Conceptual Cost Research Articles

Conceptual Cost Estimate Method of Public Office Building Structural Frame Work by Regression Analysis

Conceptual Cost Estimate Method of Public Office Building Structural Frame Work by Regression Analysis

Data-Driven Insights on the Knowledge Gaps of Conceptual Cost Estimation Modeling

AbstractAlthough data modeling methods for conceptual cost estimation are proven to be effective in academia, they are not adopted by construction practitioners as expected. To understand this fact...

Comparison of Artificial Intelligence Techniques for Project Conceptual Cost Prediction: A Case Study and Comparative Analysis

Developing a reliable parametric cost model at the conceptual stage of the project is crucial for project managers and decision makers. Existing methods, such as probabilistic and statistical algorithms have been developed for project cost prediction. However, these methods are unable to produce accurate results for conceptual cost prediction due to small and unstable data samples. Artificial intelligence (AI) and machine learning (ML) algorithms include numerous models and algorithms for supervised regression applications. Therefore, a comparative analysis for AI models is required to guide practitioners to the appropriate model. The article focuses on investigating 20 AI techniques which are conducted for conceptual cost modeling, such as fuzzy logic model, artificial neural networks, multiple regression analysis, case-based reasoning, hybrid models, such as genetic fuzzy model, and ensemble methods such as scalable boosting trees (XGBoost) and random forest. Field canals improvement projects (FCIPs) are used as an actual case study to analyze the performance of the applied ML models. Out of 20 AI techniques, the results show that the most accurate and suitable method is XGBoost with 9.091% and 0.929 based on mean absolute percentage error and adjusted R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively. Nonlinear adaptability, handling missing values and outliers, model interpretation, and uncertainty have been discussed for the 20 developed AI models. In addition, this study presents a publicly open dataset for FCIPs to be used for future models' validation and analysis.

Pre-Design Cost Modeling of Road Projects

The conceptual cost estimation is a critical indicator of the future of the project's success at the initial process before submitting the project design. Pre-Design cost measuring is recognized as the very important procedure which affects the future of constructing proposed projects of roads. Pre-design cost is a preliminary cost measuring and is considered the prediction of the cost of a project during the planning and design phase. This evaluating and measure provides a great foundation for the process of financial support decisions and cost control. The aim of this study is to create pre-design cost measuring model for constructing road projects by utilizing linear regression technique. The research methodology consists of the data collected from public sectors which are the actual cost of the constructed road projects in Erbil governorate, then models have been developed by applying SPSS software, then the models summarized and selected. The mean absolute percentage error (MAPE) has been calculated using Excel program to calculate degree of the accuracy for the produced models. The accuracy was between -29% and +51%. As a result of the study regression models are useful and has a big advantage in predicting project cost in pre-design process and the planning stage of the project, by using either simplex computer scheme or any measuring tools.

Model for predicting cost of rural road projects in Thailand

Nowadays, construction cost plays an essential role in various projects, which are buildings, roads, railways, and bridges projects. Conceptual cost estimation in feasibility study is require high accuracy and less validation error especially in construction projects at early stages. The more improvement of estimation techniques, it would lead to the lesser problems of cost overrun, and extra expenses. This paper developed a cost estimation model for new constructed rural road projects. Considering the estimation methods for predicting the cost model, parametric method based on regression learner and NN method are applied. Previously, many researchers studied the cost applicable model by using various computer applications, so that this paper differed to compare these methods based on MATLAB. Accordingly, 44 road projects were compiled from DRR database, after that identifying the effective cost parameters referred on collected data. Subsequently, the data implementation process was focusing with regression learner based on automated regression training, and lastly, with NN toolbox concentrated on nftool for simulating the cost models. Another 7 road projects are tested with holdout validation process for these models. Due to this validation, we compare the predicted cost models within these methods. Finally, the developed models are reliable for not only the conceptual phase of future rural road projects but also the related construction fields can be recovered about the cost model creation.

