Review on smartphone sensing technology for structural health monitoring

Sensing is a critical and inevitable sector of structural health monitoring (SHM). Recently, smartphone sensing technology has become an emerging, affordable, and effective system for SHM and other engineering fields. This is because a modern smartphone is equipped with various built-in sensors and technologies, especially a triaxial accelerometer, gyroscope, global positioning system, high-resolution cameras, and wireless data communications under the internet-of-things paradigm, which are suitable for vibration- and vision-based SHM applications. This article presents a state-of-the-art review on recent research progress of smartphone-based SHM. Although there are some short reviews on this topic, the major contribution of this article is to exclusively present a compre- hensive survey of recent practices of smartphone sensors to health monitoring of civil structures from the per- spectives of measurement techniques, third-party apps developed in Android and iOS, and various application domains. Findings of this article provide thorough understanding of the main ideas and recent SHM studies on smartphone sensing technology

A Parsimonious Yet Robust Regression Model for Predicting Limited Structural Responses of Remote Sensing

Small data analytics, at the opposite extreme of big data analytics, represent a critical limitation in structural health monitoring based on spaceborne remote sensing technology. Besides the engineering challenge, small data is typically a demanding issue in machine learning applications related to the prediction of system evolutions. To address this challenge, this article proposes a parsimonious yet robust predictive model obtained as a combination of a regression artificial neural network and of a Bayesian hyperparameter optimization. The final aim of the offered strategy consists of the prediction of structural responses extracted from synthetic aperture radar images in remote sensing. Results regarding a long-span steel arch bridge confirm that, although simple, the proposed method can effectively predict the structural response in terms of displacement data with a noteworthy overall performance

A Comparative Study on Structural Displacement Prediction by Kernelized Regressors under Limited Training Data

An accurate prediction of the structural response in the presence of limited training data still represents a big challenge if machine learning-based approaches are adopted. This paper investigates and compares two state-of-the-art kernelized supervised regressors to predict the structural response of a long-span bridge retrieved from spaceborne remote sensing technology. The kernelized super- vised procedure is either based on a support vector regression or on a Gaussian process regression. A small set of displacement time histories and corresponding air temperature data are fed into the regressors to predict the actual structural response. Results demonstrate that the proposed regression techniques are reliable, even when only 30% of the training data are used at the learning stage

Three-Dimensional Graph Matching to Identify Secondary Structure Correspondence of Medium-Resolution Cryo-EM Density Maps

Cryo-electron microscopy (cryo-EM) is a structural technique that has played a significant role in protein structure determination in recent years. Compared to the traditional methods of X-ray crystallography and NMR spectroscopy, cryo-EM is capable of producing images of much larger protein complexes. However, cryo-EM reconstructions are limited to medium-resolution (~4–10 Å) for some cases. At this resolution range, a cryo-EM density map can hardly be used to directly determine the structure of proteins at atomic level resolutions, or even at their amino acid residue backbones. At such a resolution, only the position and orientation of secondary structure elements (SSEs) such as α-helices and β-sheets are observable. Consequently, finding the mapping of the secondary structures of the modeled structure (SSEs-A) to the cryo-EM map (SSEs-C) is one of the primary concerns in cryo-EM modeling. To address this issue, this study proposes a novel automatic computational method to identify SSEs correspondence in three-dimensional (3D) space. Initially, through a modeling of the target sequence with the aid of extracting highly reliable features from a generated 3D model and map, the SSEs matching problem is formulated as a 3D vector matching problem. Afterward, the 3D vector matching problem is transformed into a 3D graph matching problem. Finally, a similarity-based voting algorithm combined with the principle of least conflict (PLC) concept is developed to obtain the SSEs correspondence. To evaluate the accuracy of the method, a testing set of 25 experimental and simulated maps with a maximum of 65 SSEs is selected. Comparative studies are also conducted to demonstrate the superiority of the proposed method over some state-of-the-art techniques. The results demonstrate that the method is efficient, robust, and works well in the presence of errors in the predicted secondary structures of the cryo-EM images

Three-Dimensional Graph Matching to Identify Secondary Structure Correspondence of Medium-Resolution Cryo-EM Density Maps

Cryo-electron microscopy (cryo-EM) is a structural technique that has played a significant role in protein structure determination in recent years. Compared to the traditional methods of X-ray crystallography and NMR spectroscopy, cryo-EM is capable of producing images of much larger protein complexes. However, cryo-EM reconstructions are limited to medium-resolution (~4–10 Å) for some cases. At this resolution range, a cryo-EM density map can hardly be used to directly determine the structure of proteins at atomic level resolutions, or even at their amino acid residue backbones. At such a resolution, only the position and orientation of secondary structure elements (SSEs) such as α-helices and β-sheets are observable. Consequently, finding the mapping of the secondary structures of the modeled structure (SSEs-A) to the cryo-EM map (SSEs-C) is one of the primary concerns in cryo-EM modeling. To address this issue, this study proposes a novel automatic computational method to identify SSEs correspondence in three-dimensional (3D) space. Initially, through a modeling of the target sequence with the aid of extracting highly reliable features from a generated 3D model and map, the SSEs matching problem is formulated as a 3D vector matching problem. Afterward, the 3D vector matching problem is transformed into a 3D graph matching problem. Finally, a similarity-based voting algorithm combined with the principle of least conflict (PLC) concept is developed to obtain the SSEs correspondence. To evaluate the accuracy of the method, a testing set of 25 experimental and simulated maps with a maximum of 65 SSEs is selected. Comparative studies are also conducted to demonstrate the superiority of the proposed method over some state-of-the-art techniques. The results demonstrate that the method is efficient, robust, and works well in the presence of errors in the predicted secondary structures of the cryo-EM images

