Accurate Cost Estimation Research Articles

Enhancing cost estimation performance through effective organizational control: the moderating role of project complexity in construction projects

Accurate cost estimation is important for the success of any project, particularly in complex industries like construction. However, many organizations fail to recognize the importance of good cost estimation performance. Previous research has identified various factors that impact cost estimation performance, with most under organizational controls, including input, behaviour, and output controls. Despite this, the causal relationships between these controls and cost estimation performance have not been thoroughly explored, especially from the perspective of diverse project complexities. To address this gap, this paper aimed to evaluate the effect of organizational controls on cost estimation performance, including the moderating effect of project complexity. Data were collected from 152 respondents in Malaysian construction firms, and a Partial Least Squares Structural Equation Modelling (PLS-SEM) method was used for data analysis. This paper reveals that all organizational controls significantly influenced cost estimation performance dimensions, except for one relationship. Additionally, only three moderating effects were found to be significant. These results highlight the importance of effective organizational controls in achieving better cost estimation performance across various levels of project complexity. By implementing the right control modes, organizations can improve their cost estimation performance and enhance project success.

Systematic literature review on global software development based software cost estimation models and cost drivers

<p>Global software development (GSD) is a well - established discipline of software engineering that focuses on the advantage of a global environment. Effective cost estimation is critical for the success of GSD projects. Cost estimation in a GSD environment is a challenging task. As a re sult, GSD must emphasize cost estimation. Findings show that a number of researchers over the past few decades have emphasized GSD - based cost estimation in GSD; to the best of our knowledge, however, existing cost estimation have not taken into account man y GSD - based cost drivers that must be considered when estimating costs. Motivated by all this, the purpose of this study is to review the existing GSD - based cost estimation models/techniques and cost drivers that influence the accuracy of cost estimation. To identify and compile relevant research papers, a systematic literature review was carried out. From twenty - seven selected studies, initially, 86 GSD - based cost drivers and 12 GSD - based cost estimation models/techniques were extracted. After filtration, 26 cost drivers were identified as significant and to be considered in GSD - based cost estimation. This study significantly identifies GSD - based cost drivers and existing cost estimation techniques.</p>

Environmental Damage of Different Waste Treatment Scenarios by Considering Avoided Emissions Based on System Dynamics Modeling

This study aims to develop a comprehensive model for life cycle assessment and environmental damage cost calculations considering avoided emissions in different waste management scenarios using the system dynamics (SD) approach. Our analysis reveals that under the business-as-usual (BAU) scenario for the period 2020–2050, the total net greenhouse gas (GHG) emissions reach 12.5 Mt, with the highest environmental damage cost being USD 689 million. In contrast, an integrated management strategy encompassing recycling, composting, anaerobic digestion, and incineration results in a 195% reduction in net GHG emissions compared to the BAU Scenario. Concurrently, the environmental damage cost drops to USD 277 million, incorporating USD 347 million in savings, leading to a net environmental damage cost of USD −71 million. The findings affirm that accounting for emissions avoided across various treatment methods offers a more accurate estimate of environmental damage costs. Additionally, policies centered on integrated waste management are more likely to achieve sustainability. The study also demonstrates the utility of the SD approach in providing a holistic view of waste management systems and in evaluating the effectiveness of various policy strategies for sustainable waste management.

Cost Estimation of Manufacturing Enterprises based on BP Neural Network and Big Data Analysis

The manufacturing industry is the pillar industry of modern industry, and the cost estimation of manufacturing enterprises is an important management means of the manufacturing industry. Aiming at the cost estimation problem of manufacturing enterprises, this research proposes a cost estimation method based on Back Propagation (Back Propagation) neural network and big data analysis. In the process, the Lambda architecture was used to construct the big data analysis architecture of manufacturing enterprises, the K-means clustering algorithm was introduced for data clustering, and then the genetic algorithm was combined with the Back Propagation neural network to estimate the cost. In the estimation accuracy test, the accuracy of the research method can reach 94.7% after 240 iterations; in the calculation time test, the calculation time of the research method is 403 Ks when the data size is 500 Gb in a large-scale data set; in the call data volume test, the call data volume of the research method is 164 Kb when the research method is carried out to the seventh step in the small-scale data set; when the application analysis is carried out, the research method completes accurate cost estimation for 9 target parts. This research method has good model performance and calculation accuracy, and can effectively estimate manufacturing enterprises’ costs.

