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
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
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
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
Since the construction industry has been adopting Building Information Modeling(BIM)as the standard practice for design,engineering,and fabrication in the recent decade,many Construction Management(CM)programs at U.S....Since the construction industry has been adopting Building Information Modeling(BIM)as the standard practice for design,engineering,and fabrication in the recent decade,many Construction Management(CM)programs at U.S.universities have started to introduce BIM for cost estimating in their curriculum.Although considered as the fifth dimension beyond 3D and schedule,BIM for cost estimating in many cases is still used merely as an alternative model-based quantity takeoff method to the traditional plan-based approach.The disconnection between automated quantity takeoff and cost estimating,however,still exists,and the benefits of the BIM process in a project life cycle can not be fully understood by CM students without realizing its impact in the preconstruction phase.To bridge these gaps in a CM curriculum,an Advanced Cost Estimating course for CM programs has been developed that focuses on integrating BIM in both the takeoff and estimating process.The new course streamlines the connection between model-based quantity takeoff and cost estimating with the help of a combination of multiple construction software programs.Through the integration between the software,quantity data from a BIM model can be seamlessly transferred to a construction cost database for bid pricing and reporting.This paper presents the development of the new Advanced Cost Estimating course as a case study,including its objectives,layout,and assessment methods,and provides empirical and valuable insights on how to integrate BIM in a cost estimating course for a CM curriculum.展开更多
In a real estate project, the estimated cost of construction and the revenues generally represent together the most important values of its feasibility study. When a decision of undertaking a project is made, often th...In a real estate project, the estimated cost of construction and the revenues generally represent together the most important values of its feasibility study. When a decision of undertaking a project is made, often there are few definitions of what is about to be constructed, and frequently not enough to ensure the accuracy of the estimated costs. Considering a global tendency on reducing margins of return over the real estate markets, slight variations of the construction cost can jeopardize the success of the whole real estate enterprise and even the financial stability of the builder or of the developer. This article aims at presenting a method of estimating the building construction costs applicable at the stage of feasibility studies, being able to provide acceptable errors.展开更多
Reinforced concrete(RC)as a material is most commonly used for buildings construction.Several floor systems are available following the structural and architectural requirements.The current research study provides cos...Reinforced concrete(RC)as a material is most commonly used for buildings construction.Several floor systems are available following the structural and architectural requirements.The current research study provides cost and input energy comparisons of RC office buildings of different floor systems.Conventional solid,ribbed,flat plate and flat slab systems are considered in the study.Building models in three-dimensional using extended threedimensional analysis of building systems(ETABS)and in two-dimensional using slab analysis by the finite element(SAFE)are developed for analysis purposes.Analysis and design using both software packages and manual calculations are employed to obtain the optimum sections and reinforcements to fit cities of low seismic intensities for all the considered building systems.Two ground motion records of low peak ground acceleration(PGA)levels are used to excite the models to measure the input energies.Uniformat cost estimating system is adopted to categorize building components according to 12 divisions.Also,Microsoft(MS)Project is utilized to identify the construction cost and duration of each building system.The study shows that floor system significantly causes changes in the input energy to structures.In addition,the slight increase in the PGA increases the amount of input energy particularly flat plate system.Estimated cost of the flat plate system that the flat slab system is of higher value as compared to ribbed and conventional slab systems.The use of drop panels increases this value as well.Moreover,the estimated cost of the ribbed slab system exceeds that of conventional system.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
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
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
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 un...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.展开更多
Since 2011 Indonesia has become the world’s largest exporter of steam coal. Two supporting factors of Indonesia to be the largest exporter are its enormous production and low operating cost. This paper foresees the p...Since 2011 Indonesia has become the world’s largest exporter of steam coal. Two supporting factors of Indonesia to be the largest exporter are its enormous production and low operating cost. This paper foresees the production and extraction cost of Indonesian coal in the coming period to evaluate marketing policies and estimate the cost of Indonesian coal supply in domestic market as well as in export market. The production forecasting is carried out by Gompertz curve. Peak production of Indonesian coal is expected to take place in 2026. Moreover, the extraction cost in coal basins which produce high calorific value of coal, in accordance to the operating cost forecasting, is higher than the one with low calorific value of coal due to its higher stripping ratio. Three main basins of Central Sumatra, Tarakan, and Barito basins play major rule in supplying coal for domestic use in short term. And other coal basins such as South Sumatra, Kutai, Bengkulu, and Ombilin basins provide long term supply in the domestic and export markets.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
