Hazard-Based Analysis of Highway Project Development Times

Traditional Earned Value Management technique in project planning for effort and duration remains low to medium accurate. This study seeks to develop a highly reliable and efficient Integrated Earned Value Method (IEVM) to improve cost and duration prediction accuracy. This experiment compared the performance of five machine learning models across three different datasets and six performance indicators and verified the models with three other types of live project data. The results indicated that IEVM is a highly reliable, effective, consistent, and accurate machine learning-based method with a significant increase in accuracy over the conventional Earned Value Management technique. The finding pointed out a potential gap in the relationship between dataset quality and the performance of the ML model.

This paper presents the results of a detailed quantitative analysis of performance metrics of a sample consisting of 89 transportation projects sponsored by the World Bank. The sample and a subset consisting of 65 projects were evaluated using the performance metrics of project cost, schedule and scope. The effect of project size (dollar value) and project duration on performance metrics was investigated. Also, the achievement of project goals and potential improvement in planning and estimating over time (the learning effect) was studied. It was found that, in general, in transportation projects sponsored by the World Bank, costs are overestimated and schedules are optimistic. The outcome with respect to cost seems counter‐intuitive because previous work by other researchers had shown a systematic underestimation of project costs. There is significant evidence that there are no efficient controls in place to predict or prevent schedule delays. The study also showed that during the past 15 years, no improvement (learning effect) was evident in project cost and duration estimation as the level of accuracy has not changed significantly. Further, it is observed that project duration did not affect the performance with respect to cost and delay.

An exploratory study of the effects of supplier relationships on new product development outcomes

The preparation of cost estimates for construction projects is not an easy job, because construction projects are subject to risks and uncertainties, particularly at the inception stage, when very limited information about the project is available. However, the cost estimates prepared at this stage are usually the most important to the client. Nowadays, the costs for building services installations contribute a significant share of the total construction cost of building projects. Thus the accuracy of the building services cost estimates plays an important role in the total building construction project cost estimates. Traditionally, building services cost estimates are prepared by a deterministic single-figure (most likely) approach. This approach is often cost-ineffective and reactionary in nature. The risk management process (RMP) provides a logical consistent framework in identifying the risk factors affecting costs and in making allowances for risks in cost estimation. A survey was conducted to find out the current building services cost estimation practices and to obtain an insight into the understanding, use and application of RMP in preparing building services cost estimates within the building services industry. The results of the survey have suggested that the traditional deterministic cost estimation method is still being adopted by the majority of building services engineers in preparing cost estimates. Also, RMP has not been widely adopted by the building services industry in Hong Kong. From the analysis of building services firms and engineers with regard to the critical success factors for implementation of risk management, the survey reflects that the potential of implementing RMP for building services cost estimation is high.

The continuous improvement of products to attend consumer needs are increasing the product development process's (PDP) complexity. This demands that systems offer support to many different stages of PDP, as much as in tangible products (physical objects) as in intangible products (services, software). Concurrently, it was identified a considerable increase in the quantity and level of detailing of the information from those systems. However, there was also an increase in the number of semantical obstacles on sharing the latter. These semantical obstacles are mainly related to the heterogenic nature of the information, which has its captured meaning interpreted in a divergent way, increasing the project costs and development time. In this context, along with the Interoperable Product Design Manufacture System (IPDMS) concept, this article proposes the development of the core ontologies from the foundation view of this new methodology, in order to aid the semantical interoperability in the Product Design stage, further improving the exchange of information during different phases of the PDP. The first stage of this research is dedicated to review the main concepts on PDP, interoperability and ontology engineering. The second stage is dedicated to the concept exploration on the creation of core ontologies and its relation to the foundation view of the IPDMS.The final stage regards the creation of the core ontologies, which will serve as basis for further development of the system and work as knowledge basis for the entire concept. This will allow an analysis on consistency and information sharing with other elements of the Product Design and Manufacture in future stages. The creation of the core ontologies was related to the development of a plastic injected product, gathering domains such as Design and Materials, that will further combine to create the product model ontology. The development of the concept can bring advantages to the PDP and increase the automation during the decision making process. This tool of support showed potential to aid the exchange of information and inconsistency analysis at the product development process in the future, allowing risks reduction and rework at advanced project stages, also remarkably reducing time and total project costs.

