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.

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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.

Planning and cost estimation engineers face great challenges to estimate construction productivity rate (PR) of diaphragm walls (DWs). A specific guideline to predict the construction of DWs in underground station is still not available. In order to overcome these challenges, the criteria affecting PR for the construction of DWs should be hence defined and the factors that impact the construction productivity of DWs should be well understood. Briefly and given the limitations of previous research work, we gathered and compiled a comprehensive key list of all criteria based on previous reports. This comprehensive key list was later reviewed by underground construction and specialized experts to define, identify and distinguish the most important criterion affecting the construction productivity of DWs in Egypt. We developed two questionnaire surveys to conduct these interviews with the experts. This obtained criterion was ranked and weighted using Simos' procedure based on prioritizing the influencing criteria. Our findings concluded that the ground condition type/and characteristics, soil-machine interaction, machine type and model characteristics (grab/or cutter), breakdowns and de-sender efficiency are significantly influencing the excavation rate. Additionally, factors such as cage type (fiber/or steel), connection type (welding/or mechanical) and volume of steel fixing crew are crucial to identify the rate of installation cages. On the other side, the arrival time of concrete car mixer and panel volume were determined to significantly impact the concrete casting rate. In summary, this study presented a distinctive weighting and ranking methodology of the influencing criteria for the PR of DWs. Furthermore, this work provides an initial concept for many full-scale prediction applications to estimate an accurate construction productivity of DWs, particularly in the underground projects.