Various published data show the amount of crop residue available annually in India may range from a low of 90 to a high of 180 million tonnes. Different types of crop residue are collected from farmers depending on th...Various published data show the amount of crop residue available annually in India may range from a low of 90 to a high of 180 million tonnes. Different types of crop residue are collected from farmers depending on the geography and crop pattern for instance, in north India rice straw and cotton stalks are collected while in central India soya husk and sugarcane tops are collected. Baling and transporting straw from the field, though appear to be an option for safe disposal, will be feasible only when alternate, effective and economically viable usage methods are identified and facilities and infrastructure for ex-situ management methods are created. One immediate short term use of the residue is to replace 5% - 7% of the 670 million tonnes of coal India currently consumes to generate power. The farmers will benefit from the sale of their excess crop residue. The scheme will reduce pollution due to residue burning practices. Replacing coal will cut the GHG emissions. The challenge is to mobilize the crop residue collection and timely delivery to power plants. The data and calculations in this monogram show that it is economical for the farmer to remove the crop residue from the field quickly by using modern balers, to pelletize the biomass in small-scale distributed pellet plants, to store pellets in the modern steel bins and finally to deliver the pellets to coal plants by using rail transport. The delivered cost is estimated at around Rp 6.78/kg. The Government of India encourages the power plants to pay at least Rp 10/kg for the delivered biomass in the form of pellets. The current monogram analyzes the organization of an efficient supply chain in the State of Haryana India to ensure a sustainable modern enterprise.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
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
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
Objective:A computational model of insulin secretion and glucose metabolism for assisting the diagnosis of diabetes mellitus in clinical research is introduced.The proposed method for the estimation of parameters for...Objective:A computational model of insulin secretion and glucose metabolism for assisting the diagnosis of diabetes mellitus in clinical research is introduced.The proposed method for the estimation of parameters for a system of ordinary differential equations(ODEs)that represent the time course of plasma glucose and insulin concentrations during glucose tolerance test(GTT)in physiological studies is presented.The aim of this study was to explore how to interpret those laboratory glucose and insulin data as well as enhance the Ackerman mathematical model.Methods:Parameters estimation for a system of ODEs was performed by minimizing the sum of squared residuals(SSR)function,which quantifies the difference between theoretical model predictions and GTT's experimental observations.Our proposed perturbation search and multiple-shooting methods were applied during the estimating process.Results:Based on the Ackerman's published data,we estimated the key parameters by applying R-based iterative computer programs.As a result,the theoretically simulated curves perfectly matched the experimental data points.Our model showed that the estimated parameters,computed frequency and period values,were proven a good indicator of diabetes.Conclusion:The present paper introduces a computational algorithm to biomedical problems,particularly to endocrinology and metabolism fields,which involves two coupled differential equations with four parameters describing the glucose-insulin regulatory system that Ackerman proposed earlier.The enhanced approach may provide clinicians in endocrinology and metabolism field insight into the transition nature of human metabolic mechanism from normal to impaired glucose tolerance.展开更多
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.
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.
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Since the construction industry has been adopting Building Information Modeling(BIM)as the standard practice for design,engineering,and fabrication in the recent decade,many Construction Management(CM)programs at U.S.universities have started to introduce BIM for cost estimating in their curriculum.Although considered as the fifth dimension beyond 3D and schedule,BIM for cost estimating in many cases is still used merely as an alternative model-based quantity takeoff method to the traditional plan-based approach.The disconnection between automated quantity takeoff and cost estimating,however,still exists,and the benefits of the BIM process in a project life cycle can not be fully understood by CM students without realizing its impact in the preconstruction phase.To bridge these gaps in a CM curriculum,an Advanced Cost Estimating course for CM programs has been developed that focuses on integrating BIM in both the takeoff and estimating process.The new course streamlines the connection between model-based quantity takeoff and cost estimating with the help of a combination of multiple construction software programs.Through the integration between the software,quantity data from a BIM model can be seamlessly transferred to a construction cost database for bid pricing and reporting.This paper presents the development of the new Advanced Cost Estimating course as a case study,including its objectives,layout,and assessment methods,and provides empirical and valuable insights on how to integrate BIM in a cost estimating course for a CM curriculum.
文摘In a real estate project, the estimated cost of construction and the revenues generally represent together the most important values of its feasibility study. When a decision of undertaking a project is made, often there are few definitions of what is about to be constructed, and frequently not enough to ensure the accuracy of the estimated costs. Considering a global tendency on reducing margins of return over the real estate markets, slight variations of the construction cost can jeopardize the success of the whole real estate enterprise and even the financial stability of the builder or of the developer. This article aims at presenting a method of estimating the building construction costs applicable at the stage of feasibility studies, being able to provide acceptable errors.
