Improve Cost Estimation Research Articles

Comparing data mining methods for predicting cost construction projects: A case study of cost management datasets from Thailand

This research examines three data mining approaches employing cost management datasets from 391 Thai contractor companies to investigate the predictive modeling of construction project failure with nine parameters. Artificial neural networks, naive bayes, and decision trees with attribute selection are some of the algorithms that were explored. In comparison to artificial neural network’s (91.33%) and naive bays’ (70.01%) accuracy rates, the decision trees with attribute selection demonstrated greater classification efficiency, registering an accuracy of 98.14%. Finally, the nine parameters include: 1) planning according to the current situation; 2) the company’s cost management strategy; 3) control and coordination from employees at different levels of the organization to survive on the basis of various uncertainties; 4) the importance of labor management factors; 5) the general status of the company, which has a significant effect on the project success; 6) the cost of procurement of the field office location; 7) the operational constraints and long-term safe work procedures; 8) the implementation of the construction system system piece by piece, using prefabricated parts; 9) dealing with the COVID-19 crisis, which is crucial for preventing project failure. The results show how advanced data mining approaches can improve cost estimation and prevent project failure, as well as how computational methods can enhance sustainability in the building industry. Although the results are encouraging, they also highlight issues including data asymmetry and the potential for overfitting in the decision tree model, necessitating careful consideration.

Avoiding Cost Overrun Through Stochastic Cost Estimation and External Quality Assurance

Cost overruns are a threat to project performance and continue to attract interest in both the popular media and the academic literature. Numerous studies from all continents have demonstrated that overruns remain prevalent in all industries. Although there are different suggestions as to what are the main causes of this problem, few studies have demonstrated what can be done to improve cost performance. This article provides evidence that improved cost estimation methodologies combined with external quality assurance can significantly reduce the extent of cost overruns in projects. The authors use data from 96 government projects in Norway, which implemented a quality assurance regime for large investment projects in the year 2000. The results show that cost performance was reasonably good. Only c. 25% of the projects subject to the regime experienced cost overruns. This suggests that by using proper cost estimation methodologies that are embedded in a governance framework that ensures that projects are subject to external scrutiny, the risks of cost overruns can be significantly reduced. The results should be encouraging for project owners who may have the impression that overrun is an unavoidable part of project delivery.

The lean journey of the finnish real estate.

Nothing teaches us more than our experiences. That was the main reason why the Finnish construction industry has not only studied Lean and IPD but also launched several pilot projects by public owners to learn, adapt Lean principles in practice, and develop an IPD model suitable for the Finnish construction industry. 
 The IPD model demands teamwork and adapting Lean and IPD industrywide is also teamwork. In Finland, several owners and service providers have been working together since 2010 sharing their learnings in workshops, seminars, and conferences. 
 Over 100 IPD projects have also been a platform for several academic papers. The Finnish Universities have published tens of master’s and doctoral theses during the past 10 years on Lean construction, IPD, and alliance models. 
 The Finnish construction industry has gained major benefits and made important progress on its Lean journey. Lean philosophy and tools have delivered tangible cost savings in projects and services and improved cost estimates, schedule adherence, quality, integration, and collaboration in construction projects. 
 Finland has succeeded in creating smooth and practical discussion and development forums that have shaped the whole industry. The key drivers contributing to this progress are collaboration and integration, confidence-building between the partners as well a deeper understanding and application of the Lean principles in construction. As a result, Lean and integrated project execution are already reflected in the strategies adopted by many companies and client organizations.

Technological and Economic Optimization of Wheat Straw Black Liquor Decolorization by Activated Carbon

Wheat straws are a globally abundant agro-waste that may play a critical role in the global transition from single-use plastics to green materials as an inexpensive and renewable raw material. Vast amounts of wastewater are produced during the technological process of wheat straw-cellulose/hemicellulose conversion. In this context, this work focuses on wastewater decolorization via activated carbon adsorption. A set of carefully planned experiments enabled the identification of a model that described the relationship between the system’s outputs and parameters. While process optimization is frequently connected with identifying process parameters that improve efficiency, this work employed a multi-objective optimization approach from both a technological and economic aspect. Nondominated sorting genetic algorithm versions II and III—NSGA-II and NSGA-III algorithms—were applied. As objectives, maximum efficiency and minimum cost per experiment were followed in different scenarios using pseudoweights and trade-off metrics. When optimizing only the efficiency, the results indicated a 95.54% decolorization yield, costing 0.1228 Euro/experiment, and when considering both the efficiency and cost, different solutions were obtained. The lowest cost was 0.0619, with a 74.42% decolorization. These findings indicate that incorporating an economic perspective into the optimization procedure can improve cost estimation and facilitate managerial decision-making.

