and value increases are strictly related to maintenance quality and efficiency. Volume of investments in maintenance is not the only parameter to be considered to assess facade-system-management quality. Time-accurate economic assessment requires the entity to be related to periodical checks on performance quality in the building system and in its parts. Effective technical data to draw up consistent estimates and appraisals are rarely available in literature. With all that stated above, the present manuscript aims at introducing technical data related to drawing up reliable cost estimates and effective appraisals in terms of economics, quality and environmental sustainability. Conclusions will be drawn from the end of the manuscript.
文摘A multi-layer adaptive optimizing parameters algorithm is developed forimproving least squares support vector machines (LS-SVM) , and a military aircraft life-cycle-cost(LCC) intelligent estimation model is proposed based on the improved LS-SVM. The intelligent costestimation process is divided into three steps in the model. In the first step, a cost-drive-factorneeds to be selected, which is significant for cost estimation. In the second step, militaryaircraft training samples within costs and cost-drive-factor set are obtained by the LS-SVM. Thenthe model can be used for new type aircraft cost estimation. Chinese military aircraft costs areestimated in the paper. The results show that the estimated costs by the new model are closer to thetrue costs than that of the traditionally used methods.
文摘Different forest fires causing different degrees of effects occur in fire-sensitive forests due to various reasons such as climate change.Useful as well as harmful aspects of forest fires are a multi-disciplinary research topic.Geographical information systems(GIS)and remote sensing(RS)methods offer a number of benefits for researchers and operators in the field of forest fire research.The present study analyses timber pricing based on forest contractor demands of post-salvage logging processes.The effect of timber obtained from compartment units on producers’pricing policy was modelled.Sapadere forest fire area(2500 ha)located in Antalya in Turkey was selected as the main study area.Topography parameters(aspect,slope and slope position),stand types(diameter class and crown closure),and burn severity were analyzed together using GIS and R software packages.A multi-linear regression model(R^(2)=0.752)demonstrated that factors that had the most impact on pricing were slope position,aspect,stand age,crown closure and burn severity.This model can be used to estimate salvage logging prices in Calabrian pine(Pinus brutia Ten.)stands with similar parameters.Forest administrators and contractors may readily address the unit price of timber by estimating approximate costs in a given forest area for which they are going to bid.This will help reduce operational planning times of harvesting procedures in burned stands.
文摘With the striking rise in penetration of Cloud Computing,energy consumption is considered as one of the key cost factors that need to be managed within cloud providers’infrastructures.Subsequently,recent approaches and strategies based on reactive and proactive methods have been developed for managing cloud computing resources,where the energy consumption and the operational costs are minimized.However,to make better cost decisions in these strategies,the performance and energy awareness should be supported at both Physical Machine(PM)and Virtual Machine(VM)levels.Therefore,in this paper,a novel hybrid approach is proposed,which jointly considered the prediction of performance variation,energy consumption and cost of heterogeneous VMs.This approach aims to integrate auto-scaling with live migration as well as maintain the expected level of service performance,in which the power consumption and resource usage are utilized for estimating the VMs’total cost.Specifically,the service performance variation is handled by detecting the underloaded and overloaded PMs;thereby,the decision(s)is made in a cost-effective manner.Detailed testbed evaluation demonstrates that the proposed approach not only predicts the VMs workload and consumption of power but also estimates the overall cost of live migration and auto-scaling during service operation,with a high prediction accuracy on the basis of historical workload patterns.
