Article
Computer Science, Interdisciplinary Applications
Mohammad Hesam Soleimani-Babakamali, Reza Sepasdar, Kourosh Nasrollahzadeh, Ismini Lourentzou, Rodrigo Sarlo
Summary: This study proposes an unsupervised, online structural health monitoring framework that is robust to different sensor configurations. The framework leverages generative adversarial networks (GANs) and uses the fast Fourier transform of structural accelerations as input. LSTM-based discriminators are found to be robust and effective even in overfitted discriminator scenarios. The evaluation of the framework on benchmark datasets demonstrates its high accuracy in novelty detection, damage classification, and identification under varying sensor configurations.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2022)
Article
Engineering, Mechanical
Y. F. Xu, G. L. Huang
Summary: This study provides an in-depth analysis of the impact of damage on the structural dynamic properties of finite acoustic metamaterials (AMMs). A modal sensitivity analysis is formulated to measure the modal deformation of each spring, and the effects of damage on AMMs are extensively investigated numerically. The results show significant changes in frequency response functions, eigenvalues, and mode shapes of damaged AMMs near bandgaps, with these changes diminishing as frequencies move away from the bandgaps. This research enhances our understanding of the structural dynamics of damaged metamaterials and offers valuable insights for the development of effective damage identification techniques and resilient metamaterial-based structures.
INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES
(2023)
Article
Engineering, Multidisciplinary
Marco Civera, Luigi Sibille, Luca Zanotti Fragonara, Rosario Ceravolo
Summary: Advanced data analysis techniques, such as Automated Operational Modal Analysis (AOMA) algorithms, are crucial for the Structural Health Monitoring (SHM) of civil buildings and infrastructures. AOMA enables the unsupervised estimation of modal parameters from ambient vibrations, allowing for efficient and continuous assessment of the integrity of massive structures like reinforced concrete (RC) bridges. However, the reliability of the classification between 'possibly physical' and 'certainly spurious' modes using binary clustering may be limited. This study applies Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to overcome this limitation and provides automated outlier detection and removal.
Article
Construction & Building Technology
Marco Civera, Vezio Mugnaini, Luca Zanotti Fragonara
Summary: Structural health monitoring is an important research topic in civil, mechanical and aerospace engineering. Output-only techniques are suitable for large civil structures and can be automated using artificial intelligence and machine learning techniques for interpretation.
STRUCTURAL CONTROL & HEALTH MONITORING
(2022)
Article
Computer Science, Interdisciplinary Applications
Jie Kang, Li Liu, Yu-Pei Shao, Qing-Gang Ma
Summary: This paper proposes a non-stationary signal decomposition approach to remove harmonic responses in operational modal analysis for time-varying structures. The approach effectively distinguishes between harmonic and structural components, and can identify modal parameters for time-varying structures with close or even repeated modes. The method combines time-frequency representations and a time-frequency domain decomposition technique to achieve accurate modal identification results.
COMPUTERS & STRUCTURES
(2021)
Article
Chemistry, Analytical
Hongzu Li, Pierre Boulanger
Summary: Cardiovascular diseases are a leading cause of death worldwide, and early detection and treatment are crucial. This paper proposes a new method that combines a Short-Time Fourier Transform (STFT) spectrogram with handcrafted features to detect heart anomalies beyond the capabilities of commercial products.
Article
Engineering, Multidisciplinary
Piero Paialunga, Joseph Corcoran
Summary: This paper proposes a damage detection method using permanently installed guided wave sensors, which involves identifying changes in A-scan data from a defect-free state. The method utilizes the unique amplitude-temperature dependence and associated uncertainty of each location in an A-scan to determine an adaptive threshold. The performance of the method exceeds that of Optimal baseline subtraction (OBS) when the training set exceeds around 40 measurements.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Computer Science, Interdisciplinary Applications
Samir Tiachacht, Samir Khatir, Cuong Le Thanh, Ravipudi Venkata Rao, Seyedali Mirjalili, Magd Abdel Wahab
Summary: In this paper, a method combining MSEcr and SMA algorithm for damage detection, localization, and quantification is proposed. Experimental and simulation analyses show that this method can accurately predict the location and level of damage.
ENGINEERING WITH COMPUTERS
(2022)
Article
Chemistry, Multidisciplinary
Dario Fiandaca, Alberto Di Matteo, Bernardo Patella, Nadia Moukri, Rosalinda Inguanta, Daniel Llort, Antonio Mulone, Angelo Mulone, Soughah Alsamahi, Antonina Pirrotta
Summary: This paper introduces an integrated procedure for structural and material monitoring, combining innovative approaches based on Vehicle by Bridge Interaction and continuous monitoring of pH, chloride, and sulfate ions concentration in concrete.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Multidisciplinary
Feng-Liang Zhang, Dong-Kai Gu, Xiao Li, Xiao-Wei Ye, Huayi Peng
Summary: This article presents a novel damage detection method based on fundamental Bayesian two-stage model and sparse regularization, which improves the efficiency of vibration-based damage detection in SHM. The method combines the most probable value of modal parameters and the associated posterior uncertainty to investigate the effect of uncertainty on damage detection. The usage of sparse regularization decreases the complexity of modeling and avoids overfitting. The proposed method is verified by benchmark examples and applied in an experimental structure, showing better performance due to the consideration of uncertainty.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Engineering, Multidisciplinary
Mohtasham Khanahmadi, Borhan Mirzaei, Gholamreza Ghodrati Amiri, Majid Gholhaki, Omid Rezaifar
Summary: This study introduces a damage identification method to diagnose damaged regions in 3D sandwich panels using modal dynamic data. By defining an Irregularity Detection Index, it is possible to identify damaged locations, and the index value increases with the severity of the damage.
