Earned value‐based performance monitoring of facility construction projects

Application of neural networks and neuro-fuzzy models in construction scheduling

Construction scheduling is a complex process that involves a large number of variables, making it difficult to develop accurate and efficient schedules. Traditional scheduling techniques rely on manual analysis and intuition, which are prone to errors and often fail to account for all the variables involved. This results in project delays, cost overruns, and poor project performance. Artificial intelligence models have shown promise in improving construction scheduling accuracy by incorporating historical data, site-specific conditions, and other variables that traditional scheduling methods may not consider. In this research study, application of soft-computing techniques to evaluate construction schedule and control of project activities in order to achieve optimal performance in execution of building projects were carried out. Artificial neural network and neuro-fuzzy models were developed using data extracted from a residential two-storey reinforced concrete framed-structure construction schedule and project execution documents. The evaluation of project performance indicators in earned value analysis from 0 to 100% progress at 5% increment with a total of seventeen tasks were carried out using Microsoft Project software and data obtained from the computation were utilized for model development. Using input-output and curve-fitting (nftool) function in MATLAB, a 6-10-1 two-layer feed-forward network with tansig activation-function (AF) for the hidden neurons and linear AF output neurons was generated with Levenberg-Marquardt (Trainlm) training algorithm. Similarly, with the aid of ANFIS toolbox in MATLAB software, the training, testing and validation of the ANFIS model were carried out using hybrid optimization learning algorithm at 100 epochs and the Gaussian-membership-function (gaussmf). Loss-function parameters namely MAE, RMSE and R-values were taken as the performance evaluation criteria of the developed models. The generated statistical results indicates no significant difference between model-results and experimental values with MAE, RMSE, R² of 1.9815, 2.256 and 99.9% respectively for ANFIS-model and MAE, RMSE, R² of 2.146, 2.4095 and 99.998% respectively for the ANN-model. The model performance indicated that the ANFIS-model outclassed the ANN-model with their results satisfactory to deal with complex relationships between the model variables to produce accurate target response. The findings from this research study will improve the accuracy of construction scheduling, resulting in improved project performance and reduced costs.

Earned Value Method (EVM) for Construction Projects: Current Application and Future Projections

The earned value method (EVM) is an internationally known technique for project management that emphasizes the control of project cost performance and duration, thus allowing trends to be identified during execution and warning the project manager of variances that may affect the project so that they can take the necessary corrective measures. In this research, the finished projects of a construction company in the city of Cuenca, Ecuador, were assessed. EVM was applied to projects from a database developed with information from each one to rebuild past events, existing problems, and critical points and evaluate the performance over time. The results of this analysis are meant to determine the project’s success, calculating the cost variance at the end. EVM motivates project stakeholders to pay attention to costs and progress so that timely actions can be taken to optimize resources, resulting in the completion of a project within budget and on time. In conclusion, EVM plays an essential role in the integral management of the project in terms of scope, time, and cost. Moreover, there are now guidelines for applying this method as a control tool in future construction projects.

Examining the Critical Success Factors in IT Projects

Many companies experience IT project failures in relatively new areas such as Big Data, Data Science, Enterprise Systems, Blockchain, Cloud Computing, IT security, and IoT. Because of inadequate research in identifying critical success factors for these projects, we conducted a Delphi study employing separate panels for each of two kinds of project implementations, those using a predictive lifecycle approach and those using an adaptive lifecycle approach. We found common critical success factors: user/client involvement, senior management support, effective monitoring and control, effective communication and feedback, good change and configuration management, having a competent project manager, proper project leadership, and excellent vendor performance. Both predictive and adaptive lifecycle IT projects had certain unique critical success factors. The findings provide guidance to IT Project Managers.

The Enhanced-Earned Value Management (E-EVM) Model: A Proposal for the Aerospace Industry

The framework of this paper is the aerospace industry, which is one of the world’s leading sectors, thus playing a noteworthy role in current society. This makes it especially important to try to optimize the management of the aerospace sector. Therefore, the main objective of this paper is to propose the so-called Enhanced-Earned Value Management (hereinafter, E-EVM) model, able to explore the simultaneous evaluation of many projects, from which the management of the project can take advantage. Additionally, this model considers the possibility of forecasting pending tasks, measurable in time units or cost units, until the end of the project. The main contribution of E-EVM methodology is its capacity to detect both delayed and advanced projects by converting times (hours) into monetary units (EUR). Empirically, this enhanced model has been applied to a real case study in the aerospace industry composed of thousands of subprojects and the results provide the project manager with valuable information to make decisions in a short term. Through computer graphic representation techniques, the visualization of project deployment can be improved. Finally, the E-EVM model can be used even in big projects where a very large volume of information must be simultaneously treated and also, it will be suitable to apply pattern recognition concerning the project performance.

The S-Curve as a Tool for Planning and Controlling of Construction Process—Case Study

One of the key tasks of an investor and a contractor at the stage of planning and implementing construction works is to measure the progress of execution with regard to the planned deadlines and costs. During the execution of construction works, the actual progress of the works may differ significantly from the initial plan, and it is unlikely that the construction project will be implemented entirely according to the planned work and expenditure schedule. In order to monitor the process of deviations of the deadline and the budget of the investment task, several rudimentary methods of planning—as well as the cyclical control of the progress of construction projects—are used. An effective tool for measuring the utilization of the financial outlays of a construction project is the presentation of the planned financial flows on a timeline using a cumulative cost chart, the representation of which is the S-curve. The purpose of this paper is to analyze the course of an sample construction project comparing the planned costs of the scheduled works with the actual costs of the performed works, as well as identifying the reasons leading to the failure to meet the planned deadlines and budget of the project implementation. As part of the research conducted at a construction site of a hotel facility, the authors of this paper analyzed each of the 20-month effects of financial expenditures on construction works that were developed and processed by the Bank Investment Supervision (BIS) over a period of three years (between 2017 and 2019). Based on these results, charts and tables of the scheduled and actual cumulative costs of the completed construction project were prepared, the careful analysis of which enables interesting conclusions to be drawn.