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.

A life-cycle-cost (LCC) is a powerful tool used to make economic decisions for construction building. LCC is a practice of accounting for all expenditures incurred over the lifetime of a particular structure. Costs at any given time are discounted back to a fixed date, based on assumed rates of inflation and the time-value of money. This study investigates the feasibility of obtaining an accurate deep learning prediction model of building LCC by applying historical data of similar projects. The applied LCC input and output criteria are gathered from previous literature studies. The input criteria are building area, floor height, no. of floors, structure & envelope type, building age, and year of built. The output categories include the relevant costs initial cost, operating and maintenance cost, environmental impact cost, and the end of life, each of them have its criteria. An electronic questionnaire of analytical hierarchy process (AHP) is developed to weight the selected criteria to be ready for the prediction model. Only 37 responses were received from Egypt and from outside Egypt and we excluded five of them to achieve the consistency. The Deep Belief network is developed with Restricted Boltzmann machine hidden layers based on 312 training data set of input and output criteria. Three case studies are devoted to validating on the assumption modelling procedures. The probability distributions of each case study predicted outputs are investigated by using statistical regression methodology.