A Quantitative Analysis of the Performance of Transportation Projects in Developing Countries

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.

In construction projects, the relationship between project cost and duration, which is one of the main factors of the project, are considered the parameters decision-makers. When a construction schedule is planned by the planners, additional resources are allocated to shorten the project duration considering the completion time and cost of the activities so that the most suitable construction schedule is prepared by providing a trade-off between project duration and total cost. In the case of time-cost trade-off problems, the total cost of the project is determined by summing each activities’ cost by adding indirect costs. Moreover, total project duration is determined by obtaining total project duration after CPM (Critical Path Method) calculations. Obtaining optimum duration and costs is such a problematic issue with using conventional methods and common software packages. These techniques consider deterministic project values and include CPM calculations for fitness value calculation. Moreover, current studies defined alternatives as non-variable discrete values. However, construction projects are dynamic processes, and quantities are prone to be changed related to project size. In this study, time-cost trade-off optimization is performed by using harmony search method with introducing different implementation that can be more easily used for different projects. The model is implemented in a real construction project, and the conclusions are interpreted.

Classical scheduling techniques are well-known to underestimate the average project duration, yet they remain widely used in practice due to their simplicity. In this paper, the new Critical Duration Index (CDI) is proposed. This index indirectly allows anticipation for the probability of a project ending late, as well as the average project duration extension compared with a deterministic project duration estimate. The accuracy of two simple regression expressions that use the CDI was tested on two representative data sets of 4,100 artificial and 108 empirical (real) projects. Results show that these regression expressions outperformed the only alternative index found in the literature. Besides allowing enhanced forecasting possibilities, calculating the CDI only requires basic scheduling information that is available at the planning stage. It can thus be easily adopted by project managers to improve their project duration estimates over prior deterministic techniques.

Exploiting organizational knowledge in developing IS project cost and schedule estimates: An empirical study

Calculation of safe float use in risk-analysis-oriented network scheduling

On the whole, appropriate scheduling is an extremely paramount factor in project planning. It makes planning items such as allocating resource, determining the number of contractors, maintaining, and delivering order easy. Due to its convoluted nature, scheduling new product development projects is very difficult; managers of these projects deal with complicated factors like uncertain activity duration, information flow and complex relationships. Dependency structure matrix (DSM) has been suggested to cope with these complexities. In the present study, uncertainty in estimating project duration is highlighted; therefore, input information like activity duration, information flow among activities, and the time factors are considered stochastic. Firstly, random numbers are generated based on probability distribution of inputs. Then, project duration and minimised project duration are simulated using DSM method and Monte-Carlo simulation; when simulation turns stable, the mean and variance of durations are obtained. A numerical example is provided and findings are discussed.

Project Scheduling using Dependency Structure Matrix

Investigating the effect of construction management strategies on project greenhouse gas emissions using interactive simulation

A framework combining project simulation model with extracted uncertainty parameters from past data is proposed in this paper. In the existing simulation models which take uncertainty elements into consideration, a large number of input parameters are requested. However, most of these parameters are difficult or time-consuming to state. The aim of this paper is to provide a possible solution proposing a new simulation model based on extracted parameters and setting up a protocol to extract uncertainty parameters from past log data. The output of the simulation model will be the project duration, giving feedback to the design of human resource. The proposed protocol includes a definition of necessary past data and how to calculate minimum work amount, delay probability and rework probability. On the other hand, the proposed simulation model includes basic model describing project structures such as task dependency and resource skills, delay model describing variation of task work amount, and rework model describing transition among different tasks. Besides, in the case study, we test the program of the proposed framework constructed of the parameter extracting protocol and the simulation model. After that, we apply the framework on a project introduced in existing research. Two human resource strategies are considered of, on the basis of the duration estimation results. The conclusion of this study is that the proposed framework is able to conduct duration estimation and support the decision-making process around human resource at the early stage of a project.

Transport agencies around the world recognize the importance of on-time on-budget delivery of highway projects and understand the need for an efficient transport development process. Past studies of highway project duration and cost estimation were focused on construction, and few studies have focused on preconstruction project delivery from an agency’s perspective. This paper proposes the use of hazard-based duration models for investigating the potential risk factors that could affect project development time from planning to letting. A database containing over 35 years of highway project data from the state of Indiana was used to develop appropriate models. Of the three model forms (Weibull, Weibull with gamma heterogeneity, and log-logistic) considered in the paper, it was found that the log-logistic model is the most appropriate for modeling project development time. For each work category (pavement, bridge, and road/interchange projects), risk factors were identified, and their influences on project development time (and consequently, on letting time) were investigated. The developed hazard-based duration models were further applied to demonstrate their ability to predict expected project development time for delivery and the project-related factors that influence this duration, thereby providing the agency with a useful interpretation of associated delivery risks.

Construction projects are data-rich environments. However, those data are usually captured for site-specific reasons, e.g., the filing and approval of inspection requests, with little regard to how they can be leveraged for improved project management. Typically, scheduling techniques rely on general probability estimates, which do not capture the details of the site processes causing schedule deviations. This paper illustrates how machine learning techniques can mine project data to forecast delay in the midst of the project. The proposed method uses concrete pouring requests as an example of a site data stream and implements a random forest predictive model to forecast the likelihood of acceptance for these requests. Embedded in the proposed approach is an analysis that allows for the addition of probabilistic time delays associated with the forecast of rejected requests. The methodology was tested on a real-world case study, allowing for the comparison between a project duration estimate based on critical path method (CPM) with static buffers and a project duration obtained using the proposed method. The results show a difference of 10% between the two durations. The paper shows how using data streams from a construction site with machine learning techniques can enhance project duration estimates in execution.