A Machine Learning Approach for Automated Cost Estimation of Plastic Injection Molding Parts

Market competition leads to shorter cycle times for new or updated products. Therefore, flexibility in reacting to market changes, product development, and all related processes must be accelerated. In this regard, accurate cost estimation in the early stages of product development is critical for assessing the economic viability of a product. However, cost estimation requires data and expertise from several departments. Machine learning approaches could improve the accuracy and reduce the time needed for cost estimation. To investigate the eligibility of machine learning based cost estimation, a case study was conducted on an industrial company that produces plastic molding parts as key components of its products. The study involved training various supervised machine learning algorithms on a dataset of plastic injection molding parts using three different cost calculation methods. The three methods differed in the extent to which they considered the different process steps involved in the production of the parts. Different tree-based machine learning regression models and neural network models were trained to identify the most suitable approach for cost estimation in the given context. The results showed that tree-based machine learning algorithms outperformed neural networks and that individually predicting manufacturing parameters for cost calculation of each manufacturing process step leads to the most accurate cost estimation. This paper demonstrates how machine learning can support cost estimation in the early stages of the product lifecycle, reducing development times and improving cost estimation accuracy.

Integrating Building Information Modeling and Virtual Reality to Develop Real-time Suitable Cost Estimates using Building Visualization

The literature and previous research revealed that reducing the variation order by selecting the favorite material visually by the project's owner in the design stage is not available recently. As a problem, the cost estimates in real-time from walk-through interactions selecting multi-alternative building components are a potential threat to projects' success unless there is a visualization system helping to decide the costs of different building items and the items’ suitability. This research is a powerful tool that allows the project's owner to reduce the variation order by visually selecting his favorite material in the design stage. It expresses the application of Building Information Modeling (BIM) with Virtual Reality (VR) to improve Cost Estimation (CE) and the material selection process. The authors utilize BIM and virtual reality technology to enhance the visualization and processing of project cost estimation in the construction industry. In this study, the primary goal is to develop a BIM-VR-CE system that helps construction stakeholders to visualize and quickly to decide the costs of different building items and the items’ suitability. Accordingly, the authors developed a BIM-VR-CE system that integrates BIM and virtual reality technology using Unity 3D. Navigating building environments in real-time with the ability to select the cost and items suitable for elements of projects is now available to construction stakeholders through the BIM-VR prototype developed in this paper. To evaluate the effectiveness of the proposed preliminary model, a questionnaire was created, and 85 evaluations were collected. The findings of this paper show significant potential to utilize the proposed model in construction projects to deal with cost, reduce the variation orders, and select the favorite elements.

Optimizing Deep Learning Model for Software Cost Estimation Using Hybrid Meta-Heuristic Algorithmic Approach.

