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Cost Engineering Techniques and Their Applicability for Cost Estimation of Organic Rankine Cycle Systems
The potential of organic Rankine cycle (ORC) systems is acknowledged by both considerable research and development efforts and an increasing number of applications. Most research aims at improving ORC systems through technical performance optimization of various cycle architectures and working fluids. The assessment and optimization of technical feasibility is at the core of ORC development. Nonetheless, economic feasibility is often decisive when it comes down to considering practical instalments, and therefore an increasing number of publications include an estimate of the costs of the designed ORC system. Various methods are used to estimate ORC costs but the resulting values are rarely discussed with respect to accuracy and validity. The aim of this paper is to provide insight into the methods used to estimate these costs and open the discussion about the interpretation of these results. A review of cost engineering practices shows there has been a long tradition of industrial cost estimation. Several techniques have been developed, but the expected accuracy range of the best techniques used in research varies between 10% and 30%. The quality of the estimates could be improved by establishing up-to-date correlations for the ORC industry in particular. Secondly, the rapidly growing ORC cost literature is briefly reviewed. A graph summarizing the estimated ORC investment costs displays a pattern of decreasing costs for increasing power output. Knowledge on the actual costs of real ORC modules and projects remains scarce. Finally, the investment costs of a known heat recovery ORC system are discussed and the methodologies and accuracies of several approaches are demonstrated using this case as benchmark. The best results are obtained with factorial estimation techniques such as the module costing technique, but the accuracies may diverge by up to +30%. Development of correlations and multiplication factors for ORC technology in particular is likely to improve the quality of the estimates.
Journal Article
Early Fast Cost Estimates of Sewerage Projects Construction Costs Based on Ensembles of Neural Networks
This paper presents research results on the development of an original cost prediction model for construction costs in sewerage projects. The focus is placed on fast cost estimates applicable in the early stages of a project, based on fundamental information available during the initial design phase of sanitary sewers prior to the detailed design. The originality and novelty of this research lie in the application of artificial neural network ensembles, which include a combination of several individual neural networks and the use of simple averaging and generalized averaging approaches. The research resulted in the development of two ensemble-based models, including five neural networks that were trained and tested using data collected from 125 sewerage projects completed in the Czech Republic between 2018 and 2022. The data included information relevant to various aspects of projects and contract costs, updated to account for changes in costs over time. The developed models present satisfactory predictive performance, especially the ensemble model based on simple averaging, which offers prediction accuracy within the range of ±30% (in terms of percentage errors) for over 90% of the training and testing samples. The developed models, based on the ensembles of neural networks, outperformed the benchmark model based on the classical approach and the use of multiple linear regression.
Journal Article
Predicting the trends and cost impact of COVID-19 OSHA citations on US construction contractors using machine learning and simulation
PurposeOccupational Safety and Health Administration (OSHA) of the U.S. government ensures that all health and safety regulations, protecting the workers, are enforced. OSHA officers conduct inspections and assess fines for non-compliance and regulatory violations. Literature discussion on the economic impact of OSHA inspections with COVID-19 related citations for the construction sector is lacking. This study aims to investigate the relationships between the number of COVID-19 cases, construction employment and OSHA citations and it further evaluates the total and monthly predicted cost impact of OSHA citations associated with COVID-19 violations.Design/methodology/approachAn application of multiple regression analysis, a supervised machine learning linear regression model, based on K-fold cross validation sampling and a probabilistic risk-based cost estimate Monte Carlo simulation were utilized to evaluate the data. The data were collected from numerous websites including OSHA, Centers for Disease Control and the World Health Organization.FindingsThe results show that as the monthly construction employment increased, there was a decrease in OSHA citations. Conversely, the cost impact of OSHA citations had a positive relationship with the number of COVID-19 cases. In addition, the monthly cost impact of OSHA COVID-19 related citations along with the total cost impact of citations were predicted and analyzed.Originality/valueThe application of the two models on cost analysis provides a thorough comparison of predicted and overall cost impact, which can assist the contractors to better understand the possible cost ramifications. Based on the findings, it is suggested that the contractors include contingency fees within their contracts, hire safety managers to implement specific safety protocols related to COVID-19 and request a safety action plan when qualifying their subcontractors to avoid potential fines and citations.
Journal Article
Effectiveness of Kaolinite with and Without Polyaluminum Chloride (PAC) in Removing Toxic Alexandrium minutum
Alexandrium spp. blooms and paralytic shellfish poisoning pose serious economic threats to coastal communities and aquaculture. This study evaluated the removal efficiency of two Alexandrium minutum strains using natural kaolinite clay (KNAC) and kaolinite with polyaluminum chloride (KPAC) at three concentrations (0.1, 0.25, and 0.3 g L−1), two pH levels (7 and 8), and two cell densities (1.0 and 2.0 × 107 cells L−1) in seawater. PAC significantly enhanced removal, achieving up to 100% efficiency within two hours. Zeta potential analysis showed that PAC imparted positive surface charges to the clay, promoting electrostatic interactions with negatively charged algal cells and enhancing flocculation through Van der Waals attractions. In addition, the study conducted a cost estimate analysis and found that treating one hectare at 0.1 g L−1 would cost approximately USD 31.75. The low KPAC application rate also suggests minimal environmental impact on benthic habitats.