Research Question: This paper investigates whether the Monte Carlo simulation can be widely used as a practicable method for the analysis of the risks that impact project duration. Motivation: The main goal was to explore the use of the Monte Carlo simulation for project time management, and shed some light on the key benefits and drawbacks of this method. The paper reviewed the existing literature considering traditional use of the Monte Carlo for quantitative project risk analysis (such as Kwak & Ingall, 2007; Hulett, 2017) and elaborated the issue by suggesting potential improvements in terms of method modification for schedule management, such as event chain methodology proposed by Agarwal & Virine (2017). Another goal was to examine the capability of user-friendly software to provide project managers with some of these benefits. Idea: The core idea of this paper was to evaluate the value of the Monte Carlo method for project time and schedule management, by matching traditional foundations with modern techniques. Data: The paper used the secondary data extracted from relevant literature and project examples. A literature review reveals how the application of the Monte Carlo simulation evolved as a project management tool, along with specific benefits and concerns for its application. Tools: A detailed application of the Monte Carlo in predicting project duration is provided, and the applicability and viability of the method are proven through a case demonstration. Following the presentation of a practical example and discussion of the main features, some limitations and potential improvements to the Monte Carlo method are suggested. Findings: Even with the existence of certain limitations, the Monte Carlo simulation remains the primary method for quantitative analysis of project risks. Despite the Monte Carlo having been found to be applicable, adaptable and predictive of total project duration, it is found to be insufficiently used by practitioners. Contribution: The paper urges the need for research on successful diffusion of the Monte Carlo simulation and helps practitioners to understand the adaptability of the Monte Carlo simulation as a tool for risk quantification and its use for effective duration planning of their projects.

PurposeThe literature suggests employing big data and Building Information Modeling (BIM) to examine building projects from several perspectives. Nevertheless, the literature is deficient in thorough BIM performance evaluation methods grounded in big construction project data. This paper presents an evaluation framework outlining the data input requirements and necessary data to conduct research leveraging big data for the analysis of BIM performance.Design/methodology/approachData parameters and performance metrics included in the evaluation framework are derived from a synthesis of literature review, data overview and interviews. The construction data was analyzed using PowerBI after undergoing a quality control process. Analysis results were verified through interviews with the main contractor. The project data served to assess the evaluation framework.FindingsThe evaluation framework has ten data parameters, and six performance metrics categorized into three main categories. The findings indicate that the evaluation framework can be utilized to comment on BIM performance in a project, with a level of accuracy. Results indicated that ensuring the quality of tracked project data is crucial for obtaining reliable analysis results. Determining performance metrics and data parameters prior to data recording processes can help simplify the analysis process and ensure accurate analysis results.Originality/valueThe proposed framework offers a comprehensive performance evaluation methodology that leverages the innovative application of unique and challenging to acquire big data, allowing practitioners to assess BIM performance in relation to project time, cost and scope. Identified data parameters and novel performance metrics may provide the foundation of a guideline for construction project data logging to facilitate accurate BIM performance monitoring.

Accurate project duration prediction is increasingly important for management because it defines the expected timeline for project realization. This study utilizes an integrated approach combining neural networks with the Taguchi method to forecast the time required to complete projects within predetermined deadlines. The methodology involves modelling and simulating the network of project activities to estimate the total average project duration. ration. The neural network model uses input variables such as success probability, effort, and learning factor to predict the total time necessary for project completion. The total average project duration is the output variable that is critical during the design phase. Subsequently, the Taguchi method optimizes the neural network’s outputs, incorporating mean squared error (MSE) values to enhance predictive accuracy. accuracy. This study underscores the efficacy of artificial neural networks (ANNs) as predictive tools, demonstrating their ability to meticulously estimate project duration. The proposed method’s efficiency and applicability are demonstrated by MATLAB simulation analyses, highlighting its effectiveness in precise deadline estimation. In the realm of engineering, ANNs stand tall as formidable predictive tools, their efficacy underscored by this study’s successful application. By harnessing ANNs and simulation data, this research crafts a predictive model adept at estimating the average total duration of projects. Through meticulous consideration of crucial parameters like the probability of success and effort factors, the model emerges as a beacon of accuracy within the design domain. Future research will explore the effects of additional parameters on activity networks and alternative transfer functions, as well as the potential integration of reinforcement algorithms to improve resource allocation, risk management, and project outcomes through online training data.

This paper presents a new method for forecasting time and cost of construction projects at completion and/or at any intermediate time horizon. The method is designed to overcome limitations of current applications of earned value method in forecasting project cost and durations. The method adopts recently introduced extensions to EVM and the project ratios technique for progress reporting. It introduces modifications and developments that allow more accurate and practical results. Unlike the current applications of EVM method, the proposed method uses simulation to generate stochastic S-curves based on past performance achieved by contractors. The method enables the user to assess the uncertainty associated with forecasted project cost and duration at completion so that appropriate corrective actions can be taken, when needed. A numerical example is presented to demonstrate the use of the proposed method and to illustrate its improved forecasting accuracy over current methods. The results obtained by the developed method demonstrate the effectiveness of (1) using project ratios technique in forecasting project time and cost comparing to that obtained by traditional EVM method; (2) measuring the status of critical activities only, is particularly useful in forecasting project durations; and (3) accounting for uncertainties involved in the forecasting process provides flexibility in modelling forecasted project time and cost.