Effective software cost estimation significantly contributes to decision-making. The rising trend of using nature-inspired meta-heuristic algorithms has been seen in software cost estimation problems. The constructive cost model (COCOMO) method is a well-known regression-based algorithmic technique for estimating software costs. The limitation of the COCOMO models is that the values of these coefficients are constant for similar kinds of projects whereas, in reality, these parameters vary from one organization to another organization. Therefore, for accurate estimation, it is necessary to fine-tune the coefficients. The research community is now examining deep learning (DL) as a forward-looking solution to improve cost estimation. Although deep learning architectures provide some improvements over existing flat technologies, they also have some shortcomings, such as large training delays, over-fitting, and under-fitting. Deep learning models usually require fine-tuning to a large number of parameters. The meta-heuristic algorithm supports finding a good optimal solution at a reasonable computational cost. Additionally, heuristic approaches allow for the location of an optimum solution. So, it can be used with deep neural networks to minimize training delays. The hybrid of ant colony optimization with BAT (HACO-BA) algorithm is a hybrid optimization technique that combines the most common global optimum search technique for ant colonies (ACO) in association with one of the newest search techniques called the BAT algorithm (BA). This technology supports the solution of multivariable problems and has been applied to the optimization of a large number of engineering problems. This work will perform a two-fold assessment of algorithms: (i) comparing the efficacy of ACO, BA, and HACO-BA in optimizing COCOMO II coefficients; and (ii) using HACO-BA algorithms to optimize and improve the deep learning training process. The experimental results show that the hybrid HACO-BA performs better as compared to ACO and BA for tuning COCOMO II. HACO-BA also performs better in the optimization of DNN in terms of execution time and accuracy. The process is executed upto 100 epochs, and the accuracy achieved by the proposed DNN approach is almost 98% while NN achieved accuracy of up to 85% on the same datasets.

The moderating effect of project complexity on the relationship between organizational controls and cost estimation performance: A conceptual model

Cost overrun in construction projects is high and expected to rise. The improvement of cost estimation performance is vital as it provides a better chance for the construction projects to avoid cost blowouts. In this paper, an extensive literature review is conducted from online databases such as Web of Science, Scopus, and Google Scholar which focus on organization controls and its mechanisms to improve cost estimation performance in the construction industry. While many prior studies on cost estimation emphasized investigating cost estimation methods to improve the performance of cost estimation, less attention was given to organizational and project factors. Several studies have explored the effects of organizational control on performance but failed to include project complexity in the relationship between organizational controls and performance. Therefore, this paper conceptualizes the effect of organizational controls on cost estimation performance with project complexity as a moderator. This proposition suggests project complexity has advantages and disadvantages on organizational controls. Each control mechanism has its feature and effectiveness in high complexity projects. Hence, managers should select appropriate control mechanisms, such as input control and output control that have high effectiveness on the performance.

Fluid dynamics analysis and performance of polymer flow regulators for polymer flooding in multilayered reservoirs

Polymer flooding, a high viscosity solution of water and polymer, is introduced as an enhancement of oil recovery methods which is pumped through injector wells to push the liquid hydrocarbon towards production wells. Nowadays, the polymer injections method through layers for different flow admissions has allowed the improvement of heterogeneous flow distribution and the oil recovery factor itself. However, choke valves are needed to get the flow control in admission layers. Nevertheless, any flow obstruction affects the polymer viscosity via mechanical degradation. The shear stress in a standard orifice waterflood regulator breaks down the polymer molecule, causing viscosity loss, and hence reducing the recovery factor properties. In order to avoid shear degradation, several proposal nonstandard regulators of flow/pressure control options have been selected. In this work, experimental and computational studies were made to evaluate the design performance of pressure regulators to inject a polymer solution. The intermittent flow patterns concept has gotten a superior performance. The experimental data from this shock absorber has shown the average viscosity loss being less than 10% and pressure drop up to 34.5 bar with a polymer concentration of up to 700 mg/L, high flowrate around 89 m 3 /d and average salinity water was used to prepare the polymer solution. It was confirmed that the computational results have achieved a good match with experimental data. These results will validate regulator design, and improve cost estimation, saving materials and additional water treatment facilities. • Polymer flood regulator must be a static choke to guarantee the shear control. • There should be a specific choke for each flow-rate. • The shear rate is the main issue of polymer mechanical degradation. • High pressure drop does not induce directly the polymer degradation. • The fluid path longitude is an important issue to design a polymer flood regulator.

