Cost estimation: Strategic formulation based on affecting factors in government infrastructure

Identification of fuzzy models of software cost estimation

Accurate cost estimates are crucial for assessing the commercial viability and business case of well intervention projects, considering the limited available resources. This study presents the development of a comprehensive cost estimation model tailored specifically for rigless well intervention projects. We used the probabilistic approach to develop a cost estimation model for well intervention to achieve the research objective. The developed cost model was transformed into a computer program using pseudocodes in the C-Sharp programming language. The costs of well interventions performed on five (5) wells were used to validate the well intervention cost estimation software. We compared the cost estimation results using the Well Intervention Cost Estimation software and an existing deterministic cost estimate. For every cost estimate that the software generates, three probabilistic values are calculated (P90, P50 and P10). The cost-estimating application resulted in a higher cost than the deterministic estimate because it accounted for project uncertainties. As a result of implementing this cost estimation model, oil and gas industry cost analysts can optimize resource allocation, improve project planning, and mitigate financial risks associated with well interventions, thus improving operational efficiency and profitability.

Software cost and time estimation is the process of estimating the cost and time required to develop a software system. Software cost and time estimation supports the planning and tracking of software projects. Effectively controlling the expensive investment of software development is one of the important issues in software project management. Estimating software development cost with high precision is still a great challenge for project managers, because it allows for considerable financial and strategic planning. Software cost estimation refers to the predictions of the likely amount of effort, time, and staffing levels required to build a software system. A very helpful form of cost estimation is the one made at an early stage during a project, when the costing of the project is proposed for approval. However, estimates at the early stages of the development are the most difficult to obtain. In this paper a novel Constructive Cost Model (COCOMO) based on soft computing approach is proposed for software cost estimation. This model carries some of the desirable features of neural networks approach, such as learning ability and good interpretability, while maintaining the merits of the COCOMO model. Unlike the standard neural networks approach, the proposed model can be interpreted and validated by experts, and has good generalisation capability. The model deals effectively with imprecise and uncertain input and enhances the reliability of software cost estimates. From the experimental results, it was concluded that, by the proposed neural network model, the accuracy of cost estimation can be improved and the estimated cost can be very close to the actual cost.

Cost estimating has been acknowledged as a crucial component of construction projects. Depending on available information and project requirements, cost estimates evolve in tandem with project lifecycle stages; conceptualisation, design development, execution and facility management. The premium placed on the accuracy of cost estimates is crucial to producing project tenders and eventually in budget management. Notwithstanding the initial slow pace of its adoption, Building Information Modelling (BIM) has successfully addressed a number of challenges previously characteristic of traditional approaches in the AEC, including poor communication, the prevalence of islands of information and frequent reworks. Therefore, it is conceivable that BIM can be leveraged to address specific shortcomings of cost estimation. The impetus for leveraging BIM models for accurate cost estimation is to align budgeted and actual cost. This paper hypothesises that the accuracy of BIM-based estimation, as more efficient, process-mirrors of traditional cost estimation methods, can be enhanced by simulating traditional cost estimation factors variables. Through literature reviews and preliminary expert interviews, this paper explores the factors that could potentially lead to more accurate cost estimates for construction projects. The findings show numerous factors that affect the cost estimates ranging from project information and its characteristic, project team, clients, contractual matters, and other external influences. This paper will make a particular contribution to the early phase of BIM-based project estimation.

The main challenge within the development of enormous and sophisticated projects comes is that the cost estimation with a lot of accuracy. Several estimation models are introduced within the course of your time, that concludes that software cost estimation isn't precise and new ways or models ought to be planned fairly often. The business of software ought to be economical. Due to fast amendment in technology, implementation of complicated software systems at cheaper cost and also the urge to take care of higher quality software are a number of the foremost challenges for the software firms. One among the toughest works is cost estimation, within the field of software engineering. It's the estimation of total cost needed in developing software. An in depth summary of existing software cost estimation techniques or models are given by this model. The models are majorly classified in two type's algorithmic and non-algorithmic models. Key think about the event of recent software is that the choice of the appropriate cost estimation model and it additionally depicts the strengths and weakness of varied cost estimation models. Researchers have planned numerous ways of cost estimation. This paper offers associate insight into the varied models and techniques utilized in estimating cost of the software. The advantages and disadvantages of the present cost estimating techniques are highlighted during this paper. There is as such not any single methodology which may be considered the simplest methodology thus during this paper it's urged that a mix of the ways ought to be accustomed get an correct cost estimate.

