Cost Estimation Model Research Articles

Research on differentiated quality control strategy of UHV converter transformer based on economic sensitivity analysis

±800kV converter transformer is the most difficult and valuable equipment in UHVDC converter station. The total price of converter transformer in a single project is up to RMB 4.9 billion. The converter transformer connects the AC power grid and the converter valve hall, which is the hub of DC transmission. It realizes the electrical isolation, power exchange, voltage matching and on load regulation of AC / DC system through electromagnetic induction. The cost and quality performance of UHV converter transformer also determine the economy and reliability of power system operation. The purpose of this paper is to ± Taking 800 kV converter transformer as the research object, through a large number of investigation and field investigation, the establishment of the ± Procurement cost estimation model of 800 kV converter transformer. In this study, through exploring the cost characteristics and quality characteristics of the main materials and components of the transformer, the concepts of cost sensitivity and quality sensitivity are proposed, and the four quadrant matrix analysis method is used to analyse the cost and quality control of the transformer, and then the differentiated quality control strategy is formulated. It can guide the quality grading control of converter transformer in engineering, and realize the collaborative improvement of power equipment economy and reliability.

VSIM: Distributed local structural vertex similarity calculation on big graphs

In many graph analytical applications, the local structural vertex similarity calculation is an essential prerequisite for advanced graph mining. The similarity calculation finds out all the similar vertex pairs whose local structural similarity scores (like the number of common neighbors, and the Jaccard index of adjacency sets) are above a given threshold. The real-world applications use a wide range of similarity thresholds. However, the existing distributed methods for the problem only optimize for either high thresholds (> 0.7) or low thresholds (< 0.1). To overcome the drawback, we propose a new distributed vertex similarity calculation framework VSIM that is efficient under a broad range of thresholds. VSIM processes static undirected graphs with local structural similarity scores that measure the similarity between vertices based on the first-order topology information. VSIM generates a similarity calculation task for every vertex in the graph and conducts all the tasks in parallel on a distributed computing platform along with a distributed key-value store. Each task finds vertices similar to a given center vertex with two task execution modes. The two modes optimize for high and low thresholds, respectively. Each task picks the suitable mode adaptively according to cost estimation models. We also propose an efficient implementation for VSIM on Apache Spark, with three optimization techniques to reduce communication costs and balance workloads on power-law graphs. The experimental evaluation shows that VSIM outperforms the state-of-the-art distributed methods by up to 67x speedup. VSIM can achieve near-linear node scalability in low-threshold and small cache scenarios.

Construction Cost and Carbon Emission Assessment of a Highway Construction—A Case towards Sustainable Transportation

Due to its dynamic nature in construction, benchmarking environmental emissions of road construction projects can be a daunting task. Often stakeholders will have to prioritize the economic and environmental indicators based on the project objectives. The study presents a methodological framework to compare economic and environmental impacts to benchmark sustainable transport construction projects. Through findings, the study aims to inform focus areas and key stages of infrastructure projects to benchmark sustainable performance. Process-based emission and cost estimation models are presented with an AHP based weighting factor that enables prioritization of emissions and costs based on project scopes and objectives. Using a case study, results are represented to validate the framework and methodology. Concrete and steel are identified as the main materials that contribute to total carbon emissions, while soil and gravel are responsible for the highest costs. Electricity consumption is discovered as the major fuel type contributing to carbon emissions. Concrete and dump trucks are discovered as the top two sources of emissions and costs, respectively. Scenario analyses revealed that the choice of equipment significantly affects the project’s emissions and costs. The application of sustainable materials can significantly reduce emissions and cost. The use of the case study approach results in a lack of generalizability. However, the same methodology and process can be adopted for the sustainable benchmarking of different projects. Researchers are encouraged to investigate processes to automate sustainable benchmarking of transport infrastructure construction projects. The study is one of the first attempts to compare cost and environmental impacts using a systematic methodology of transportation infrastructure construction projects.