Application of Decision Support Technology for Conceptual Cost Estimation

Conceptual cost estimates are often made at the beginning of the project when project scope is not yet well defined. Hence, predicting the conceptual costs on time, with high accuracy, presents a considerable challenge. One potential solution is to more effectively utilize historical data via integration with predictive analytical models. In this project, a decision support system was developed which predicts conceptual costs of construction projects and supports decision-making for long-term capital planning in public universities. The prototype system was developed based on historical data for roofing projects at the University of Alabama. We collected this historical data via a web-based data entry form subsystem. The developed system uses ridge regression models to train historical data. This system has a user-friendly interface and supports what-if analysis, allowing the user to see multiple scenarios of the estimation. The system also encompasses capabilities to forecast the effects of inflation on multi-year projects. Subsequent validation has demonstrated improvement in the resulting accuracy of the conceptual estimates.

Contact-Based Model for Epidemic Spreading on Temporal Networks

We present a contact-based model to study the spreading of epidemics by means of extending the dynamic message-passing approach to temporal networks. The shift in perspective from node- to edge-centric quantities enables accurate modeling of Markovian susceptible-infected-recovered outbreaks on time-varying trees, i.e., temporal networks with a loop-free underlying topology. On arbitrary graphs, the proposed contact-based model incorporates potential structural and temporal heterogeneities of the contact network and improves analytic estimations with respect to the individual-based (node-centric) approach at a low computational and conceptual cost. Within this new framework, we derive an analytical expression for the epidemic threshold on temporal networks and demonstrate the feasibility of this method on empirical data.

Polyurethane-carbon microfiber composite coating for electrical heating of concrete pavement surfaces

Electrically-heated pavements have attracted attention as alternatives to the traditional ice/snow removal practices. Electrically conductive polymer-carbon composite coatings provide promising properties for this application. Based on the concept of joule heating, the conductive composite can be utilized as a resistor that generates heat by electric current and increases the surface temperature to melt the ice and snow on the pavement surface. This research investigates the feasibility of applying an electrically conductive composite coating made with a Polyurethane (PU) binder and micrometer-scale carbon fiber (CMF) filler as the electrical heating materials on the surface of Portland cement concrete (PCC) pavements. PU-CMF composite coatings were prepared using different volume fractions of CMF, applied on the PCC surfaces, and evaluated in terms of volume conductivity, resistive heating ability, durability, and surface friction properties at the proof-of-concept level. A conceptual cost analysis was performed to compare this method with other heated pavement systems with respect to economic viability. Percolative behavior of CMF in PU matrix was captured and most desirable CMF dosage rates in terms of each performance parameter were investigated. Two percolation transition zones were identified for CMF in PU matrix at dosage rate ranges of 0.25–1% and 4–10%. The composites exhibited their most desirable performance and properties at CMF dosage rates greater than 10% and smaller than 15%.

Electrically conductive asphalt concrete: An alternative for automating the winter maintenance operations of transportation infrastructure

Hot mix asphalt (HMA) was modified with carbon fiber (CF) to achieve electrically conductive asphalt concrete with applications to pavement anti-icing and de-icing. The volume resistivity of such electrically conductive asphalt concrete was compared with that of electrically conductive asphalt mastic. Then, the temperature increase, the power consumption, and the snow melting capability of electrically conductive asphalt concrete were evaluated. Lastly, a conceptual cost estimation was performed based on data obtained from lab investigations, publicly available bid records, and the experience gained from the field implementation of a heated pavement system made of electrically conductive portland cement concrete.

Intelligent methodology for project conceptual cost prediction

Developing a reliable parametric cost model at the conceptual stage of the project is crucial for projects managers and decision makers. Several methodologies exist to develop a conceptual cost model. However, many gaps exist in the current methodologies such as depending only on experts ‘opinions and questionnaire survey to identify the project features, key cost drivers and developing deterministic predictive models without taking uncertainty nature into consideration. The main contribution of this study is developing an intelligent methodology for predicting the project cost at the conceptual stage. The proposed methodology can automatically identify key cost drivers and maintain uncertainty to predicted cost. Field canals improvement projects (FCIPs) are used as a case study to validate the proposed methodology. The selected methodology has applied quantitative approaches to identify the key cost drivers. In addition, the methodology has applied a genetic fuzzy model that automatically generates fuzzy rules to automatically predict the conceptual cost. Moreover, the results show a superior performance of the genetic fuzzy model than the traditional fuzzy model. In addition, this study presents a publicly open dataset for FCIPs to be used for future models validation and analysis.

