Digital Twin-Driven Intelligent Construction:Features and Trends

Digital twin(DT)can achieve real-time information fusion and interactive feedback between virtual space and physical space.This technology involves a digital model,real-time information management,comprehensive intelligent perception networks,etc.,and it can drive the rapid conceptual development of intelligent construction(IC)such as smart factories,smart cities,and smart medical care.Nevertheless,the actual use of DT in IC is partially pending,with numerous scientific factors still not clarified.An overall survey on pending issues and unsolved scientific factors is needed for the development of DT-driven IC.To this end,this study aims to provide a comprehensive review of the state of the art and state of the use of DT-driven IC.The use of DT in planning,design,manufacturing,operation,and maintenance management of IC is demonstrated and analyzed,following which the driving functions of DT in IC are detailed from four aspects:information perception and analysis,data mining and modeling,state assessment and prediction,intelligent optimization and decision-making.Furthermore,the future direction of research,using DT in IC,is presented with some comments and suggestions.This work will help researchers gain in-depth and systematic understanding of the use of DT,and help practitioners to better promote its implementation in IC.

An intelligent approach for flight risk prediction under icing conditions

Flight risk prediction is significant in improving the flight crew's situational awareness because it allows them to adopt appropriate operation strategies to prevent risk expansion caused by abnormal conditions,especially aircraft icing conditions.The flight risk space representing the nonlinear mapping relations between risk degree and the three-dimensional commanded vector(commanded airspeed,commanded bank angle,and commanded vertical velocity)is developed to provide the crew with practical risk information.However,the construction of flight risk space by means of computational flight dynamics suffers from certain defects,including slow computing speed.Accordingly,an intelligent approach for flight risk prediction is proposed to address these defects based on neural networks.Radial Basis Function Neural Network(RBFNN)is optimized using Adaptive Particle Swarm Optimization(APSO).To optimize both the parameters and the structure of APSO-RBFNN,a fitness function containing the training accuracy and network structure size is proposed.Extensive experimental results demonstrate that the flight risk predicted by APSO-RBFNN is very close to that obtained via computational flight dynamics.The average error(RMSE)is less than 10^(-1).The approach achieves a speedup close to 1000x compared with computational flight dynamics.In addition,some flight upset and recovery cases are presented to illustrate the efficiency of the intelligent approach for flight risk prediction.