Improved Effort and Cost Estimation Model Using Artificial Neural Networks and Taguchi Method with Different Activation Functions

Software estimation involves meeting a huge number of different requirements, such as resource allocation, cost estimation, effort estimation, time estimation, and the changing demands of software product customers. Numerous estimation models try to solve these problems. In our experiment, a clustering method of input values to mitigate the heterogeneous nature of selected projects was used. Additionally, homogeneity of the data was achieved with the fuzzification method, and we proposed two different activation functions inside a hidden layer, during the construction of artificial neural networks (ANNs). In this research, we present an experiment that uses two different architectures of ANNs, based on Taguchi’s orthogonal vector plans, to satisfy the set conditions, with additional methods and criteria for validation of the proposed model, in this approach. The aim of this paper is the comparative analysis of the obtained results of mean magnitude relative error (MMRE) values. At the same time, our goal is also to find a relatively simple architecture that minimizes the error value while covering a wide range of different software projects. For this purpose, six different datasets are divided into four chosen clusters. The obtained results show that the estimation of diverse projects by dividing them into clusters can contribute to an efficient, reliable, and accurate software product assessment. The contribution of this paper is in the discovered solution that enables the execution of a small number of iterations, which reduces the execution time and achieves the minimum error.

Early-stage cost estimation model for power generation project with limited historical data

PurposeReliable conceptual cost estimation of large-scale construction projects is critical for successful project planning and execution. For addressing the limited data availability in conceptual cost estimation, this study proposes an enhanced ANN-based cost estimating model that incorporates artificial neural networks, ensemble modeling and a factor analysis approach.Design/methodology/approachIn the ANN-based conceptual cost estimating model, the ensemble modeling component enhances training, and thus, improves its predictive accuracy and stability when project data quantity is low; and the factor analysis component finds the optimal input for an estimating model, rendering explanations of project data more descriptive.FindingsOn the basis of the results of experiments, it can be concluded that ensemble modeling and FAMD (Factor Analysis of Mixed Data) are both conjointly capable of improving the accuracy of conceptual cost estimates. The ANN model version combining bootstrap aggregation and FAMD improved estimation accuracy and reliability despite these very low project sample sizes.Research limitations/implicationsThe generalizability of the findings is hard to justify since it is difficult to collect cost data of construction projects comprehensively. But this difficulty means that our proposed approaches and findings can provide more accurate and stable conceptual cost forecasting in the early stages of project development.Originality/valueFrom the perspective of this research, previous uses of past-project data can be deemed to have underutilized that information, and this study has highlighted that — even when limited in quantity — past-project data can and should be utilized effectively in the generation of conceptual cost estimates.

Pipelined XPath Query Based on Cost Optimization

XPath query is the key part of XML data processing, and its performance is usually critical for XML applications. In the process of XPath query, there is inherent seriality between query steps, which makes it difficult to parallelize the query effectively as a whole. On the other hand, although XPath query has the characteristics of data stream processing and is suitable for pipeline processing, the data flow of each query step usually varies a lot, which results in limited performance under multithreading conditions. In this paper, we propose a pipelined XPath query method (PXQ) based on cost optimization. This method uses pipelined query primitives to process query steps based on relation index. During pipeline construction, a cost estimation model based on XML statistics is proposed to estimate the cost of the query primitive and provide guidance for the creation of a pipeline phase through the partition of query primitive sequence. The pipeline construction technique makes full use of available worker threads and optimizes the load balance between pipeline stages. The experimental results show that our method can adapt to the multithreaded environment and stream processing scenarios of XPath query, and its performance is better than the existing typical query methods based on data parallelism.

