Digital twin for civil engineering systems: An exploratory review for distributed sensing updating
We live in an environment of ever-growing demand for transport networks, which also have
ageing infrastructure. However, it is not feasible to replace all the infrastructural assets that …
ageing infrastructure. However, it is not feasible to replace all the infrastructural assets that …
f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks
Obtaining expert labels in clinical imaging is difficult since exhaustive annotation is time-
consuming. Furthermore, not all possibly relevant markers may be known and sufficiently …
consuming. Furthermore, not all possibly relevant markers may be known and sufficiently …
Anomaly detection of structural health monitoring data using the maximum likelihood estimation-based Bayesian dynamic linear model
Enormous data are continuously collected by the structural health monitoring system of civil
infrastructures. The structural health monitoring data inevitably involve anomalies caused by …
infrastructures. The structural health monitoring data inevitably involve anomalies caused by …
Lstm-autoencoder for vibration anomaly detection in vertical carousel storage and retrieval system (vcsrs)
JS Do, AB Kareem, JW Hur - Sensors, 2023 - mdpi.com
Industry 5.0, also known as the “smart factory”, is an evolution of manufacturing technology
that utilizes advanced data analytics and machine learning techniques to optimize …
that utilizes advanced data analytics and machine learning techniques to optimize …
Modeling and forecasting of temperature-induced strain of a long-span bridge using an improved Bayesian dynamic linear model
Temperature-driven baseline is highly responsive to anomalous structural behavior of long-
span bridges, which means that the discrepancy between the measured and forecasting …
span bridges, which means that the discrepancy between the measured and forecasting …
Bayesian dynamic linear model framework for structural health monitoring data forecasting and missing data imputation during typhoon events
QA Wang, CB Wang, ZG Ma, W Chen… - Structural Health …, 2022 - journals.sagepub.com
A Bayesian dynamic linear model (BDLM) framework for data modeling and forecasting is
proposed to evaluate the performance of an operational cable-stayed bridge, that is, Ting …
proposed to evaluate the performance of an operational cable-stayed bridge, that is, Ting …
[图书][B] Probabilistic machine learning for civil engineers
JA Goulet - 2020 - books.google.com
An introduction to key concepts and techniques in probabilistic machine learning for civil
engineering students and professionals; with many step-by-step examples, illustrations, and …
engineering students and professionals; with many step-by-step examples, illustrations, and …
An explainable probabilistic model for health monitoring of concrete dam via optimized sparse bayesian learning and sensitivity analysis
Machine learning has become increasingly popular for modeling dam behavior due to its
ability to capture complex relationships between input parameters and dam behavior …
ability to capture complex relationships between input parameters and dam behavior …
Modified dam deformation monitoring model considering periodic component contained in residual sequence
D Yuan, B Wei, B Xie, Z Zhong - Structural Control and Health …, 2020 - Wiley Online Library
Considering that dam deformation and its influencing factors present complex nonlinearities,
a modified deformation monitoring model is proposed in this study. On the basis of the …
a modified deformation monitoring model is proposed in this study. On the basis of the …
Physics-informed neural networks for system identification of structural systems with a multiphysics damping model
Structural system identification is critical in resilience assessments and structural health
monitoring, especially following natural hazards. Among the nonlinear structural behaviors …
monitoring, especially following natural hazards. Among the nonlinear structural behaviors …