Improved Cost Estimates for Agricultural Conservation Practices

HighlightsDeveloped Useful Life Total Cost (ULTC) functions for 23 types of agricultural conservation practices.Derived each cost function from multiple ULTC estimates bracketing a range of design variations and sizes for each agricultural conservation practice.Developed Annual ULTC based on practice life cycle duration.Compared Prioritize, Target, Measure Application costs to ULTC.Recommend using ULTC rather than Prioritize, Target, Measure Application Environmental Quality Incentives Program payment as a cost surrogate. Abstract. The cost to achieve water quality goals is an essential piece of information necessary for assessing whether the expected societal benefits are worthy of investment.Within the United States, taxes generate the “public money” to pay to improve water quality. State and Federal Agencies distribute the public’s money to local governments and landowners as grants and cost-share to implement agricultural conservation practices (“practices”). Comparing the cost to improve water quality and the anticipated public benefit helps inform the investment decision.The lack of a robust method for estimating the cost of practices and developing a Water Quality Strategy hampers the ability to compare cost and benefits. Within Minnesota and North Dakota, Water Quality Practitioners commonly use the Prioritize, Target, Measure Application (PTMApp) to develop strategies to improve water quality. PTMApp utilizes the Environmental Quality Incentives Program payment as a surrogate to estimate practice cost. The Environmental Quality Incentives Program payment is a percentage of the estimated cost to implement a typical practice scenario, excluding the labor to plan, design and permit the practice; inspect the practice during construction; operate and maintain the practice; finance costs; and in most cases forgone income.We addressed the need for estimates of practice cost by developing Useful Life Total Costs (UTLCs) for 23 agricultural conservation practices. Useful Life Total Costs incurred throughout the practice life cycle begin with planning and end with reconstruction to maintain proper function. We developed multiple ULTCs (year 2020) for each practice by bracketing the range of design variations and sizes. Legacy PTMApp costs ranged from 1% to 55% of the UTLC, confirming underestimation of the actual practice costs.Cost functions developed by selecting the best-fit line between the ULTCs and a predominant practice physical characteristic are useful for developing Water Quality Strategies. The cost functions, recently incorporated into PTMApp, considerably improve the ability to estimate the actual cost to achieve water quality goals and societal benefits. Keywords: Benefits, Implementation, Life cycle, Planning, PTMApp, Useful life, Water quality.

COST ESTIMATION PERFORMANCE IN THE CONSTRUCTION PROJECTS: A SYSTEMATIC REVIEW AND FUTURE DIRECTIONS

Cost estimation in construction project is very critical for avoiding cost overrun in the project. This paper aims to provide a basis for improving cost estimation performance in construction through a systematic review of previous studies for last 31 years. The papers have identified a total of 238 construction cost estimation papers in 23 journals. Only 33 papers focused on factors influencing the performance of cost estimation. These papers were then analyzed, synthesized, and summarized in terms of the distribution across countries and citation influences. The factors of cost estimation performance were clustered to several themes with most of the factors in control themes. The factors in control themes are cluttered based on Organizational Control Theory (OCT). However, control themes provide less conceptual basis and dynamic to explain cost estimation performance and relationship among the factors. Therefore, this study reclassified other factors of cost estimation performance with reference to Contingency Theory (CT) and Task-Technology Fit Theory (TTFT). Hence, a new framework with relationship among the factors and cost estimation performance was developed. Further development and research of using the CT and TTFT frameworks were also discussed.

Technoeconomic Investigation of Amine-Grafted Zeolites and Their Kinetics for CO2 Capture.

Solid adsorbents with precise surface structural chemistry and porosity are of immense interest to decode the structure–property relationships and maintain an energy-intensive path while achieving high activity and durability. In this work, we reported a series of amine-modified zeolites and their CO2 capture efficiencies. The amine impregnated molecular zeolite compounds were characterized and systematically investigated for CO2 adsorption capacity through thermogravimetric analysis for the occurrence of atmospheric pure CO2 gas at 75 °C with diethylenetriamine (DETA), ethylenediamine (EDA), monoethanolamine (MEA), and triethanolamine (TEA)-loaded zeolite 13X, 4A, and 5A adsorbents. The kinetics of the adsorption study indicated that the adsorption capacity for CO2 adsorption was improved with amine loading up to a certain concentration over 13X-DETA-40, showing an adsorption capacity of 1.054 mmol of CO2 per gram of zeolite in a very short amount of time. The result was especially promising in terms of the initial adsorption capacity of zeolite, which adsorbed approximately 0.8 mmol/g zeolite within the first two minutes of experimentation. A detailed flow chart that includes a brief look into the process adopted for adsorption was included. Lagergren pseudo-first- and pseudo-second-order models of 40 wt % DETA zeolite 13X gave CO2 adsorption capacities of 1.055 and 1.058 mmol/g and also activation energies of 86 and 76 kJ/mol, respectively. The CO2 adsorption capacity of 13X-DETA-40 in a lab-scale reactor was found to be 1.69 mmol/g. A technoeconomic study was conducted for the solid amine zeolites to understand the investment per ton of CO2 adsorbed. This study was used as a basis to improve cost estimates from a microscale to a lab-scale reactor. The cost of investment for 13X-DETA-40 was reduced by 84% from $49,830/ton CO2 adsorbed in a microscale reactor to $7,690/ton of CO2 adsorbed in a lab-scale reactor.