A serialized civil aircraft R&D cost estimation model considering commonality based on BP algorithm

The common design of serial civil aircraft, an important strategy of modern civil aircraft research and develop-ment, minimizes the whole life cycle cost of civil aircraft through asset reuse and resource sharing. However, the existing estimating model for the R&D cost of civil aircraft ignores the effects of common design, so the value estimated by estimating derivative models is significantly inconsistent with the actual one. To solve this problem, a novel assessment method for civil aircraft commonality indicators is developed based on fuzzy set in the present study, exploiting the attributes and structural parameters of the aircraft to be assessed as input to determine the degree of membership that pertains to the commonality sub-interval as the commonality indicator. Then the BP (Back Propagation) neural network algorithm is adopted to establish the relationship between the common index and the decrease rate of the R&D cost of derivative models. The model employs over a dozen typical civil aircraft models (e.g., Boeing, Airbus, and Bombardier) as the sample data for network learning training to build a mature neural network model for estimating the R&D cost of novel derivative models. As revealed from the comparative analysis on the calculated results of the samples, the estimated results of the model given the effects of commonality in the present study exhibit higher estimation accuracy and value for future work.

Early cost estimation models based on multiple regression analysis for road and railway tunnel projects

Estimates of a tunnel construction cost are among the most critical tasks during the planning stage of both road and railway projects to justify the project and allow a valid comparison between alternative solutions and perform reliable “what if” scenarios relative to the tunnel diameter and length. Numerous factors influence the tunnel construction cost, and very little information on these factors is available at the early stage of project planning. Developing an accurate cost estimate is therefore very difficult at this stage, and thus, a very limited number of cost models are available for this purpose. This paper develops early parametric cost estimating models for road and railway tunnels in the planning stage of a project based upon the application of multiple regression analysis on 25 constructed projects located in Western European countries. The developed models incorporate not only tunnel length and diameter but also the type of tunneling methods (mechanized and conventional), which are largely affected by geological conditions. The results showed high correlation coefficients (R2) of 0.968 and 0.79 for mechanized and conventional tunneling models respectively. In addition, the results of the developed models were compared against actual costs to assess their accuracy and robustness. The developed models achieved cost estimation accuracy over 75%, indicating that the models fit for their purpose and lead to fairly accurate cost estimates of road and railway tunnels.

994 The Cost of Testicular Torsion Care in Ireland 2009-2018: A Novel Cost Estimation Model Using a National Database

Abstract Introduction Activity-Based Funding (ABF) is a new reimbursement model for public hospitals in Ireland. Little data exists regarding cost estimates for acute surgical admissions in Ireland. This study presents a novel method of direct-cost estimation for Testicular Torsion (TT) care in Ireland. Method Hospital In-Patient Enquiry (HIPE) data, covering all public hospital admissions in Ireland, were analysed to derive annual incidence rates for TT between 2009-2018. The monetary cost of each case was calculated by cross-referencing the Diagnosis-Related Group (DRG) with reference prices for inpatients and day-cases in public hospitals in 2019. Annual cost was adjusted using the Consumer Price Index for Health (Ireland) Results N = 1,746 patients under 25 years underwent orchidectomy or orchidopexy for TT between 2009 and 2018. The direct cost of TT care in public hospitals between 2009 and 2018 was €6,331,402. Costs increased 54% over 10 years, from €513,232 in 2009 to €788,700 in 2018 (2019 Euros). Just over two-thirds of cases (70%, n = 1,230) were reimbursed with public funding Conclusions This novel cost-estimation model may serve as a template for future direct-cost estimates for surgical interventions in Ireland. This will improve the accuracy of future economic evaluation for healthcare interventions in Ireland.

Construction project cost estimation model cost dependent on multi-objective fuzzy optimization calculation

With the development of the social economy, the number and scale of construction projects are also growing. How to control construction project costs has become the key to investment and construction. Based on the multi-objective fuzzy optimization computing, the completed project investment data in the actual project progress is used for empirical analysis of a construction project using the cost prediction model.