Improved Global Sensitivity Analysis for Critical Factor Analysis with Intercorrelated Inputs: Power Plant Cost Example

AbstractGiven the importance of early conceptual cost estimating, it is important for the estimators to identify the most critical factors at the early phase of a project that drive the final cost....

Behind closed doors: the DC-10 and the demise of McDonnell Douglas

Theoretical basis This case has three primary objectives. First, it allows students to think through a conceptual cost and benefit analysis associated with the decision-making process in line with basic economic thinking. Students will revisit core concepts of marginal benefit vs marginal cost, the notion of opportunity costs and the role of sunk costs in this type of analysis, while also highlighting the nature of market structure, oligopolies and competition across firms in an industry. The second goal of this case is to consider the role of business ethics in the DC-10 case: specifically, to consider the potential influence of moral awareness and moral disengagement in unethical decisions made by McDonnell Douglas. Students will develop an understanding of these concepts and solidify their learning by applying them to the case and engaging in active discussion. Finally, the third goal of the case allows students to explore organizational culture and specifically offer recommendations for organizations thinking about the link between decision-making, the role of ethics and culture. Research methodology The technical reports released by the National Transportation Safety Board along with secondary data such as available public data such as news reports were used to round out the synopsis of the case study. Case overview /synopsis This case explores the accidents of two McDonnell Douglas DC-10s in the early 1970s at the onset of the jumbo jet race between Boeing, Lockheed and McDonnell Douglas. It explores the series of events during the “Windsor Incident” in 1972 and the subsequent accident over Paris in 1974. It explores the reasons why the cargo door on the DC-10 was faulty and subsequently why the door was not fixed. It examines the interplay of industry suppliers such as McDonnell Douglas and how they interact with oversight authorities such as the Federal Aviation Authority. The Teaching Note focuses on the economic thinking at McDonnell Douglas, behavioral ethics and organizational culture. Complexity academic level This case is best explored over a 90 min session but could be expanded to take up one 3 h session. The authors have used this case format in an undergraduate organizational behavior class, an MBA Leadership and Organizational Change class, and an MBA Economics of Managers class. It works particularly well in the MBA setting, as students with work experience can see the links between the mistakes made by McDonnell Douglas and their workplaces.

Conceptual Cost Modelling for Sustainable Construction Project Planning— A Levenberg–Marquardt Neural Network Approach

Conceptual Cost Modelling for Sustainable Construction Project Planning— A Levenberg–Marquardt Neural Network Approach

설계변경에 따른 철도교량 개략공사비 추정모델 개발 : PSC BEAM 중심으로

설계변경에 따른 철도교량 개략공사비 추정모델 개발 : PSC BEAM 중심으로

Predicting Conceptual Cost for Field Canal Improvement Projects

AbstractA conceptual cost estimation is prepared to assess the feasibility of a project or establish the project’s initial budget at the early stages of the project. The main objective of the paper...

Improving accuracy of conceptual cost estimation using MRA and ANFIS in Indonesian building projects

PurposeThe purpose of this paper is to develop a conceptual cost estimation (CCE) model for building project by using a pragmatic approach, which is a mix of tools drawn from multiple regression analysis (MRA) and adaptive neuro-fuzzy inference system (ANFIS), to improve the accuracy of cost estimation at an early stage.Design/methodology/approachThis paper presents a set of MRA and integrating MRA with ANFIS or MRANFIS. A simultaneous regression analysis was developed to determine the main cost factors from 12 variables as input variables in the ANFIS model. Cost data from 78 projects of state building in West Sumatra, Indonesia were used to indicate the advantages of the proposed model.FindingsThe result shows that the proposed model, MRANFIS, has successfully improved the mean absolute percent error (MAPE) by 2.8 percent from MRA of 10.7–7.9 percent for closeness of fit to the model data and by 3.1 percent from MRA of 9.8–6.7 percent for prediction performance to the new data.Research limitations/implicationsBecause the significant variables are different for each building type, the model may be not appropriate for other buildings depending on the characteristics of building. The models can be used and analyzed based on the own historical project data for each case so that the model can be applied.Originality/valueThe study thus provides better accuracy of CCE at an early stage for state building projects in West Sumatra, Indonesia by using the integrated model of MRA and ANFIS.

