Cost Estimate and Input Energy of Floor Systems in Low Seismic Regions

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.

A Hybrid Model for Improving Software Cost Estimation in Global Software Development

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.

Prediction Model of Drilling Costs for Ultra-Deep Wells Based on GA-BP Neural Network

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.

Forecasting on Indonesian Coal Production and Future Extraction Cost: A Tool for Formulating Policy on Coal Marketing

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.

Cost estimation and control of colorectal cancer screening

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.

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

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

Support Vector Machine Cost Estimation Model for Road Projects

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

Estimating Military Aircraft Cost Using Least Squares Support Vector Machines

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.

Estimating costs of salvage logging for large-scale burned forest lands: A case study on Turkey's Mediterranean coast

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.

A Hybrid Approach for Performance and Energy-Based Cost Prediction in Clouds

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.

An improved activity-based costing model for product cost estimation applied in a complex manufacturing environment

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.

Streamlining Takeoff and Estimating with BIM:A Case Study of a Construction Cost Estimating Course

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.

Important Factors for Construction Project Cost Estimating Using ANN

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

Exploring ERP Adoption Cost Factors

Due to their limited resources, budgets and their high sensitivity to costs, when Small and Medium Enterprises （SMEs） take the first step into implementing an Enterprise Resource Planning （ERP） system, they need to think about many things, foremost the cost of adoption. Literature suggests that most ERP implementations fail due to inaccurate and optimistic budget and schedule estimations, as well as, anticipating indirect costs beforehand is problematic. With the deficiency of a clear model of cost factors for ERP adoptions, ERP adoptions face high risks of failure. Failures could be caused by several factors, but the scope of this research is focused on identifying, exploring, and validating a comprehensive list of ERP adoption cost factors. This could aid SMEs in visualizing the different expected costs, and would consequently assist in better future cost management and estimations. There has been plenty of research in ERP; however, a clear gap in ERP cost identification, management, and estimation exists. This paper focuses on identifying direct and indirect cost factors that influence total costs in the ERP adoption process. The paper presents a cost list that has been developed through literature and an ERP expert panel. Furthermore, this study validates the costs list through interviews with different stakeholders within ERP adoption projects in Egypt.

Limitations of Cost Estimation Using Building Information Modeling in Poland

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

Method for Estimating of Construction Cost of a Building Based on Previous Experiences

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.

A Modified Cost Estimation Formulae for Calculating Manufacturing Cost of Injection Part/Mold

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.

Product Assembly Cost Estimation Based on Artificial Neural Networks

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.

Intelligent Cost Modeling Based on Soft Computing for Avionics Systems

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

Advanced Strategies to Mobilize Crop Residue to Replace Coal in India

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.