Techno-economic feasibility analysis of zeotropic mixtures and pure fluids for organic Rankine cycle systems

• Optimization of net present value of ORC units using mixtures and pure fluids. • Zeotropic mixture R1234yf/i-butane (53/47) reaches highest net present value. • Sensitivity analysis employed for comparing R1234yf/i-butane and R1234yf. • Result robust to variations in fluid cost, electricity price and cost models. In this paper the techno-economic feasibility of employing zeotropic mixtures as working fluids in organic Rankine cycle power systems is assessed. For an application case based on the utilization of low temperature geothermal heat at 135 °C, the net present value of the organic Rankine cycle system was maximized by optimizing the process variables and heat transfer equipment design parameters. Parametric studies detailing the effect of the assumed electricity price and the employed equipment cost estimation models were carried out. The working fluids propane, i-butane, i-pentane, R1234yf, and their mixtures were selected for investigation, since they have shown promising performance in previous studies. The results suggest that the highest net present value (43.1 M€) is reached by the mixture R1234yf/i-butane (53.3/46.7), which is 2.1 M€ higher compared to the most economically feasible pure fluid R1234yf for an electricity price of 0.252 €/kWh. The results of the sensitivity analyses suggest that the techno-economic benefit of using the mixture is robust to variations in the electricity price, the cost of the working fluid, and the equipment cost estimation models. In the comparison of R1234yf based mixtures to propane based mixtures, the inclusion of model for estimating the turbine efficiency is assessed to have a relevant influence on the results. The results also indicate that the R1234yf/propane, R1234yf/i-butane, and R1234yf/i-pentane result in lower cooling water flows and thereby lower cooling tower costs compared with R1234yf.

Developing a preliminary cost estimation model for tall buildings based on machine learning

ABSTRACT The last half-century has witnessed an astronomical rise in the number of tall building projects in urban centers globally. These projects however frequently experience delays and total abandonment due to economic reasons. This study presents the application of Machine Learning techniques in the systematic development of a model to estimate the preliminary cost of tall building projects. The techniques considered include Multi-Linear Regression Analysis (MLRA), k-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Multi Classifier Systems. Twelve models were developed and compared using standard performance metrics. The results revealed that the best performing model was based on a Multi Classifier System using KNN as the combining classifier, with a Correlation Coefficient (R2) of 0.81, Root Mean Squared Error (RMSE) of 6.09, and Mean Absolute Percentage Error (MAPE) of 80.95%. This research showed the potential of modern digital technologies such as machine learning to solve problems of the construction industry. The procedure described in this study is of significant value to research and practice in the development of preliminary cost estimation models. The developed model can function as a decision support tool in the preliminary cost estimation stage of tall building projects.

A generic approach to evaluate costs and effectiveness of agricultural Beneficial Management Practices to improve water quality management

Nutrient export from agricultural areas is among the main contributors to water pollution in various watersheds. Agricultural Beneficial Management Practices (BMPs) are commonly used to reduce excessive nutrient runoff and improve water quality. The successful uptake of BMPs not only depends on their effectiveness but also on their costs of implementation. This study conducts a set of cost-effectiveness analyses to help stakeholders identify their preferred combinations of BMPs in the Qu’Appelle River Basin, a typical watershed in the Canadian Prairies. The considered BMPs are related to cattle and cropping farms and are initially selected by agricultural producers in this region. The analyses use a water quality model to estimate the impact of implementing BMPs on nutrient export, and the cost estimation model to approximate the cost of implementing BMPs at tributary and watershed scales. Our results show that BMPs' effectiveness, total costs of implementation and costs per kilogram of nutrient abatement vary between tributaries. However, wetland conservation is among the optimal practices to improve water quality across the watershed. It is also found that the rates of BMP adoption by stakeholders can influence the effectiveness of practices in a large watershed scale, which highlights the importance of stakeholder engagement in water quality management. This type of analyses can help stakeholders choose single or a combination of BMPs according to their available budget and acceptable levels of reduction in nutrients.

Developing a cost-estimation model for work-related stress: An absence-based estimation using data from two Italian case studies.

