In competitive growth and Industry 4.0, construction prediction and management have a key role. To find a way to provide a simulation method for the damage assessment of buildings and Industry 4.0, building information modelling technology is the most suitable choice. This work presents and analyses the building material from design modelling to model information extraction, virtual construction, and an imported virtual simulation engine. A simulation system has been built to understand the force and material collision detection of buildings, and a three-dimensional (3D) simulation platform is developed based on the Unity3D engine. A 3D display of building model and simulation data is realized in this work based on the simulation software platform. The results show that the building 3D simulation images constructed by the designed system are high definition, take little time, and have excellent performance. The outcomes are realized in terms of the engineering cost ratio and have energy consumption and efficiency values of 20% and 40%, respectively, which are much better than the traditional method. Efficiency has also improved to 76% from the traditional method using the proposed method, which makes it a robust platform for construction prediction and management in industries. The virtual simulation technology is applied to solve problems of building design and damage assessment. The influence of this technology on the overall design of the building is discussed, followed by future development directions for industrial automation. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

In order to solve the energy consumption hypothesis of large buildings, the energy consumption hypothesis based on the BP neural network is proposed. First, to study the system of statistical index of building energy consumption and the system of statistical reporting of energy consumption of civil construction. In addition, to establish reliable consumer authority control to ensure the security and management of the database. Second, based on an analysis of the mechanism by which the BP neural network operates, this article optimizes it and describes the structure of the neural network, which includes the number of network layers, the number of neurons in each layer, and the number of latent neuron layers. hidden neuron layers and hidden neurons. The maximum value method is used to normalize the input sample data; finally, the learning and training process of neural network is determined. Based on BP neural network theory, the energy consumption statistics platform and prediction system are established by using Delphi 6.0. These include functional modules such as basic building information management, building energy consumption information management, building energy consumption summary, energy presampling information management, and building energy consumption forecast; the collection of building energy consumption data is mainly completed by intelligent energy consumption monitoring sensor network system. Finally, the city’s building energy consumption information system conducts construction energy audits and analyzes the potential for energy savings. The results show that the hypothesis model determined by the BP neural network algorithm has an average error of 10.6% in predicting the construction energy consumption data, which is better than Matlab’s predicted result and the mean error is 12.6%. From this, it can be seen that the BP neural network algorithm can provide better predictions of building energy consumption.