Article
Engineering, Chemical
Zhijiang Lou, Youqing Wang, Shan Lu, Pei Sun
Summary: This study proposes a novel robust PCA scheme called MRPCA, which adopts a difference selection mechanism for outlier samples in the offline training stage and an outlier detection mechanism for distinguishing outliers from fault data in the online monitoring stage. With these mechanisms, MRPCA achieves high fault detection rates and low false alarm rates in tests.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2021)
Article
Engineering, Multidisciplinary
Ana Fernandez-Navamuel, Filipe Magalhaes, Diego Zamora-Sanchez, Angel J. Omella, David Garcia-Sanchez, David Pardo
Summary: This paper proposes a Deep Learning Enhanced Principal Component Analysis (PCA) approach for outlier detection to assess the structural condition of bridges. By adding residual connections, the outlier detection ability of the network is enhanced, allowing for the detection of lighter damages.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2022)
Review
Instruments & Instrumentation
Hamed Momeni, Arvin Ebrahimkhanlou
Summary: This paper reviews high-dimensional data analytic methods and their applications in structural health monitoring and non-destructive evaluation. These methods can be used for dimension reduction and damage detection, providing vast opportunities in SHM/NDE.
SMART MATERIALS AND STRUCTURES
(2022)
Article
Engineering, Civil
Sikandar Sajid, Luc Chouinard
Summary: An efficient and fully automated global health monitoring approach is proposed as an alternative to non-destructive test methods for detecting and quantifying local defects in reinforced concrete slabs. The use of structural health methodologies on high-frequency measurements is shown to be an efficient way to detect and delineate local defects. Experimental measurements on a reinforced concrete slab with built-in defects demonstrate the effectiveness of the proposed approach. The detection and localization of local defects using the automated health monitoring approach is comparable to traditional local NDT methods.
JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING
(2023)
Article
Engineering, Civil
Sneha Prasad, Chih-Hung Chiang, David Kumar, Sumit Kalra, Arpit Khandelwal
Summary: This study presents a feature-based methodology that employs the KAZE feature detection and descriptor algorithm to identify natural patterns and measure displacements of large structures. The methodology accurately determined the vibrational characteristics of a wind turbine tower and blade using natural patterns. The implemented approach demonstrated superior accuracy and robustness compared to other algorithms.
JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING
(2023)
Article
Computer Science, Artificial Intelligence
Pei Li, Wenlin Zhang, Chengjun Lu, Rui Zhang, Xuelong Li
Summary: A novel robust kernel principal component analysis method with optimal mean (RKPCA-OM) is proposed to enhance the robustness of KPCA by automatically eliminating the optimal mean. The theoretical proof guarantees the convergence of the algorithm and the obtained optimal subspaces and means. Exhaustive experimental results validate the superiority of the proposed method.
Article
Engineering, Multidisciplinary
Kang Yang, Sungwon Kim, Rongting Yue, Haotian Yue, Joel B. Harley
Summary: The article introduces a long short-term principal component analysis reconstruction method for detecting synthetic damage under highly variable environmental conditions, achieving a near 0.95 area under curve score without the need for any temperature or other compensation methods.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2022)
Article
Computer Science, Information Systems
Alessio Burrello, Alex Marchioni, Davide Brunelli, Simone Benatti, Mauro Mangia, Luca Benini
Summary: Principal component analysis (PCA) is widely used for dimensionality reduction, but its computational cost and memory requirements hinder its adoption in resource-constrained embedded platforms. The history PCA (HPCA) algorithm, with a parallel and memory-efficient implementation, achieves data compression with accurate results in a structural health monitoring (SHM) application.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Chemistry, Multidisciplinary
Xin Sha, Naizhe Diao
Summary: In this study, a two-level feature extraction method based on 21-norm is proposed to remove noises and outliers in industrial data and extract key features. Extensive experiments demonstrate that this method is more effective than other state-of-the-art fault detection methods.
Article
Construction & Building Technology
Nitin Nagesh Kulkarni, Koosha Raisi, Nicholas A. Valente, Jason Benoit, Tzuyang Yu, Alessandro Sabato
Summary: This research presents an automated approach for detecting sub-pavement voids using infrared (IR) images collected with an unmanned aerial vehicle. The approach achieved comparable accuracy to ground penetrating radar while reducing inspection time. The method outperformed other deep learning algorithms by 24% in detecting both visible and sub-pavement defects.