文摘Reinforced concrete(RC)as a material is most commonly used for buildings construction.Several floor systems are available following the structural and architectural requirements.The current research study provides cost and input energy comparisons of RC office buildings of different floor systems.Conventional solid,ribbed,flat plate and flat slab systems are considered in the study.Building models in three-dimensional using extended threedimensional analysis of building systems(ETABS)and in two-dimensional using slab analysis by the finite element(SAFE)are developed for analysis purposes.Analysis and design using both software packages and manual calculations are employed to obtain the optimum sections and reinforcements to fit cities of low seismic intensities for all the considered building systems.Two ground motion records of low peak ground acceleration(PGA)levels are used to excite the models to measure the input energies.Uniformat cost estimating system is adopted to categorize building components according to 12 divisions.Also,Microsoft(MS)Project is utilized to identify the construction cost and duration of each building system.The study shows that floor system significantly causes changes in the input energy to structures.In addition,the slight increase in the PGA increases the amount of input energy particularly flat plate system.Estimated cost of the flat plate system that the flat slab system is of higher value as compared to ribbed and conventional slab systems.The use of drop panels increases this value as well.Moreover,the estimated cost of the ribbed slab system exceeds that of conventional system.
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.
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.
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.
文摘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.
文摘Since 2011 Indonesia has become the world’s largest exporter of steam coal. Two supporting factors of Indonesia to be the largest exporter are its enormous production and low operating cost. This paper foresees the production and extraction cost of Indonesian coal in the coming period to evaluate marketing policies and estimate the cost of Indonesian coal supply in domestic market as well as in export market. The production forecasting is carried out by Gompertz curve. Peak production of Indonesian coal is expected to take place in 2026. Moreover, the extraction cost in coal basins which produce high calorific value of coal, in accordance to the operating cost forecasting, is higher than the one with low calorific value of coal due to its higher stripping ratio. Three main basins of Central Sumatra, Tarakan, and Barito basins play major rule in supplying coal for domestic use in short term. And other coal basins such as South Sumatra, Kutai, Bengkulu, and Ombilin basins provide long term supply in the domestic and export markets.
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Various published data show the amount of crop residue available annually in India may range from a low of 90 to a high of 180 million tonnes. Different types of crop residue are collected from farmers depending on the geography and crop pattern for instance, in north India rice straw and cotton stalks are collected while in central India soya husk and sugarcane tops are collected. Baling and transporting straw from the field, though appear to be an option for safe disposal, will be feasible only when alternate, effective and economically viable usage methods are identified and facilities and infrastructure for ex-situ management methods are created. One immediate short term use of the residue is to replace 5% - 7% of the 670 million tonnes of coal India currently consumes to generate power. The farmers will benefit from the sale of their excess crop residue. The scheme will reduce pollution due to residue burning practices. Replacing coal will cut the GHG emissions. The challenge is to mobilize the crop residue collection and timely delivery to power plants. The data and calculations in this monogram show that it is economical for the farmer to remove the crop residue from the field quickly by using modern balers, to pelletize the biomass in small-scale distributed pellet plants, to store pellets in the modern steel bins and finally to deliver the pellets to coal plants by using rail transport. The delivered cost is estimated at around Rp 6.78/kg. The Government of India encourages the power plants to pay at least Rp 10/kg for the delivered biomass in the form of pellets. The current monogram analyzes the organization of an efficient supply chain in the State of Haryana India to ensure a sustainable modern enterprise.
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.
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.
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.
基金supported by a grant from the NIH(No.U42 RR16607)
文摘Objective:A computational model of insulin secretion and glucose metabolism for assisting the diagnosis of diabetes mellitus in clinical research is introduced.The proposed method for the estimation of parameters for a system of ordinary differential equations(ODEs)that represent the time course of plasma glucose and insulin concentrations during glucose tolerance test(GTT)in physiological studies is presented.The aim of this study was to explore how to interpret those laboratory glucose and insulin data as well as enhance the Ackerman mathematical model.Methods:Parameters estimation for a system of ODEs was performed by minimizing the sum of squared residuals(SSR)function,which quantifies the difference between theoretical model predictions and GTT's experimental observations.Our proposed perturbation search and multiple-shooting methods were applied during the estimating process.Results:Based on the Ackerman's published data,we estimated the key parameters by applying R-based iterative computer programs.As a result,the theoretically simulated curves perfectly matched the experimental data points.Our model showed that the estimated parameters,computed frequency and period values,were proven a good indicator of diabetes.Conclusion:The present paper introduces a computational algorithm to biomedical problems,particularly to endocrinology and metabolism fields,which involves two coupled differential equations with four parameters describing the glucose-insulin regulatory system that Ackerman proposed earlier.The enhanced approach may provide clinicians in endocrinology and metabolism field insight into the transition nature of human metabolic mechanism from normal to impaired glucose tolerance.