The value of advanced flood models, damage costs and land use data in cost-effective climate change adaptation

Damage costs from coastal flooding are high and are expected to increase further due to rising sea levels. The associated risks suggest a need for developing methodologies and models to assess damage costs in coastal areas as a basis for decision-making on climate change adaptation. The accuracy of damage cost estimates is critical in cost-effective decision-making on adaptation measures, reflecting climate hazards, flood levels, and associated damage costs. However, detailed models require extended work compared to simpler models due to demands on input data, including land use, damage costs, and adaptation options and costs. In the paper, varying levels of resolution, damage costs and flood models are assessed for coastal flooding risks for two Danish urban areas. Two damage cost models are used: a detailed, context-specific model using local high-resolution land use- and flood damage cost data and a less detailed model with lower-resolution land use data and damage costs based on generic European cost curves. Two flood models are used for the assessment using the detailed damage cost model based on a dynamic and a static flood approach, respectively. For the coarser model, only a static flood model is used. Significant differences are identified between the modelling approaches: The flood area varies up to a factor of ten for static floods due to the digital elevation map (DEM) model alone and a factor of 17 when changing both the DEM and using a dynamic flood. Corresponding factors for resulting damage costs are four and five, respectively.

Identification of an optimal method for echocardiographic right ventricular mass estimation in a swine model of pulmonary hypertension

Abstract Background Right ventricular (RV) mass and Fulton Index (FI) - the ratio of right to left ventricular mass - are useful markers of RV remodeling and predictors of mortality in pulmonary hypertension (PH). Cardiac magnetic resonance imaging provides an accurate estimation of RV mass but cost, availability and patient contraindications limit broad application. While echocardiography is universally available and offers feasible longitudinal assessments, current guidelines lack a standardized method for RV mass determination. Purpose We compared the performance of four previously described echocardiographic models of RV mass to estimate actual RV weight and FI measured post-mortem in a swine model of PH. Methods Echocardiography was performed in a Yorkshire pig model of post-capillary PH induced by pulmonary vein banding and healthy animals using a Philips iE-33 ultrasound system (n=70). RV mass was measured using the following models: trapezoid method (1), exact radius method (2), longitudinal plane method (3), and an algorithm (4) proposed by Kochav's group. Figure 1 depicts pertinent echocardiographic views and parameters. A two-dimensional area-length method was applied to estimate left ventricular mass and FI was calculated. On the day of echocardiography, pigs were euthanized, hearts were explanted, and ventricular weights were recorded. Pearson’s correlation, linear regression and Bland-Altman analysis were employed to evaluate different methods compared to recorded weights and Fulton Index. Results PH animals presented with significant RV remodeling and higher RV weights (42.4g, range 18.4-93.6g) compared to control pigs (30.5g, range 11.1-46.0g), allowing for examination of echocardiographic methods across a wide span of RV weights. FI ranged from 0.29 to 3.60 with a mean of 0.70. All methods demonstrated a strong linear relationship with post-mortem RV weight (r(1) = 0.88, r(2) = 0.88, r(3) = 0.84 r(4) = 0.82, p<0.0001). Bland-Altmann analysis revealed good agreement with recorded RV mass for trapezoid, exact radius and longitudinal plane method (bias(1) = 4.5±7.5g, bias(2) = -5.9±6.6g, bias(3) = -2.7±8.4g) while Kochav’s algorithm tended to underestimate RV weights (bias(4) = -18.5±8.7g). Figure 2 illustrates the performance of exact radius and longitudinal plane methods. Measured left ventricular mass correlated strongly with post-mortem left ventricular weight and reached good agreement (r = 0.91, p<0.0001; bias = -4.0±9.8g). FI was best approximated by exact radius (bias = -0.04±0.12) and longitudinal plane (bias = 0.01±0.18) methods. The trapezoid model tended to overestimate FI (bias = 0.10±0.11) and Kochav’s algorithm underestimated (bias = -0.23±0.12). Conclusion RV mass and FI can be successfully approximated using echocardiography in a clinically relevant porcine PH model. Longitudinal plane and exact radius methods performed best and can be suggested for evaluating RV mass in clinical PH patients.Figure 1Figure 2

Mutation-specific costs of advanced non-small cell lung cancer treatment.