Cost estimate of platelet transfusion in the United States for patients with chronic liver disease and associated thrombocytopenia undergoing elective procedures

Aims: This study aimed to estimate the cost of platelet transfusion in patients with chronic liver disease (CLD)-associated thrombocytopenia undergoing an elective procedure in the United States.Materials and methods: The study was conducted in two parts: development of a conceptual framework identifying direct, indirect and intangible costs of platelet transfusion, followed by the estimation of the total cost of platelet transfusion in patients with CLD-associated thrombocytopenia before an elective procedure in the United States using the conceptual framework and cost data obtained from a literature search. The cost of the entire care required to raise a patient’s platelet count before the procedure was considered.Results: The final conceptual framework included the costs of generating the supply of platelets, the platelet transfusion itself, adverse events associated with platelet transfusion and refractoriness to platelet transfusion. When costs were accounted for in all the framework cost categories, the total direct cost of a platelet transfusion in a patient with CLD and associated thrombocytopenia was estimated to be in the range of $5258 to $13,117 (2017 US dollars) in the United States. The largest portion of costs was incurred by the transfusion event itself ($3723 to $4436) and the cost of refractoriness ($874 to $7578), which included the opportunity cost of a delayed procedure and subsequent platelet transfusions with human leukocyte antigen-matched platelets.Limitations and conclusions: Although we were unable to include all cost components identified in the conceptual framework in our total cost estimate, thus likely underestimating the true total cost, and despite the data gaps and challenges limiting our estimate of the full cost of a platelet transfusion in patients with CLD-associated thrombocytopenia undergoing an elective procedure in the United States, this study outlines a comprehensive conceptual framework for estimating the cost elements of a platelet transfusion in these patients.

The Modus Operandi of Urban Agriculture Initiatives

Urban agriculture (UA) has turned into a diverse and complex movement. Important challenges will be to set accurate expectations by civil society in relation to UA development, and to find ways to discuss UA in governance and collaboration networks from an aggregate point of view. However, analytical tools that allow comprehensive study of UA initiatives (UAIs) are absent. This article elaborates on a conceptual framework from the COST Action Urban Agriculture Europe (Prové et al. 2015) and evaluates findings that result from applying the framework to four UAIs. We found that, analytically, the framework generates in-depth information on UAIs, and argue that it can be a useful tool in networks that are responsible for collaboration, support, or governance within the UA movement. We also discuss its usability issues and discuss future research.

Residential buildings conceptual cost estimates with the use of support vector regression

Cost analyses, and the conceptual cost estimates among them, are of the key importance for the construction projects successes. Implementation of neural networks or machine learning methods provides broad possibilities for this specific type of cost. The aim of the paper is to present some results of the studies on the use of support vector regression as a machine learning tool for conceptual cost estimates of residential buildings. Results for three models based on support vector regression and radial basis kernel functions are introduced.

Estimasi Biaya Konseptual Pada Jembatan Beton Bertulang Dengan Metode Indek Biaya

&lt;p class="DRAbstrak"&gt;Cost estimation is the art of estimating the amount of cost required for an activity based on available information. The conceptual cost estimate is an early stage in planning a construction project. This estimate provides the cost that must be budgeted for a construction project. Cost conceptual estimates have low accuracy because the time of calculation and available information is limited. This study aims to obtain a conceptual model of the conceptual cost of short-spaced bridges. The method used is the cost index. The cost index is a figure indicating the cost per m2 of bridges at a given time. The required data are contract documents and drawings design that are built in 2012 - 2015 in Banyumas residency area. Span of bridge 4 - 38.8 meters and width of bridge 2 - 7 meters with caisson foundation. The data were obtained from Dinas Bina Marga and Public Works Agency. The results showed that the conceptual cost model of reinforced concrete bridge with caisson foundation was BJiL = (100.540.56t2-404.528.636,58t + 406.914.286.088,58) x P x W, with t = year, P = span bridge, and W = bridge width. The error value of validation of this model is 2.31%.&lt;/p&gt;