Improving cost estimation in construction projects

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.

Sourcing Competition under Cost Uncertainty and Information Asymmetry

Driven by increasing costs in the traditionally regarded low‐cost manufacturing bases (e.g., China), many firms have started to outsource their production to the regions of even lower costs (e.g., Southeast Asia). However, a new environment may involve higher cost uncertainty and severer information asymmetry. Motivated by these observations, we consider a sourcing game where competing firms choose between a supplier with transparent certain cost (type‐C supplier) and a supplier with potentially lower but less transparent, uncertain cost (type‐U supplier). We characterize the equilibrium of the sourcing game and study how different parameters affect the firms’ sourcing strategy and profit performance. First, we find that due to information asymmetry, a large market size can make firms prefer the C‐supplier to the U‐supplier even if the latter has a lower average cost. Second, reducing the cost uncertainty or improving the signal accuracy of the U‐supplier does not necessarily make it more attractive to sourcing firms, which cautions the suppliers when making efforts to mitigate cost uncertainty or improve cost estimation. Third, higher competition intensity makes the diversified sourcing strategy more likely to be adopted under certain conditions. Interestingly, increasing the cost of the C‐supplier (e.g., a cost hike in China) may make both sourcing firms better off because it can lead to a new sourcing equilibrium. Finally, this study shows that the direction of quantity distortion under the optimal competitive mechanism differs from that under the traditional monopolistic setting.

An improved cost estimation for unit commitment using back propagation algorithm

The daily load is the main issue for many power plant industries that are affected by the varying maximum and minimum peak hours. Due to electricity being used less during the weekends, compared to weekdays, where the spending is higher. The same logic applies to day and night spending, which requires balancing among the units so that it can operate during high demand hours. The main problem is to determine the units that will be affected according to the operation schedule which means which unit, and for how long, will it stay on or off. In this context, the main objective for unit commitment, in general, minimizes the total cost of operating a unit, and at the same time maintain the constraints met. Several approaches and techniques used in existing studied, each have a solution for the optimal unit commitment problem. Some of the approaches presented, use complex methods in order to address the issues, while others use simple forms to do the same task. The problem of operation scheduling for unit commitment will be different depending on the type of industry, and according to the plan of mixing unit and operating constraints.

Development of automated information-search system of accounting of confisced cars

Purpose. To form an idea about the analyze the current state of automated banking systems, methods and tools for designing automated systems. The use of modern information technology has a cardinal effect and changes the business processes in banks, bringing them to a fundamentally different level. Banking technologies are inextricably linked with information technologies that provide comprehensive automation of business. The technique. Effective enterprise management in modern conditions is impossible without the use of computer technology. Information system - an interrelated set of tools, methods and personnel used for storing, processing and issuing information in order to achieve the goal. Results. In this study the current state of automated banking systems, methods and means of designing automated systems were analyzed. Scientific novelty. Improving cost estimation methods contributes to more efficient control of time and expenses in construction. Practical value. The development of computer technology and information technology allowed the creation of the majority of banks own computing complexes, on the basis of which were automated the main areas of banking activity. The deepening of the automation of the functioning of banking and other financial structures is accompanied by the improvement of the technology of banking operations and increased levels of controllability.