14 Background: Novel immunotherapy and targeted therapies prolong survival, but are associated with substantial costs for patients with advanced non-small cell lung cancer (NSCLC). Because biomarker results can strongly impact treatment options, we explored the relation between biomarker results and NSCLC treatment costs. Methods: Using the Flatiron Health database, we conducted a retrospective cohort study to investigate the association between biomarker test results and medication costs in patients with newly diagnosed advanced NSCLC between 2011 and 2020. To be included, patients had to receive at least one biomarker testing. Patients with two or more actionable gene alterations, without structured/treatment data, or who participated in clinical trials were excluded. Patients with negative actionable gene alterations and no definitive PD-L1 results served as the reference group. We linked medication records to Medicare Spending by Drug Dashboard to calculate the first-year cost. We applied a coarsened exact matching approach to estimate the cost difference compared to the matched reference group. Results: A total of 32,008 patients with advanced NSCLC were identified. The mean first-year medication costs in the overall cohort increased from $29,283 in 2011 to $46,062 in 2020. The unadjusted mean first-year costs varied substantially, from $27,703 for patients with ALK+ NSCLC to $51,047 for patients with RET+ NSCLC ($34,520 for the reference group; details in Table). After coarsened exact matching, the mean medication costs for patients with MET+, RET+, BRAF+, PD-L1≥1%, or PD-L1<1% advanced NSCLC were significantly higher than that for the reference group, whereas patients with ALK+ or EGFR+ tumor had significantly lower mean medication costs than the reference group. There was no significant difference in costs between patients with NTRK+ or ROS1+ tumor and the reference group. Conclusions: Our economic evaluation found that medication costs for advanced NSCLC increased substantially over time and differed by biomarker status, highlighting the importance of using more accurate cost estimates in cost-effectiveness analysis of novel therapies to guide value-based practices. [Table: see text]

More Accurate Cost Estimation for Internet of Things Projects by Adaptation of Use Case Points Methodology

This paper adapts the Use Case Points method to estimate the size and development effort required for the Internet of Things systems. Despite the extensive use of UCP in software engineering, it has yet to be adapted for IoT systems, which is essential for project management and resource planning. Our proposed adaptation, UCP for IoT, is based on a four-layer IoT architecture and tailors the standard software UCP to the specifications of IoT systems. It was validated using a case study of three IoT systems, demonstrating its applicability and effectiveness in estimating the development effort required for IoT projects. However, the results also highlight the need for further improvements, particularly given the absence of historical data sets for IoT projects. Our future work will focus on gathering such datasets and further refining the proposed model.

Investigation of critical factors affecting cost overruns and delays in Egyptian mega construction projects

Construction projects are prone to cost overruns and delays that can significantly impact their profitability and quality. Identifying the root causes of these issues is critical to finding practical solutions. This study focuses on the Egyptian construction sector and identifies the top six factors that lead to cost overruns and delays in mega construction projects. These factors are inadequate project planning, poor communication and coordination, changes in project scope, lack of skilled labour, insufficient budget allocation, and payment delays. The study recommends strategies to mitigate these factors, such as involving all stakeholders in the planning process, using project management tools, and setting realistic timelines to improve project planning and scheduling. Regular meetings and clear communication channels can achieve effective communication and coordination. The study also suggests managing changes in project scope through effective change management processes and ensuring sufficient budget allocation by conducting accurate cost estimates and setting aside contingency funds. These recommendations can provide practical insights for practitioners and policymakers in the construction industry to mitigate the impact of cost overruns and delays. Overall, the study emphasises the importance of identifying and addressing the key factors contributing to cost overruns and delays in mega construction projects.

