Implementation of a Life Cycle Cost Deep Learning Prediction Model Based on Building Structure Alternatives for Industrial Buildings

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.

Automated summary

This article presents an LCC deep learning prediction model to assess structural and envelope-type alternatives for industrial building and make the most suitable structure decision. The model is validated through investigation cases and comparison of design structure alternatives, concluding that precast/pre-stressed concrete frames are the best choice.