In today's global environment, accelerated life testing is becoming a competitive advantage when time spent from conceptual stage to the final product development needs to be minimized (project costs and development time) in order to be successful. Using accelerated life testing techniques for mechanical and structural applications have strong challenges when defining the actual operational loading but also the fatigue lifemodel to represent actual field performance. Such common problems as well as some helpful strategies using accelerated life testing are presented for faster planning of structural testing. Examples from the transport refrigeration industry are used to demonstrate the utility of this strategy. Loading and test fixtures were designed as needed to simulate the normal operating levels and to accelerate specific failure modes while replicating field performance. In conclusion the test and analysis were effectively used to increase the degree of reliability improvements and to reduce the total number of test hours resulting in a shorter design cycle.

The project cost of power grid engineering technology transformation projects has shown importance in the development of China's power industry. By summarizing the models that can be used to predict the cost of technological transformation, the genetic algorithm based support vector machine (GA-SVM) and the cost estimation model based on extreme gradient boosting machine learning algorithm (XGBoosting) are studied. This paper combines the above theories and the Shapley value theory in game theory to construct a combined cost estimation model, which can realize high-precision estimation of power grid technological transformation projects. This paper takes the overhead line project as an example for simulation, which proves that the combined forecasting model can accurately reflect the actual project cost, and it has reference value for the cost forecast of the power grid engineering technology transformation project.

This paper presents how to use enhancement method to improve Earned Value (EV) method of projects. This method improves earned value management system (EVMS) and forecasting time and cost for projects. These method use established consists of four variables: Scheduled Performance Index (SPI), Cost Performance Index (CPI), Cost Variance (CV), Schedule Variance (SV). The model has been validated by a simulation study using a progress generator a new software. This software was developed in Visual Studio 2008 - Visual Basic .NET 2008 and Microsoft Office Project 2007 and it included some basic assumptions and data were generated day by day randomly. The results show a strong relationship between response variable and above four predictors. Therefore, it can be concluded that the model could be used in a wide range of projects for EAC estimation.

Effective construction site management is crucial for the success of any building project. This book provides a comprehensive guide to site management, covering pre-construction planning, site operations, safety and risk management, communication and coordination, cost and schedule control, quality and compliance, and technology and innovation. With chapters on project planning, site layout, material management, labor management, quality control, and cost estimation, this book offers practical advice and best practices for site managers, contractors, and construction professionals. Case studies and real-world examples illustrate successful site management techniques, while discussions on innovation and technology highlight future trends and directions in the field. By following the principles and strategies outlined in this book, construction teams can ensure efficient, safe, and successful project delivery.

Defining a clear project brief at an early stage is critical for the project success. A lack of project brief clarity can cause design and construction changes or errors throughout project delivery, which ultimately leads to project time and cost overruns. This research proposes a dynamic simulation model to evaluate the overall impact of briefing on cost and time performance of construction projects. The causal feedback loop model portraying the impact of briefing on project cost and time performance is developed. The interrelationships that exist among the influencing factors incorporated into the stock and flow model of the project briefing. Then, the proposed model is applied in a road project to assess its applicability and performance. The results indicate that a clear project brief aid in reducing brief changes, design changes, and construction changes throughout project delivery, which can ultimately improve project cost and time performance. Despite the fact that the clear brief increases required budget and duration for the project briefing, it still represents good value to the project cost and time performance overall.

