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To help decision-makers choose appropriate infrastructure project delivery systems (IPDS) and keep up with the construction industry’s rapid growth, this study aims to develop a goal optimization technique.This looks into team integration, large production and optimum sustainability. The suggested approach for meeting several infrastructure project objectives is flexible and expandable. This research overcomes the significant discrepancy between the construction industry’s progress and the rate at which project delivery methods evolve.

This study examined pertinent literature to choose an appropriate project delivery method and gave information on several elements that affect that decision. After optimization using a genetic algorithm (GA), a Pareto front of solutions has been found. The three construction goals of sustainability, mass production and team integration are all met by the chosen best solution. The four most popular possibilities for studying the suggested approach are five primary categories, each of which has 22 variables, and the weight of each variable was established using Simo’s procedure. This is optimized, demonstrating the accuracy of the optimization model.

Sustainability, mass production and team integration are the major goals of selecting the finest IPDS. The Pareto-optimal solutions discovered through analysis demonstrated that the created GA is reliable and generates solid outcomes. In fact, it enables decisions that were based on a single criterion to be overturned. The process has therefore demonstrated its efficacy in identifying the ideal answer. First integrated project delivery (IPD), second design-build (DB), third design-bid-build (DBB) and last construction manager at risk (CMR) are the best options. The weight of the aims function has found these rankings to be satisfactory.

The findings demonstrate that the suggested strategy can lead to optimization, providing the government with a wide range of options for attaining certain project objectives. The ability of this study to evaluate the combined effects of three objectives in choosing the best IPDS, the production of optimal selection solutions (IPDS), which can help with better decision-making when many objectives are present, and the flexibility and extendibility of the suggested approach for achieving priorities in infrastructure projects are what make it unique. This approach was able to select IPDS to meet developments in the construction project.

To confirm the validity of the GA, the factor of error was calculated, which is equal to 1.7599e-08.

Innovative design and execution approaches are employed in infrastructure sectors and planning to enhance the integrated project delivery system, assure the sustainability of infrastructure projects, and meet the demands of the dynamic, changing environment. Delivery methods must incorporate new technologies. By combining digital technology, teamwork, and mass manufacturing, a greater degree of exceptional quality, sustainability, and resilience in the environment will be generated. As a result, a new approach does not rely on the reaction policy, but instead considers alternative scenarios and employs a simulation model to determine the best course of action.

In the paper, the system dynamics approach to construction management is validated in light of pertinent research. Additionally, it describes the difficulties facing the infrastructure projects' delivery system. Additionally, the strategy for system dynamics creation is described. This strategy includes a causal loop diagram, generates a stock-flow diagram, and simulates forecasts of model behavior over time. Next, the optimization model's validation process is used to create a system dynamics model for choosing the best infrastructure project delivery system project and controlling it to maximize sustainability, mass production, digital integration, and team integration. The dynamic complexity of project management is growing.

The primary goal is to present a system dynamics (SD) simulation to look at how well infrastructure projects perform in terms of choosing the best method for delivering infrastructure projects. One of the most ideal methods for delivering projects is integrated project delivery. An effective methodology for making strategic decisions on the choice of the best project delivery method. In order to enhance certain infrastructure project delivery system metrics for sustainability, mass production, digital integration, and team integration, the model included building strategy and sophisticated system dynamics simulation. According to the construction strategy, the outcomes have been satisfactory.

System dynamics research has been done to replicate the idea of contemporary construction in order to determine the best approach for delivering infrastructure. The government and decision-makers would benefit from understanding this research as they decide on the best delivery method for boosting the sustainability and productivity of infrastructure projects in Egypt.

This study aims to establish a new system to predict the defect liability phase (DLP) cost using the Six Sigma methodology, which investigates sources of variations and reduces the error level to 3.4 per million through five phases: define, measure, analyze, design and verify.

After the initial handover of the construction project, the DLP follows the practical completion. During this stage, the contractor is responsible for the remedy of any defects that appeared in the project. Many researchers have studied defect reasons and their associated costs in different industries, while the construction industry remains a green field for this kind of research. The objective of this study was to develop a model to predict the DLP cost. The research methodology adopted the five stages of the Six Sigma cycle: defining objectives, measuring the data, analyzing performance, designing the model and verifying the results. Twenty factors were identified as potential factors affecting the DLP cost. Factors were categorized into two main clusters: project data and organization data. Interviews were conducted with 42 project management experts, who have 8–35 years of experience in construction project management, to rank the 20 factors based on their importance. Simo’s procedure was used to obtain the weight of each factor affecting the DLP cost based on the opinions of the experts. The Pareto principle was used to select the “Vital Few” factors affecting the DLP cost, and six factors were selected. The design of experiments (DOE) was used to establish a dynamic model to predict the DLP cost using a sample of 41 construction projects obtained from the above-mentioned 42 project management experts. The model accuracy was verified using data obtained from a different sample of five construction projects, which were not used to establish the model.

The results showed that among the 20 factors, only six were found to have a cumulative impact of 50% over the cost of the DLP: type of project, project contract value, nationality of the employer, project manager experience, DLP duration and sector of the employer. A model was established through the DOE to predict the DLP cost using the values of the aforementioned factors.

As a natural limitation of using DOE, the newly developed model can be applied to predict the DLP cost based on data within the range of data used during the model development, which means that model is confined within the specific measured values of factors. Furthermore, it will be beneficial for future studies to study the impact of other factors related to the types of materials or equipment used in building the project because it was not considered during this study because of the huge diversities in these factors and difficulties in determining its impact on the DLP cost.

The unique results of using DOE through Minitab software facilitated obtaining of a dynamic model, which means that researchers can modify any value of the six factors and monitor instantly the expected change in the DLP cost, which will allow a better understanding of the impact of each factor on the DLP cost. Moreover, the new model will help contractors to predict the expected DLP cost to be added for their project budget, which will mitigate the risk of cost overrun resulted from the cost of defect rectification.

A dynamic model was established to predict the DLP cost using the DOE. The new model was validated, and the prediction error ranged from −18% to +21%.