Article
Engineering, Multidisciplinary
Bjorn T. Svendsen, Ole oiseth, Gunnstein T. Froseth, Anders Ronnquist
Summary: This paper presents a novel hybrid structural health monitoring (SHM) framework for damage detection in bridges using numerical and experimental data. The framework combines a calibrated numerical finite element (FE) model and a machine learning algorithm to establish relevant structural damage based on the evaluation of different cases.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Clinical Neurology
Vicente M. Garcao, Mariana Abreu, Ana R. Peralta, Carla Bentes, Ana Fred, Hugo P. da Silva
Summary: This study proposed a video-based seizure detection method for epilepsy patients using optical flow, principal component analysis, independent component analysis, and machine learning. The method achieved high accuracy and robustness, accurately detecting the onset and offset of seizures.
Article
Engineering, Civil
Qian Fan, Zhenjian Chen, Zhanghua Xia, Wei Zhang
Summary: This paper proposes a novel two-stage structural damage detection strategy using VMD, FastICA and ESSAWOA algorithm. The first stage applies VMD and FastICA to process the initial response signals of the structure and detect the damage time. In the second stage, ESSAWOA algorithm is used to identify the structural parameters and determine the location and extent of the damage. Numerical simulation and experimental verification show that the proposed strategy can effectively detect the damage information of the structure.
JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING
(2023)
Article
Engineering, Mechanical
Soroosh Kamali, Mohammad Ali Hadianfard
Summary: In this work, a novel method called SOMI is proposed for estimating the modal parameters of structures. SOMI can accurately estimate modal damping ratios and mode shapes using the Frequency Domain Decomposition principles. It eliminates the problems encountered by the FDD method and achieves better accuracy and robustness in noisy conditions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Review
Engineering, Multidisciplinary
Mattia Francesco Bado, Joan Ramon Casas, Judit Gomez
Summary: Distributed optical fiber sensors have potential in civil engineering, but anomaly readings are a common issue that requires computer-aided post-processing for resolution. Different algorithms are discussed in this study to eliminate disruptive anomalies and improve monitoring accuracy.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2021)
Article
Engineering, Civil
Mattia Francesco Bado, Joan R. Casas, Gintaris Kaklauskas
Summary: Distributed Optical Fiber Sensors (DOFS) are increasingly popular strain monitoring tools applied to Structural Health Monitoring (SHM) of Reinforced Concrete (RC) structures, measuring not only on the external surfaces but also the strains present on embedded reinforcement bars (rebars).
ENGINEERING STRUCTURES
(2021)
Review
Chemistry, Analytical
Mattia Francesco Bado, Joan R. Casas
Summary: This review article presents recent advancements in Structural Health Monitoring using Distributed Optical Fiber Sensors, showcasing their advanced features and deployment methodologies to contribute to the collective growth towards an efficient SHM.
Article
Chemistry, Analytical
Tian Peng, Maria Nogal, Joan R. Casas, Jose Turmo
Summary: The inverse problem of structural system identification is prone to ill-conditioning issues, and uncertainty quantification analysis is necessary to evaluate its impact on estimated parameters. The dynamic constrained observability method can compensate for the shortcomings of existing methods, and its correct performance and applicability are demonstrated through the analysis of a real bridge. The optimal sensor placement should consider not only the accuracy of sensors, but also the unknown structural part as epistemic uncertainty is removed with increasing knowledge of the structure.
Article
Construction & Building Technology
Mara Bartolozzi, Joan R. Casas, Marco Domaneschi
Summary: The research examines the effects of corrosion on the consequences of bond strength deterioration for a reinforced concrete bridge pier in a seismic affected area. It concludes that bond degradation is more critical for the safety of the pier than the effect of rebars cross-section loss.
STRUCTURAL CONCRETE
(2022)
Editorial Material
Engineering, Civil
Helder Sousa, Jochen Kohler, Joan R. Casas
STRUCTURE AND INFRASTRUCTURE ENGINEERING
(2022)
Article
Engineering, Civil
Rick M. Delgadillo, Fernando J. Tenelema, Joan R. Casas
Summary: In this paper, the non-linear and non-stationary dynamic response of bridges under operational loads is studied, and a novel technique called Improved Completed Ensemble EMD with Adaptive Noise (ICEEMDAN) is used to decompose the signals into intrinsic mode functions (IMF) and obtain their corresponding Hilbert spectra. The marginal Hilbert spectrum (MHS) and instantaneous phase difference (IPD) are proposed as total damage indicators (DI) for damage detection, localization, and quantification under transient vibration due to traffic. Experimental and real case results demonstrate the robustness and sensitivity of ICEEMDAN in addressing damage location.