Journal Article
Economic–Environmental Sustainability in Building Projects: Introducing Risk and Uncertainty in LCCE and LCCA
The aim of this paper is to propose a methodology for supporting decision-making in the design stages of new buildings or in the retrofitting of existing heritages. The focus is on the evaluation of economic–environmental sustainability, considering the presence of risk and uncertainty. An application of risk analysis in conjunction with Life-Cycle Cost Analysis (LCCA) is proposed for selecting the preferable solution between technological options, which represents a recent and poorly explored context of analysis. It is assumed that there is a presence of uncertainty in cost estimating, in terms of the Life-Cycle Cost Estimates (LCCEs) and uncertainty in the technical performance of the life-cycle cost analysis. According to the probability analysis, which was solved through stochastic simulation and the Monte Carlo Method (MCM), risk and uncertainty are modeled as stochastic variables or as “stochastic relevant cost drivers”. Coherently, the economic–financial and energy–environmental sustainability is analyzed through the calculation of a conjoint “economic–environmental indicator”, in terms of the stochastic global cost. A case study of the multifunctional building glass façade project in Northern Italy is proposed. The application demonstrates that introducing flexibility into the input data and the duration of the service lives of components and the economic and environmental behavior of alternative scenarios can lead to opposite results compared to a deterministic analysis. The results give full evidence of the environmental variables’ capacity to significantly perturb the model output.
Journal Article
Current Possibilities of Using BIM Models for Compiling Cost Estimates in the Design Phase of Residential Buildings
This article focuses primarily on the current possibilities of using data and information from BIM models to estimate costs using identified methods and pricing systems for apartment buildings with different construction technologies. The authors analyse buildings with a built-up space of 3600–5300 m3, representing hundreds of projects currently available on the market. The applied methods include Pricing of Buildings Using a Spreadsheet Program, IFC-Supported Pricing Software, Pricing of Buildings in Design Software, and Pricing of Buildings Using a Design/Construction Library to compile cost estimates in the Czech URS, German Baupreislexikon, and British Spon’s Architects’ and Builders’ Price Book pricing systems. The usability of the BIM model with respect to the selected pricing system, construction technology, and methods ranges from 50% to 85%, with labour intensity ranging from 64 to 159 h. The key aspects for a wider application of BIM models include the completion of standardization at the level of graphic and non-graphic requirements related to the intended use of the data and information. The average cost per cubic metre of built-up space is EUR 469 in the Czech Republic, EUR 617 in Germany, and EUR 671 in Great Britain. This study brings new and distinctive insights compared to previous research by providing specific values for labour intensity and extractability, defining the limits of BIM use for cost estimation, and proposing recommendations to increase the applicability of the obtained data in practice.
Journal Article
A Study of Factors Influencing the Compliance of Design Estimates at the Construction Stage of Residential Buildings
This article primarily addresses the factors affecting the possibility of achieving the costs estimated in the design stage of a building after its completion. The authors rely on an information base of twenty-three apartment buildings erected by twelve construction companies between 2017 and 2023, divided into two phases (2017–2020 and 2021–2023). The outputs of the article present the conclusions of several years of research into the identification of factors and risks affecting construction costs, capturing the development of price indicators over time, creating a realistic picture of working with costs from the building’s design stage during its execution and the application of sustainable and digitalization technologies within a selected segment of the building industry. The presented conclusions are based on statistical dependencies compiled using regression analysis to explore the relationships between the cost, time and technological parameters of selected buildings. These outputs provide an interesting and well-founded perspective on the obtained data, thus overcoming the lack of relevant methods, techniques and fitting algorithms for a sophisticated and long-term approach to pricing in the construction sector.
Journal Article
Evaluating Social Housing Retrofit Options to Support Clients’ Decision Making—SIMPLER BIM Protocol
The UK government made significant commitments to upgrading the energy efficiency of seven million British homes by 2020, aiming at reducing carbon emissions and addressing fuel poverty. One alternative to achieve better energy performance in existing houses is retrofit. However, there are difficulties associated with retrofitting social housing. It is currently challenging to compare scenarios (retrofit options) considering costs, potential energy efficiency gains, and at the same time minimising disruption to users. This paper presents a Building Information Modelling (BIM) protocol aimed to support decision making by social housing owners. It adopts BIM to simulate alternative retrofit options, considering: (a) potential reductions in energy consumption, (b) 4D BIM for retrofit planning and reduction of users’ disruption and (c) simulation of costs. A what-if scenario matrix is proposed to support decision making in the selection of social housing retrofit solutions, according to client and users’ needs. A case study of the retrofit of a mid-terrace house is presented to demonstrate the workflow. The main output of the work is the BIM protocol, which can support client decision making in diverse social housing retrofit projects, considering all three elements (energy simulation, planning for reduced disruption and cost estimation) in an integrated fashion. Such an integrated approach enables clients to make better informed decisions considering diverse social housing retrofit options through a simple process using readily available BIM technology.
Journal Article
Estimate final cost of roads using support vector machine
The cost overrun in road construction projects in Iraq is one of the major problems that face the construction of new roads. To enable the concerned government agencies to predict the final cost of roads, the objective this paper suggested is to develop an early cost estimating model for road projects using a support vector machine based on (43) sets of bills of quantity collected in Baghdad city in Iraq. As cost estimates are required at the early stages of a project, consideration was given to the fact that the input data for the support vector machine model could be easily extracted from sketches or the project’s scope definition. The data were collected from contracts awarded by the Mayoralty of Baghdad for completed projects between 2010–2013. Mathematical equations were constructed using the Support Vector Machine Algorithm (SMO) technique. An average of accuracy (AA) (99.65%) and coefficient of determination (R2) (97.63%) for the model was achieved by the created prediction equations.
Journal Article