Objectives:This paper discusses the development of a cost-estimation model for work-related stress based on psychosocial risk exposure and absence from work. It presents findings from its implementation and evaluation in two organizations in Italy, using national-level tools developed by the Italian Workers’ Compensation Authority (INAIL). It also provides recommendations for the development of similar cost-calculation methods in other countries.Methods:The cost-estimation model was based on the human capital approach using an indirect cost indicator: loss of productivity due to days of absence attributable to work-related stress. Furthermore, the population attributable fraction (PAF) epidemiological measure was used to calculate the impact of exposure to work-related stress on the basis of data collected through validated tools developed by INAIL and salary cost data.Results:The developed model was implemented and evaluated in two organizations, the first in healthcare (N=1014) and the second in public administration (N=534). In the first case, it was found that absence related to work-related stress cost the organization €445 000. In the second case, the cost was €360 000.Conclusions:The proposed model provides an example of how organizations can incorporate well-established indicators associated with work-related stress (eg, various types of absence, psychosocial risk perception, loss of productivity on the basis of salary costs) in a practical way in cost estimations of work-related stress. Such cost estimation can be applied in other countries and organizations to establish the economic and business case of managing work-related stress.

Safety Cost Estimation of Building Construction with Fuzzy Logic and Artificial Neural Network

The number of cases of construction work accidents continues to increase every year. To prevent work accidents, especially the fatality rate, Occupational Health and Safety (OHS) must be implemented in the construction project. Implementation of OHS can be a success if the availability of the budget is allocated explicitly for implementing OHS in construction projects. However, respondents’ current OHS budget is currently insufficient when referring to the guidelines regarding OHS costs applied in Indonesia. This condition will increase the initial budget and cause financial losses. So it is necessary to develop a cost estimation model that can estimate costs quickly and accurately. The results from this study will help estimator make OHS cost estimates quickly and accurately so that an estimator does not need a long time to do cost estimations at the beginning of the project, and the results of the estimated cost estimates are accurate. The fuzzy method uses to classify the contract value. The output of the fuzzy process will use as input in learning with the neural network. The Mean Absolute Percentage Error of tested data set for the adapted model is highly accurate (9.906%). The model obtained has a better MAPE value than the estimated cost by using regression analysis.

Transport Cost Estimation Model of the Agroforestry Biomass in a Small-Scale Energy Chain

The delivery of biomass products from the production place to the point of final transformation is of fundamental importance within the constitution of energy chains based on biomass use as a renewable energy source. Transport can be one of the most economically expensive operations of the entire biomass energy production process, which limits choices in this sector, often inhibiting any expansive trends. A geographic identification, through remote sensing and photo-interpretation, of the different biomass sources was used to estimate the potential available biomass for energy in a small-scale supply chain. This study reports on the sustainability of transport costs calculated for different types of biomass sources available close a biomass power plant of a small-scale energy supply chain, located in central Italy. To calculate the transport cost referred to the identified areas we used the maximum travel time parameter. The proposed analysis allows us to highlight and visualize on the map the areas of the territory characterized by greater economic sustainability in terms of lower transport costs of residual agroforestry biomass from the collection point to the final point identified with the biomass power plant. The higher transport cost was around €40 Mg−1, compared to the lowest of €12 Mg−1.

A method of activity-based software maintenance cost estimation for package software

This paper defines software maintenance activities and develops a model for maintenance cost estimation of package software. First, we classified software maintenance activities which were collected from the literature reviews. Second, we developed a cost structure for package software maintenance based on the identified activities. Third, the activity-based software maintenance cost estimation model was developed based on the software maintenance activities and cost structure. Fourth, we defined the adjustment factors for the software maintenance cost estimation model to improve the accuracy of the developed model. Finally, the developed model was verified using actual data from software maintenance projects. The proposed model showed reliable performance in terms of the mean magnitude of relative error and prediction quality. Therefore, the proposed model is suitable for setting budgets and predicting costs associated with package software maintenance projects.