In Construction Management, it is difficult to predict the cost estimate during the preliminary stage of the project because of limited information and unknown factors. Artificial Neural Networks can help in the prediction of estimate because of their simplicity and adaptability to non-linear problems. Due to their self-organizing nature they can be used to solve the problems even with low level programming. This makes them useful in interpreting and generalizing inadequate input information. ANN’s are crude derivatives of the biological neural network with single layered or multi-layered neuron in the form of input layer, hidden layer and output layer. The neural network first has to undergo training from historical data in order to make predictions or show results. A problem was formulated based on these drivers with numerical and categorical data. The data set was trained with a neural network using the MATLAB software using feed forward backpropagation. Training was carried out till the greatest correlation and least Mean Squared Error was obtained after multiple iterations. This trained data was used to predict the cost for a new project. The output of the testing was 87% accurate despite the small data set used.

This article provides an insight into the cost estimation of painting projects for both contractors and others. The cost estimating methods include benchmarking, unit price estimating, developed pricing, market pricing, and critical path scheduling. The first step in developing an accurate estimate for an industrial painting contract is determining the scope of work. The article describes the method of calculating quantities of materials and labor, surface area takeoff, and equipment costs. It concludes by listing the forgotten costs and presenting information on coating condition assessment and determining selling cost.

The advancement of computing and communication technologies has fueled the growth of Information Technology (IT), with Artificial Intelligence (AI) emerging as a transformative force in modernizing project management practices. This study explores the application of two prominent AI techniques—Decision Tree Analysis (DTA) and Artificial Neural Networks (ANN)—in improving project planning and control. A review of empirical studies highlights the limitations of conventional tools such as Gantt charts and the Critical Path Method (CPM) in managing complex project variables, often resulting in cost overruns and schedule delays. In contrast, DTA and ANN demonstrate superior predictive accuracy, decision support, and adaptability capabilities. DTA offers transparent and structured decision-making models, while ANN excels in pattern recognition and outcome forecasting. The findings underscore that integrating these AI tools enhances project efficiency, cost estimation, and time management, establishing AI as a critical asset for future project success.Keywords: Artificial Intelligence, Decision Tree Analysis, Artificial Neural Networks, Project Planning, Project Control

There is a global trend for integrated subsea alliances to be involved in projects earlier, with a broader scope and in closer collaboration with the operating company. Sometimes, this is achieved by enabling contractors to develop and propose their own field development concept, known as a supplier-led solution (SLS). This paper refers to a case study that demonstrates an effective way to improve project economics. A benchmark field layout plan was first generated using the customer's reference case. Then, a fully unconstrained SLS was developed to challenge and improve this reference case based on six main factors: Integrated production system modelling Fit-for-purpose project and design scoping Alternative field architecture Use of standardization Use of supplier or industry specification Integrated project execution A production assessment, cost estimation, and schedule were developed first for the reference case, fully compliant with the customer specifications, and with no deviation to the scope of work, and second, for the completely unconstrained SLS. Comparing the SLS to the reference case for this case study, the main improvements were as follows: Accelerated production thanks to integrated modelling and the introduction of subsea boosting technology Significant improvement in capex of approximately 41% Accelerated schedule due to a proposed innovative bidding process and a reduced equipment lead time enabled by standardization Lower project risk profile by improving the execution predictability primarily due to the integrated subsea offering The combination of these improvements resulted in better project economic viability and allowed lower contingency cost accruals due to proactive risk management. The SLS also helped the operating company to build a viable development scenario in order to pass through the next project stage gate. This paper demonstrates how an integrated subsea alliance with a broad spectrum of capabilities from the reservoir to the surface can deliver value through fully integrated earlier engagement. The methodology presented in this paper can be customized for any offshore prospect; the only prerequisites are early engagement, access to required data, and the freedom for the contactors to develop their own solution, in coordination with the operating company. A key feature of the proposed methodology is to link the SLS to reservoir representation, which avoids the common pitfall of delivering capex savings at the expense of larger production (revenue) losses.

An international association advancing the multidisciplinary study of informing systems. Founded in 1998, the Informing Science Institute (ISI) is a global community of academics shaping the future of informing science.