Performance, processing yields, and fillet composition of specific United States diploid and triploid rainbow trout (Oncorhynchus mykiss) lines reared in a semi-commercial scale freshwater recirculating aquaculture system

Large rainbow trout marketed as steelhead are gaining considerable attention for production in recirculating aquaculture systems (RAS). Diploid and triploid trout with varying genetics are available from several commercial suppliers; however, the performance and processing metrics of these lines have not been compared when raised in freshwater RAS. Current data on these outcomes could support more accurate cost and profitability estimates for potential RAS projects and optimize operation for existing farms. Accordingly, the objective of this study was to quantify performance metrics, processing yield, and fillet composition of six all-female rainbow trout lines (three diploid and three triploid) exposed to an artificial winter (12:12 light: dark at 40 g (Artificial Winter Photoperiod; AWP)) or continuous, 24-h light (No Winter Photoperiod; NWP) grown in a semi-commercial scale freshwater RAS to 3.5 kg. Overall, growth was impacted by line, ploidy, and artificial winter photoperiod. Diploid and AWP trout were smaller than triploids and NWP trout at stocking and 500 g. Mean diploid and triploid weight was similar at larger sizes (>1 kg); however, triploid performance compared to diploids varied by commercial supplier. Mortality was lower in lines from commercial supplier 1 than lines from commercial supplier 2 and the United States Department of Agriculture (14.4 ± 0.8 vs 27.5 ± 2.0 and 29.8 ± 4.3%, respectively). Head-on gutted and skin-on fillet yields were related with larger fish weight. Mature prevalence in diploids may have led to differences observed between diploids and triploids in fillet lipid (10.9 ± 0.8 vs 13.3 ± 0.6%) and moisture (67.0 ± 0.6 vs 65.1 ± 0.4%). The results provide guidance for rainbow trout RAS producers to make informed decisions on optimal harvest size targets and tailor their rainbow trout line or ploidy selection to those harvest sizes.

Improvement of pricing in the construction industry: Integrating information modeling technologies and transitioning regions to the resource index method

Ensuring accurate cost estimation for project implementation, while accounting for regional specifics, is a crucial task for pricing practices in the construction industry. To address this challenge, the transition from the base index method with cost item indexation to the resource index method for estimating project costs is planned in the 1st quarter of 2023. Additionally, starting from 2024, the mandatory use of information modeling technologies for project design will be implemented. This article examines the rationale behind implementing modifications in the pricing system, outlines the algorithm for estimating costs using the resource index method, identifies the advantages and challenges associate with this new approach, and highlights the prospects of integrating information modeling technologies. The study employs comparative and statistical analysis, as well as methods of synthesis and systematization of information. The analysis of data from the Federal State Information System of Pricing in Construction (TsS FSIS) focuses on regions that have already transitioned to the resource index method. On the basis of the research findings, the article reveals the issues related to the adoption of alternative cost estimation methods in a region. It also proposes approaches for enhancing the pricing system to effectively apply the resource index method for cost estimation in construction projects, while incorporating the use of information modeling technologies (IMT).

Machine Learning for Accurate Software Development Cost Estimation in Economically and Technically Limited Environments

Cost estimation for software development is crucial for project planning and management. Several regression models have been developed to predict software development costs, using historical datasets of previous projects. Accurate cost estimation in software development is heavily influenced by the relevance and quality of the cost estimation dataset and its suitability to the software development environment. The currently available cost estimation datasets are limited to North American and European environments, leaving a gap in the representation of other economically and technically constrained software industries. In this article, the authors evaluate the performance of regression models using the SEERA dataset, which highly represents these constrained environments. This study provides insights into selecting regression models for cost estimation in software development. It highlights the importance of using appropriate models based on the specific software development model and dataset used in the estimation process. In the performance evaluations of eight regression models, including elastic net, lasso regression, linear regression, neural network, RANSACRegressor, random forest, ride regression, and SVM, for cost estimation in different software models, along with correlation coefficients and accuracy indicators, were reported. The results showed that SVM and random forest indicated superior performance. However, the elastic net, lasso regression, linear regression, neural network, and RANSACRegressor models also demonstrated exemplary performance in cost estimation.

Evaluation of Probabilistic Project Cost Estimates

Evaluation of cost estimates should be fair and give incentives for accuracy. These goals, we argue, are challenged by a lack of precision in what is meant by a cost estimate and the use of evaluation measures that do not reward the most accurate cost estimates. To improve the situation, we suggest the use of probabilistic cost estimates and propose guidelines on how to evaluate such estimates. The guidelines emphasize the importance of a match between the type of cost estimate provided by the estimators and the chosen cost evaluation measure, and the need for an evaluation of both the calibration and the informativeness of the estimates. The feasibility of the guidelines is exemplified in an analysis of a set of 69 large Norwegian governmental projects. The evaluation indicated that the projects had quite accurate and unbiased P50 estimates and that the prediction intervals were reasonably well-calibrated. It also showed that the cost prediction intervals were noninformative with respect to differences in cost uncertainty and, consequently, not useful to identify projects with higher cost uncertainty. The results demonstrate the usefulness of applying the proposed cost estimation evaluation guidelines.