Optimization of software cost estimation model based on biogeography-based optimization algorithm

Estimation of software cost (ESC) is considered a crucial task in the software management life cycle as well as time and quality. Prior to the development of a software project, precise estimations are required in the form of person month and time. In the last few decades, various parametric and non-algorithmic or non-parametric approaches regarding the estimation of software costs have been developed. Among them, the constrictive cost model (COCOMO-II) is a commonly used method for estimating software cost. To further improve the accuracy of this model, researchers and practitioners have applied numerous computational intelligence algorithms to optimize their parameters. However, accuracy is still a big problem in this model to be addressed. In this paper, we proposed a biogeography-based optimization (BBO) method to optimize the current coefficients of COCOMO-II for better estimation of software project cost or effort. The experiments are conducted on two standard data sets: NASA-93 and Turkish Industry software projects. The performance of the proposed algorithm called BBO-COCOMO-II is evaluated by using performance indicators including the manhattan distance (MD) and the mean magnitude of relative error (MMRE). Simulation results reveal that the proposed algorithm obtained high accuracy and significant error minimization compared to original COCOMO-II, particle swarm optimization, genetic algorithm, flower pollination algorithm, and other various baseline cost estimation models.

Dynamic cost analysis for disposal of low and intermediate level nuclear waste in China

The management and financial problems of nuclear waste have raised the concern worldwide due to lack of experience in decommissioning and disposal. In this paper, a primary cost estimation model is proposed based on Activity-based Life Cycle Costing (AB-LCC) approach in order to influence the strategy of Low and Intermediate Level Waste (LILW) disposal in China. AB-LCC model provides a dynamic cost estimation of LILW disposal and its application is not restricted by the region and time. A dynamic analysis of resources due to time and regional differences of LILW management during its life cycle time has been performed on the basis of Activity-based Costing (ABC) matrix. The static and dynamic costs of the primary and secondary activities at each stage of the disposal repositories are obtained. The model is verified by comparison with statistics from the cases of Aube repositories and difference is approximately less than 5%. Therefore AB-LCC model is used to estimate the LILW disposal costs in China with different disposal modes. Finally, LILW management strategy was suggested to the Chinese policy-makers that the Northwest Centralized Disposal Mode (NCDM) is more cost-saving than the Coastal Independent Disposal Mode (CIDM) and Gansu province is the most recommended disposal site for economic considerations.

An economic assessment framework for decommissioning of offshore wind farms using a cost breakdown structure

PurposeAs wind power generation increases globally, there will be a substantial number of wind turbines that need to be decommissioned in the coming years. It is crucial for wind farm developers to design safe and cost-effective decommissioning plans and procedures for assets before they reach the end of their useful life. Adequate financial provisions for decommissioning operations are essential, not only for wind farm owners but also for national governments. Economic analysis approaches and cost estimation models therefore need to be accurate and computationally efficient. Thus, this paper aims to develop an economic assessment framework for decommissioning of offshore wind farms using a cost breakdown structure (CBS) approach.MethodsIn the development of the models, all the cost elements and their key influencing factors are identified from literature and expert interviews. Similar activities within the decommissioning process are aggregated to form four cost groups including: planning and regulatory approval, execution, logistics and waste management, and post-decommissioning. Some mathematical models are proposed to estimate the costs associated with decommissioning activities as well as to identify the most critical cost drivers in each activity group. The proposed models incorporate all cost parameters involved in each decommissioning phase for more robust cost assessment.Results and discussionA case study of a 500 MW baseline offshore wind farm is proposed to illustrate the models’ applicability. The results show that the removal of wind turbines and foundation structures is the most costly and lengthy stage of the decommissioning process due to many requirements involved in carrying out the operations. Although inherent uncertainties are taken into account, cost estimates can be easily updated when new information becomes available. Additionally, further decommissioning cost elements can be captured allowing for sensitivity analysis to be easily performed.ConclusionsUsing the CBS approach, cost drivers can be clearly identified, revealing critical areas that require attention for each unique offshore wind decommissioning project. The CBS approach promotes adequate management and optimisation of identified key cost drivers, which will enable all stakeholders involved in offshore wind farm decommissioning projects to achieve cost reduction and optimal schedule, especially for safety-critical tasks.