Recovery of missing field measured wind pressures on a supertall building based on correlation analysis and machine learning

In long-term field measurements and structural health monitoring (SHM), data loss sometimes occurs due to interruption of power supply, sensor failure, and disruption of data transmission, which inevitably affects subsequent data analysis and structural condition assessment. To address this issue, this paper proposes a novel data recovery method based on correlation analysis and machine learning (ML) for recovery of missing wind pressures on cladding of high-rise buildings in field measurements and SHM. The method first utilizes correlation analysis to select appropriate inputs of the ML model, and subsequently to recover the missing wind pressures using the ML model with the selected appropriate inputs. In this study, four ML models (i.e., data recovery models), including deep neural network (DNN), particle swarm optimization-DNN (PSO-DNN), extreme learning machine (ELM), and support vector regression (SVR), are developed based on the proposed data recovery method. The time series of wind pressures with non-Gaussian features, which were collected by a multi-point synchronous monitoring system on a 600-m-high skyscraper during Super Typhoon Mangkhut, are employed to validate the performance of the developed data recovery models. The results show that all the four ML models perform satisfactorily in the recovery of the missing time series of wind pressures, among which the PSO-DNN model performs best and has good recovery accuracy for data loss that occurs in different areas of building cladding. This demonstrates that the proposed data recovery method based on correlation analysis and machine learning is a reliable and effective tool to recover the missing time series of wind pressures on high-rise buildings under severe wind conditions. Notably, this is the first attempt based on field measurements to recover the missing time series of wind pressures with non-Gaussian characteristics on a skyscraper during an extreme typhoon event, which aims to provide a useful means for the recovery of missing monitoring data in field measurements and SHM.