Cause of delay and cost overrun in infrastructure projects

This paper focused on identifies causes of cost overrun and delay factors in infrastructure projects. Mainly, a structured review of literature has been conducted for the purpose of knowing extend of time delay and cost overruns experienced by infrastructure projects. To know this issue this paper explored current state of cost overruns and delay in infrastructural projects. Principally, the reviewed of 15 empirical literatures demonstrated different causes of delay and cost overrun. Main findings proposed distinctive factors of cost overrun and delays, and the main of them; pitfalls and errors in design, economic and political instability, bureaucracy of owner, poor experience of contractors, late in payment to contractor for terminate work, incompetent contractor human resources, in accurate scheduling and initial cost estimation by contractor and recurrent design change.

SELECTING A STANDARD SET OF ATTRIBUTES FOR THE DEVELOPMENT OF MACHINE LEARNING MODELS OF BUILDING PROJECT COST ESTIMATION

Accurate cost estimation is a critical aspect of successful construction projects, and the application of machine learning offers promising advancements in this domain. However, to achieve reliable cost predictions, the selection of a standardized set of attributes that significantly influence model performance is essential. This research addresses the research gap by investigating the systematic clarification of a standard set of attributes for machine learning models in building cost estimation. Firstly, plenty of attributes were summarized by literature review, then by questionnaire surveying and focus group discussion of the Delphi study period, the final 68 ranked attributes were determined and formulated the attribute set of building data. The findings of this research are beneficial to improve the accuracy of estimation by providing the essence of developing a building cost estimation of machine learning because the domain researcher can refer to these listed attributes to determine the lay structure of a new model.

STUDY OF WORK UNIT PRICE PERMEN PUPR NUMBER 28 OF 2016 WITH PERMEN PUPR NUMBER 1 YEAR 2022 IN JU 1 RIVER EXCAVATION WORK

Construction cost estimation is a critical aspect of construction activities that requires in-depth attention. The cost estimation process, which relies on specifications and working drawings prepared by the project owner, should ensure efficient and accurate project execution with appropriate and measurable execution methods. However, in reality, as revealed in previous research, there are still weaknesses in considering nonconformances in specifications in selecting heavy equipment, determining optimal execution methods, and ensuring efficient workforce management, including work safety aspects. Therefore, it is important to choose an implementation method that suits the characteristics of the field to ensure that the resulting cost analysis is accurate and appropriate. In some cases, the Unit Price Analysis Method (AHSP), in some cases, still tends to be general in nature and has not fully taken into account the differences between the 2016 and 2022 AHSP, such as changes in the coefficients for labor, equipment and materials. The effect of this discrepancy is that the base unit price often does not reflect the actual situation at the project site, which can impact the accuracy of cost estimates. To overcome this challenge, more in-depth adjustments are needed in the AHSP method to ensure that construction cost estimates truly reflect actual field conditions. This step will enable the use of the AHSP method as a more relevant and accurate tool for predicting construction project costs, which will ultimately support more efficient and effective project management. Thus, industrial construction can maximize resource efficiency and reduce potential cost discrepancies in the field.

Overview of BIM contributions in the construction phase: review and bibliometric analysis

Building Information Modelling (BIM) is a revolutionary invention within the construction industry that essentially aids the design, construction and management of construction projects throughout their lifespan. Globally, the Architecture, Engineering, and Construction (AEC) industry has for decades progressively adopted and implemented BIM. While there are several papers in this context, none have tried to extensively document BIM’s comprehensive contributions and uses in the construction phase. Therefore, this paper aims to identify BIM’s various contributions and uses in the construction phase and analyze publication trends, co-occurring keywords, contributing authors and countries. A systematic overview approach was used to review published articles on state-of-the-art of BIM in construction, supported by bibliometric network mapping analysis. A total of 409 documents were extracted and analyzed. The study’s findings document BIM’s various uses and contributions to the AEC industry, such as simulation of each stage of the construction process, virtual presentation of the building and site, visualization of progress, management of construction work, enhancement of safety, communication and collaboration, quick generation of reliable and accurate cost estimates, assistance in the fast realization of return on investment (ROI), and serving as a platform that hosts and documents various technological tools used during the construction phase. The bibliometric analysis reveals the most contributing scholars, countries, document sources, trend network mapping of co-occurring keywords, and publication trends. The primary practical implications of this study’s discoveries can be exploited as a basis for further research and to influence the future direction of BIM in the AEC industry. The findings will enhance the wider spread, application and understanding of BIM in the AEC industry, thereby increasing BIM awareness and knowledge globally.