PurposeThe Government’s investment in infrastructure projects is considerably high, especially in bridge construction projects. Government authorities must establish an initial forecasted budget to have transparency in transactions. Early cost estimating is challenging for Quantity Surveyors due to incomplete project details at the initial stage and the unavailability of standard cost estimating techniques for bridge projects. To mitigate the difficulties in the traditional preliminary cost estimating methods, there is a requirement to develop a new initial cost estimating model which is accurate, user friendly and straightforward. The research was carried out in Sri Lanka, and this paper aims to develop the artificial neural network (ANN) model for an early cost estimate of concrete bridge systems.Design/methodology/approachThe construction cost data of 30 concrete bridge projects which are in Sri Lanka constructed within the past ten years were trained and tested to develop an ANN cost model. Backpropagation technique was used to identify the number of hidden layers, iteration and momentum for optimum neural network architectures.FindingsAn ANN cost model was developed, furnishing the best result since it succeeded with around 90% validation accuracy. It created a cost estimation model for the public sector as an accurate, heuristic, flexible and efficient technique.Originality/valueThe research contributes to the current body of knowledge by providing the most accurate early-stage cost estimate for the concrete bridge systems in Sri Lanka. In addition, the research findings would be helpful for stakeholders and policymakers to propose policy recommendations that positively influence the prediction of the most accurate cost estimate for concrete bridge construction projects in Sri Lanka and other developing countries.

Background: The accuracy of the cost estimate is a key success factor for any construction project. It is the base for an effective tendering process. It can also be considered as the cornerstone of the cost control process. Objective: This paper aims to develop a model that can be used to assess the expected cost estimating accuracy of construction projects. This model is named as Construction Cost Estimate Accuracy Index (CCEAI). Methods: A questionnaire survey that contains fifteen factors clustered into four categories was carried out among 90 experts based on the construction cost estimate. Only sixty-six questionnaires were returned. The Analytical Hierarchy Process (AHP) was used to identify the relative weights of the different cost estimates. Results: The questionnaire results were analyzed using the AHP technique to calculate the relative weight for each of the input factors and categories. A Construction Cost Estimating Accuracy Assessment model (CCEAI) was developed based on the calculated relative weights. Then, three projects were used as case study applications to check the validity of the proposed model. The results showed that the CCEAI model is greatly reliable in predicting the expected accuracy of the cost estimate. Conclusion: The results of this research and the developed model are very important and can be considered as a powerful tool to predict and improve the expected accuracy of any future construction cost estimate.

Global Software Development (GSD) has grown in popularity as important tool for ensuring the efficient use of resources in internationally distributed environments across multiple geographical locations for business. The most prominent features of GSD are lowering costs, speeding up growth, and gaining access to talented developers all over the world. However, there are a number of drawbacks that result from the distance between development teams including coordination and communication causing the hidden business costs involved in development process. In the context of GSD, it is necessary to focus on economic cost estimation models as estimating the needed resources and effort remains a difficult task. Software cost estimation has become a critical factor in determining software development effectiveness economically. There are many cost estimating models, including algorithmic, non-algorithmic, and hybrid for business development environment. Over the last decade, several studies have focused on economic cost estimate in GSD. To the best of our understanding, current cost estimation techniques/models do not take into account all the additional business cost drivers that are necessary to calculate accurate cost estimation in the GSD context. In this paper we present a comparative analysis of economic software cost estimation techniques along with cost divers used in GSD context. This paper summarizes the additional cost drivers in GSD and discusses open research topics in economic cost estimation in GSD based on the results review of the associated literature.

The accurate cost estimate is one of the success keys to construction projects. The influence of inaccurate cost estimation on construction projects is critical. There is no consensus among researchers about the most important factors influencing the cost estimate accuracy. The study’s objective is to get an accurate cost estimate because the precise cost estimate keeps all parties focused on delivering the project within the budget. This objective is achieved by collecting the influential factors from the literature review and creating a powerful arithmetical model of cost estimation. Twenty-nine factors were collected by reviewing many researches. A questionnaire was prepared and then was examined by fourteen project managers. Pareto technique was conducted to get the foremost influential factors. Based on the Pareto technique, the 29 factors were reduced to nine. Then, a powerful arithmetical model was created in this study to get an accurate cost estimate. Finally, fourteen finished projects were selected to be case studies. The cost variance percentages were between 1% and 15% for each case study. By using the model, the estimated contract value of each case study was recalculated and, the cost variance percentages were between 0.5% and 0.8% for each case study.

Review of hardware cost estimation methods, models and tools applied to early phases of space mission planning

A Novel Soft Computing Model to Increase the Accuracy of Software Development Cost Estimation

Governments worldwide are introducing “reference class forecasting” to improve the accuracy of megaproject cost estimation and thus ultimately the ability to deliver megaprojects on budget without altering the project specifications and/or changing the time schedule. In contrast to current findings, which show that reference class forecasting leads to more accurate project cost estimates by counteracting human cognitive and organizational biases, this article indicates the contrary, that reference class forecasting doesnotlead to more accurate cost estimates. The article theorizes that reference class forecasting fails to produce more accurate project cost estimates because estimates are always a relational network effect of human and nonhuman actors’ “biased” efforts to establish them. This finding challenges the existing literature by pointing to a more complex understanding of project cost estimation and biases. The finding is based on a longitudinal case study of a 23.6‐billion‐kroner Danish public megaproject, which failed to meet its objectives despite the application of reference class forecasting.

Planning level tunnel cost estimation based on statistical analysis of historical data

Evaluation of different statistical techniques for developing cost correlations of micro hydro power plants

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.