Despite the use of numerous new and advanced technologies in construction projects, the industry’s efficiency remains low. This business has faced significant challenges for a long time, such as non-value-added activities, also known as waste. Lean construction (L.C.) is one method for improving the situation by reducing waste and increasing value for the client. This study provides an in-depth literature review to provide a comprehensive list of all critical drivers and groups all these drivers into one research paper in order to determine the importance (weights) of these drivers and their relative importance, and to propose an innovative methodology for ranking them using Simos’ approach. Seven construction project case studies were proposed. Their lean status was assessed, the key-list was verified using a Weighted-Sum Model as a multi-criteria decision-making technique to rank them, the best one in terms of lean implementation was found. Subsequently, a sensitivity analysis was conducted to determine the most critical criterion for the key list. The following are some of the study’s main findings: 18 key drivers were identified and ranked, a clear definition of the client’s requirements was the most global weight between factors, and the case studies demonstrated that more than 60% of the lean drivers are implemented in Egypt. Day-to-day observation and standardized work were the top two most widely used lean practices in Egypt.

No doubt that the most vital issues to achieve a great success project are the choice of best suitable project delivery methods. According to the experience of project management staff, the delivery of the project is chosen. However, that leads to similar repetitive issues, for example, exceeding the cost of the project and exceeding the project's schedule, and that's what many projects face. It is difficult to develop the management of the recurring issues of the project because there is no awareness of delivery methods. The efficiency of project implementation is greatly affected by selecting the appropriate delivery method. Fuzzy data at early stages of construction projects leads to the fuzzy decision of selection a suitable type to deliver the project contract. In this study, the main purpose was to determine the comprehensive criteria that significantly influence the selection of infrastructures construction project delivery systems. These criteria will aid decision making process more comprehensive and effective innovation tool to choose the reliable Infrastructures Project Delivery System.

Undoubtedly, most industrial buildings have a huge Life Cycle Cost (LCC) throughout their lifespan, and most of these costs occur in structural operation and maintenance costs, environmental impact costs, etc. Hence, it is necessary to think about a fast way to determine the LCC values. Therefore, this article presents an LCC deep learning prediction model to assess structural and envelope-type alternatives for industrial building, and to make a decision for the most suitable structure. The input and output criteria of the prediction model were collected from previous studies. The deep learning network model was developed using a Deep Belief Network (DBN) with Restricted Boltzmann Machine (RBM) hidden layers. Seven investigation cases were studied to validate the prediction model of a 312-item dataset over a period of 30 years, after the training phase of the network to take the suitable hidden layers of the RBM and hidden neurons in each hidden layer that achieved the minimal errors of the model. Another case was studied in the model to compare design structure alternatives, consisting of three main structure frames—a reinforced concrete frame, a precast/pre-stressed concrete frame, and a steel frame—over their life cycle, and make a decision. Precast/pre-stressed concrete frames were the best decision until the end of the life cycle cost, as it is possible to reuse the removed sections in a new industrial building.