Digital transformation for cost estimation system via meta-learning and an empirical study in aerospace industry

It is increasingly challenging to estimate product cost to optimize the quotation decision for aerospace manufacturing companies owing to the diversity of designations, intermittent demands, complicated decision procedure between different functional departments, and knowledge gaps among the involved decision makers. There is a need of effective solutions to support digital transformation of manual approach in which it can expedite and enhance the accuracy of cost estimation by automating specific tasks and streamlining decision processes. Although a number of studies have been done to enhance the performance of forecasting models, little research has been done to address the interrelations between cost estimation model and the associated decisions for quotation. Focusing on realistic needs, this study aims to develop a digital cost estimation system by integrating data-driven methodologies, search engines, and rule-based decision mechanism based on domain knowledge for improving the accuracy of cost estimation and the effectiveness of quotation for revenue management. An empirical study was conducted in a global aerospace manufacturer in Taiwan for validation. The results have shown the practical viability for the proposed framework with better performance than conventional approaches. The developed solution has been implemented.

An optimized equation based on the gene expression programming method for estimating tunnel construction costs considering a variety of variables and indexes

Accurate cost estimation in tunneling is key to the project’s success. Such information is critical for the early conceptual and design phases when key choices must be made. Numerous variables influence the cost of tunnel construction, and limited information is available at the early stages of design when the possible use of tunnels is being studied. Therefore, there is a limited number of models at engineers’ disposal to develop a proper cost estimate for tunneling projects. This study aimed to offer a model for estimating the construction cost of drilling and blasting tunnels in the preliminary stage of a project. For this purpose, an optimized gene expression programming (GEP) method was used based on the study of 900 data points obtained from ten drilling and blasting tunnels, which were randomly split into the training (800 data points) and testing (100 data points) datasets. With the experience of previously constructed tunnels, eleven parameters were considered the most effective for the tunnel’s construction cost. The best fit on predictions generated an equation for the optimized GEP model. Finally, by comparing the equation’s outputs with the actual costs and its behavior with practice, its potential ability to estimate the construction cost of drilling and blasting tunnels was approved. Moreover, the Graphical User Interface (GUI) of the GEP model was created as a practical tool for estimating the construction cost of tunnels. This model can reduce tunnel uncertainties and give ML development in tunnel planning.

Cost estimation model of prefabricated construction for general contractors based on system dynamics

PurposeThe poor capacity of prefabricated construction cost estimation is the essential reason for the low profitability of the general contractor. Therefore, this study aims to focus on the cost estimation of prefabricated construction as the research object. This research aims to enhance the accuracy of total project cost estimation for general contractors, ultimately leading to improved profitability.Design/methodology/approachThis study used Vensim PLE software to establish a system dynamics model. In the modeling process, a systematic research review was used to identify cost-influencing factors; ABC classification and the analytic hierarchy process were used to score and determine the weights of influencing factors.FindingsThe total cost error obtained by the model is less than 2% compared with the actual value. It can be used to cost estimation and analysis. The analysis results indicate that there are 7 key factors, among which the prefabrication rate has the most significant impact. Furthermore, the model can provide the extreme range cost; the minimum cost can reduce by 13% from the value in the case. The factor's value can compose a cost control strategy for general contractors.Practical implicationsThe cost of prefabricated buildings can be estimated well, and deciding the prefabrication rate is crucial. The cost can be declined by correct cost control strategies when bidding and subcontracting are in process. The strategies can follow the direction of the model.Originality/valueA systemic, quantitative and qualitative analysis of cost estimation of prefabricated buildings for general contractors has been conducted. A mathematical model has been developed and validated to facilitate more effective cost-control measures.