Artificial Neural Networks For Building Projects Cost Estimating

Purpose. To form an idea about the use of neural networks for estimating the cost of construction projects. Artificial neural networks are successfully used in solving numerous complex non-linear problems associated with forecasting, evaluation, decision-making, optimization, systematization, and choice in the fields of construction and its management. Artificial neural networks are particularly effective for solving complex problems, such as cost estimation problems, when the relationship between variables cannot be expressed by simple mathematical relationships. The technique. The parametric estimation method is a method in which the statistical evaluation between historical data and other variables is used for valuation. Using this method, you can get a more accurate estimate of the cost, due to the fact that this approach requires a lower level of detail compared to other methodologies. The level of accuracy of the assessment depends on the complexity, the amount of resources allocated for such work and the cost data embedded in the model. Results. Cost estimation is one of the most important factors in the management of construction projects. Any feasibility study for a project requires an accurate cost estimate in order to make the right decision about the future fate of the project. Scientific novelty. Improving cost estimation methods contributes to more efficient control of time and expenses in construction. Practical value. The use of artificial neural networks can potentially eliminate some of the main disadvantages of traditional evaluation methods. This gives great prospects for improving the reliability and validity of the method of parametric valuation.

Cost estimation accuracy in supply chain design

PurposeThe purpose of this paper is to investigate how management attention and supply chain complexity affect the decision-making process and cost estimation accuracy of supply chain design (SCD) decisions.Design/methodology/approachThe research follows an embedded case study design. Through the lens of the behavioural theory of the firm, the SCD decision process and realised outcomes are investigated through longitudinal data collection across ten embedded cases with varying degrees of supply chain decision-making complexity and management attention.FindingsThe findings suggest that as supply chain decision-making complexity increases, cost estimation accuracy decreases. The extent to which supply chain decision-making complexity is readily recognised influences the selection of strategies for information search and analysis and, thus, impacts resulting cost estimation errors. The paper further shows the importance of management attention for cost estimation accuracy, especially management attention based on conflicting goals induce behaviours that improve estimation ability.Research limitations/implicationsA framework proposing a balance between supply chain decision-making complexity and management attention in SCD decisions is proposed. However, as an embedded case study the research would benefit from replication to externally validate results.Originality/valueThe method used in this study can identify how supply chain complexity is related to cost estimation errors and how management attention is associated with behaviours that improve cost estimation accuracy, indicating the importance of management attention in complex supply chain decision-making.

Divide and conquer approach to quantum Hamiltonian simulation

We show a divide and conquer approach for simulating quantum mechanical systems on quantum computers. We can obtain fast simulation algorithms using Hamiltonian structure. Considering a sum of Hamiltonians we split them into groups, simulate each group separately, and combine the partial results. Simulation is customized to take advantage of the properties of each group, and hence yield refined bounds to the overall simulation cost. We illustrate our results using the electronic structure problem of quantum chemistry, where we obtain significantly improved cost estimates under very mild assumptions.

Improving the cost estimates of complex projects in the project-based industries

PurposeProject-based industries face major challenges in controlling project cost and completing within the budget. This is a critical issue as it often connects to the main objectives of any project. However, accurate estimation at the beginning of the project is difficult. Scholars argue that project complexity is a major contributor to cost estimation inaccuracies. Therefore, recognising the priorities of acknowledging complexity dimensions in cost estimation across similar industries is beneficial in identifying effective practices to reduce cost implications. Hence, the purpose of this paper is to identify the level of importance given to different complexity dimensions in cost estimation and to recognise best practices to improve cost estimation accuracy.Design/methodology/approachAn online questionnaire survey was conducted among professionals including estimators, project managers, and quantity surveyors to rank the identified complexity dimensions based on their impacts in cost estimation accuracy. Besides, in-depth interviews were conducted among experts and practitioners from different industries, in order to extract effective practices to improve the cost estimation process of complex projects.FindingsStudy results show that risk, project and product size, and time frame are the high-impact complexity dimensions on cost estimation, which need more attention in reducing unforeseen cost implications. Moreover, study suggests that implementing a knowledge sharing system will be beneficial to acquire reliable and adequate information for cost estimation. Further, appropriate staffing, network enhancement, risk management, and circumspect estimation are some of the suggestions to improve cost estimation of complex projects.Originality/valueThe study finally provides suggestions to improve cost estimation in complex projects. Further, the results are expected to be beneficial to learn lessons from different industries and to exchange best practices.