Prediction of long-term dynamic responses of a heritage masonry building under thermal effects by automated kernel-based regression modeling

Field monitoring via ambient vibration tests is a reliable approach to assessing the health and integrity of masonry structures. However, the implementation of long-term field monitoring is often expensive, time-consuming, and labor-intensive. The best solution to addressing this challenge is to leverage the capacity of machine learning for predicting dynamic responses. This chapter intends to propose an automated kernel-based regression method for predicting the modal frequencies of a heritage masonry building under seasonal thermal effects. The crux of this method is to select an optimum kernel regressor between Gaussian process regression and support vector regression by Bayesian hyperparameter optimization. Due to the importance of thermal effects on long-term monitoring of masonry structures, temperature records are used as the main predictors, while structural modal frequencies are the main responses for regression modeling. Results show that the proposed method is successful in predicting the dynamic responses of the masonry building with high prediction accuracy

Long-Term Structural Health Monitoring by Remote Sensing and Advanced Machine Learning

This book offers an in-depth investigation into the complexities of long-term structural health monitoring (SHM) in civil structures, specifically focusing on the challenges posed by small data and environmental and operational changes (EOCs). Traditional contact-based sensor networks in SHM produce large amounts of data, complicating big data management. In contrast, synthetic aperture radar (SAR)-aided SHM often faces challenges with small datasets and limited displacement data. Additionally, EOCs can mimic the structural damage, resulting in false errors that can critically affect economic and safety issues. Addressing these challenges, this book introduces seven advanced unsupervised learning methods for SHM, combining AI, data sampling, and statistical analysis. These include techniques for managing datasets and addressing EOCs. Methods range from nearest neighbor searching and Hamiltonian Monte Carlo sampling to innovative offline and online learning frameworks, focusing on data augmentation and normalization. Key approaches involve deep autoencoders for data processing and novel algorithms for damage detection. Validated using simulated data from the I-40 Bridge, USA, and real-world data from the Tadcaster Bridge, UK, these methods show promise in addressing SAR-aided SHM challenges, offering practical tools for real-world applications. The book, thereby, presents a comprehensive suite of innovative strategies to advance the field of SHM

Regression Tree Ensemble to Forecast Thermally Induced Responses of Long-Span Bridges

The ambient temperature is a critical factor affecting the deformation of long-span bridges, due to its seasonal fluctuations. Although there exist various sensor technologies and measurement techniques to extract the actual structural response in terms of the displacement field, this is a demanding task in long-term monitoring. To address this challenge, data prediction looks to be the best solution. In this paper, the thermally induced response of a long-span bridge is forecasted with a regression tree ensemble method in conjunction with Bayesian hyperparameter optimization, adopted to tune the proposed regressor. Results testify that the offered method is reliable when there is a linear correlation between the temperature and the induced structural deformation, hence in terms of the thermally induced displacement field

Elimination of Thermal Effects from Limited Structural Displacements Based on Remote Sensing by Machine Learning Techniques

Confounding variability caused by environmental and/or operational conditions is a big challenge in the structural health monitoring (SHM) of large-scale civil structures. The elimination of such variability is of paramount importance in avoiding economic and human losses. Machine learning-aided data normalization provides a good solution to this challenge. Despite proper studies on data normalization using structural responses/features acquired from contact-based sensors, this issue has not been explored properly via new features, such as displacement responses from remote sensing products, including synthetic aperture radar (SAR) images. Hence, the main aim of this work was to eliminate environmental variability, particularly thermal effects, from different and limited structural displacements retrieved from a few SAR images related to long-term health monitoring programs of long-span bridges. For this purpose, we conducted a comprehensive comparative study to investigate two supervised and two unsupervised data normalization algorithms. The supervised algorithms were based on Gaussian process regression (GPR) and support vector regression (SVR), for which temperature records acquired from contact temperature sensors and structural displacements retrieved from spaceborne remote sensors produce univariate predictor (input) and response (output) data for the regression problem. For the unsupervised algorithms, this paper employed principal component analysis (PCA) and proposed a deep autoencoder (DAE), both of which conform with unsupervised reconstruction-based data normalization. In contrast to the GPR- and SVR-based data normalization algorithms, both the PCA and DAE methods only consider the SAR-based displacement (output) data without any requirement of the environmental and/or operational (input) data. Limited displacement sets of long-span bridges from a few SAR images of Sentinel-1A, related to long-term SHM programs, were considered to assess the aforementioned techniques. Results demonstrate that the proposed DAE-aided data normalization is the best approach to remove thermal effects and other unmeasured environmental and/or operational variability