STRUCTURE AND INFRASTRUCTURE ENGINEERING
(2023)
Review
Chemistry, Analytical
Mattia Francesco Bado, Daniel Tonelli, Francesca Poli, Daniele Zonta, Joan Ramon Casas
Summary: This article discusses the application of the Digital Twin model in maintaining and optimizing infrastructure. The model digitally reconstructs real-life assets using data sampled by a sensor network, providing functionalities for monitoring and decision-making, as well as predicting and compensating for structural behavior.
Article
Construction & Building Technology
Hang Su, Qingtian Su, Joan R. Casas, Xu Jiang, Guandong Zhou
Summary: This paper discusses the analytical models for calculating composite girders with partial shear connection and introduces a project case using partial shear connection. A nonlinear finite element model is established to simulate the mechanical properties and structural performance. The influence of partial shear connection on various parameters is analyzed, showing that it can effectively reduce crack and stress concentration areas, but shear stiffness mutation increases stress at the edge of the shear connection region.
STRUCTURAL ENGINEERING INTERNATIONAL
(2022)
Article
Engineering, Civil
Jian Tang, Qingtian Su, Hang Su, Joan R. R. Casas
Summary: In this paper, a partial connection-prestressing method is proposed to decrease the prestress transferred to steel girders in the negative moment region of steel-concrete composite bridges. Experimental results show that the PCP method enhances the cracking load (P-cr) by 3.1 times without decreasing the ultimate strength and overall stiffness of the composite girders.
JOURNAL OF BRIDGE ENGINEERING
(2023)
Article
Chemistry, Analytical
Duo Liu, Shengtao Li, Joan R. R. Casas, Xudong Chen, Yangyang Sun
Summary: Distributed fiber optic sensors can accurately and extensively detect structural cracks and deformation. This study used optical fiber technology to monitor a segmental prestressed bent cap with a large dry joint, comparing it with a monolithic cast-in-place counterpart. The results showed that DFOS technology can successfully analyze the stress state of the segmental beam with shear key joints, including strain distributions, crack patterns, and damage processes. The DFOS data confirmed that the shear key joint is a weak point causing high stress concentration and higher damage rate compared to cast-in-place beams.
Article
Chemistry, Analytical
Sardorbek Niyozov, Marco Domaneschi, Joan R. Casas, Rick M. Delgadillo
Summary: This paper proposes and tests a methodology for detecting and localizing damage in bridges under both traffic and environmental variability considering non-stationary vehicle-bridge interaction. The proposed method is validated using a numerical bridge benchmark. The results show that machine learning algorithms applied to bridge damage detection appear to be a promising technique to efficiently solve the problem's complexity when both operational and environmental variability are included in the recorded data.
Article
Chemistry, Physical
Bing Shangguan, Qingtian Su, Joan R. Casas, Hang Su, Shengyun Wang, Rongxin Zhao
Summary: In this study, a static load test was conducted on a composite segment of a hybrid bridge with a full section. The test results showed that the concrete filling prevented extensive buckling of the steel flange, significantly improving the load-carrying capacity of the steel-concrete joint. Strengthening the interaction between the steel and concrete also helped prevent interlayer slip and increased flexural stiffness. These findings provide important insights for the rational design of steel-concrete joints in hybrid girder bridges.
Article
Construction & Building Technology
Alfred Strauss, Andre Orcesi, Andreas Lampropoulos, Bruno Briseghella, Dan M. Frangopol, Helder S. Sousa, Joan Casas, Jose C. Matos, Kristian Schellenberg, Matias Valenzuela, Mitsuyoshi Akiyama, Poul Linneberg, Rade Hajdin, Thomas Moser
Summary: Infrastructure systems, such as bridges, play a crucial role in the economic growth and sustainable development of countries. Decision-making in infrastructure management involves a combination of qualitative and quantitative data, models, and expert judgement. However, there is still a gap between implemented decision-making models and research, which calls for further research and training.
STRUCTURAL ENGINEERING INTERNATIONAL
(2023)
Article
Engineering, Civil
Rick M. Delgadillo, Fernando J. Tenelema, Joan R. Casas
Summary: One of the main challenges in bridge damage identification is acquiring sensitive damage features that are not influenced by operational and environmental effects or noise. Principal Component Analysis (PCA) is used to remove environmental variability and obtain damage-sensitive indices. The combined use of PCA, Hilbert Huang Transform (HHT) and Variational Mode Decomposition (VMD) is applied to eliminate environmental influence in transient vibrations due to traffic, and the Instantaneous Phase Difference (IPD) is used as a novel vibration damage feature in non-stationary vibrations.
STRUCTURE AND INFRASTRUCTURE ENGINEERING
(2023)
