[HTML][HTML] The state of the art of data science and engineering in structural health monitoring
Structural health monitoring (SHM) is a multi-discipline field that involves the automatic
sensing of structural loads and response by means of a large number of sensors and …
sensing of structural loads and response by means of a large number of sensors and …
Characterisation of geotechnical model uncertainty
The calculated response from a numerical model will deviate from the measured one given
the presence of modelling idealizations and real world construction effects. This deviation …
the presence of modelling idealizations and real world construction effects. This deviation …
[HTML][HTML] A novel double-hybrid learning method for modal frequency-based damage assessment of bridge structures under different environmental variation patterns
Monitoring of modal frequencies under an unsupervised learning framework is a practical
strategy for damage assessment of civil structures, especially bridges. However, the key …
strategy for damage assessment of civil structures, especially bridges. However, the key …
Convolutional neural networkbased data anomaly detection method using multiple information for structural health monitoring
Structural health monitoring (SHM) is used worldwide for managing and maintaining civil
infrastructures. SHM systems have produced huge amounts of data, but the effective …
infrastructures. SHM systems have produced huge amounts of data, but the effective …
Ensemble learningbased structural health monitoring by Mahalanobis distance metrics
H Sarmadi, A Entezami… - Structural Control and …, 2021 - Wiley Online Library
Environmental variability is still a major challenge in structural health monitoring. Due to the
similarity of changes caused by environmental variations to damage, false positive and false …
similarity of changes caused by environmental variations to damage, false positive and false …
[PDF][PDF] Data anomaly detection for structural health monitoring using a combination network of GANomaly and CNN
The deployment of advanced structural health monitoring (SHM) systems in large-scale civil
structures collects large amounts of data. Note that these data may contain multiple types of …
structures collects large amounts of data. Note that these data may contain multiple types of …
Realtime system identification: an algorithm for simultaneous model class selection and parametric identification
In this article, a novel Bayesian realtime system identification algorithm using response
measurement is proposed for dynamical systems. In contrast to most existing structural …
measurement is proposed for dynamical systems. In contrast to most existing structural …
Data-driven development of three-dimensional subsurface models from sparse measurements using Bayesian compressive sampling: A benchmarking study
With the rapid development of computing and digital technologies recently, three-
dimensional (3D) subsurface models for accurate site characterization have received …
dimensional (3D) subsurface models for accurate site characterization have received …
[HTML][HTML] Data anomaly detection for structural health monitoring by multi-view representation based on local binary patterns
Structural health monitoring (SHM) systems provide opportunities to understand the
structural behaviors remotely in real-time. However, anomalous measurement data are …
structural behaviors remotely in real-time. However, anomalous measurement data are …
[PDF][PDF] Managing risk in geotechnical engineering–from data to digitalization
If you scan a page from a soil report, this is called digitization. If you deploy digital
technologies, both software such as building information modeling and machine learning …
technologies, both software such as building information modeling and machine learning …