AUTOMATION IN CONSTRUCTION
(2023)
Article
Materials Science, Characterization & Testing
Nitin Nagesh Kulkarni, Shweta Dabetwar, Jason Benoit, Tzuyang Yu, Alessandro Sabato
Summary: This study improves the accuracy of infrared thermography (IRT) in detecting voids underneath roadways by comparing and validating three advanced image-processing techniques. Among them, sparse principal component thermography (S-PCT) allows determining the physical size of voids with an accuracy above 95%. This research provides a foundation for advancing the use of IRT as a more accurate and cost-effective method for road condition monitoring.
NDT & E INTERNATIONAL
(2022)
Review
Construction & Building Technology
Gabriel M. F. Ramalho, Antonio M. Lopes, Lucas F. M. da Silva
Summary: Lamb waves have shown promising results in NDT and SHM, particularly in monitoring the health of adhesive joints. The paper covers equipment, testing procedures and techniques, as well as discussions on signal processing and statistical methods. Further research is needed in the area of monitoring weak adhesion using Lamb wave-based SHM methods.
STRUCTURAL CONTROL & HEALTH MONITORING
(2022)
Article
Engineering, Multidisciplinary
Abderrahim Abbassi, Niklas Romgens, Franz Ferdinand Tritschel, Nikolai Penner, Raimund Rolfes
Summary: The implementation of machine learning methods for structural health monitoring has proven to be powerful in detecting damage and compensating for environmental conditions. The use of guided waves and unsupervised dimensionality reduction learning methods can effectively detect damage and distinguish positions without additional temperature compensation techniques.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Chemistry, Analytical
Gilbert A. Angulo-Saucedo, Jersson X. Leon-Medina, Wilman Alonso Pineda-Munoz, Miguel Angel Torres-Arredondo, Diego A. Tibaduiza
Summary: Improvements in computing capacity have enabled the development of machine learning algorithms for structural health monitoring (SHM). This study focuses on configuring a data acquisition system, developing a damage classification methodology, and using machine learning algorithms to detect and classify damages. The results validate the effectiveness of the SKN and XYF networks in damage classification tasks.
Article
Chemistry, Medicinal
Jingyu Zhang, Jinxin Che, Xiaomin Luo, Mingfei Wu, Weijuan Kan, Yuheng Jin, Hanlin Wang, Ao Pang, Cong Li, Wenhai Huang, Shenxin Zeng, Weihao Zhuang, Yizhe Wu, Yongjin Xu, Yubo Zhou, Jia Li, Xiaowu Dong
Summary: In this study, dimensionality reduction analysis and model molecule validation were used to identify key structural features for improving the oral absorption of BTK-PROTACs. The newly discovered BTK-PROTACs B1 and B2 were optimized based on the results. Compound C13 with improved oral bioavailability, high BTK degradation activity, and selectivity was discovered. It showed inhibitory effects against hematologic cancer cells and attenuated the BTK-related signaling pathway. The oral administration of C13 effectively reduced BTK protein levels and suppressed tumor growth. This study led to the discovery of a new orally bioavailable BTKPROTAC for the treatment of lymphoma.
JOURNAL OF MEDICINAL CHEMISTRY
(2022)
Article
Chemistry, Multidisciplinary
Kevin M. M. Wyss, John T. T. Li, Paul A. A. Advincula, Ksenia V. Bets, Weiyin Chen, Lucas Eddy, Karla J. J. Silva, Jacob L. L. Beckham, Jinhang Chen, Wei Meng, Bing Deng, Satish Nagarajaiah, Boris I. I. Yakobson, James M. M. Tour
Summary: A rapid and scalable method, using flash Joule heating (FJH), has been developed to produce graphitic 1D materials (F1DM) from polymers. By tuning the parameters, F1DM with controllable diameters and morphologies can be obtained, and hybrid materials with turbostratic graphene can also be synthesized. The F1DM outperform commercially available carbon nanotubes in nanocomposites. Compared to current synthetic strategies, FJH synthesis significantly reduces energy demand and global-warming potential, offering a cost-effective and sustainable route to convert waste plastic into valuable nanomaterials.
ADVANCED MATERIALS
(2023)
Article
Engineering, Civil
Debasish Jana, Satish Nagarajaiah
Summary: Full-field online sensing provides dense spatial information of vibrating structures in real-time. Computer vision-based technologies can capture dense responses, but long-term implementation of such techniques could be expensive and impractical. To address these problems, we propose a framework to estimate full-field vibration responses from a few sensors. This framework does not require any prior information on the structural model and shows excellent potential in health monitoring and control of various systems.
ENGINEERING STRUCTURES
(2023)
Article
Materials Science, Multidisciplinary
Paul A. Advincula, Wei Meng, Lucas J. Eddy, Jacob L. Beckham, Ivan R. Siqueira, Duy Xuan Luong, Weiyin Chen, Matteo Pasquali, Satish Nagarajaiah, James M. Tour
Summary: Graphene has been proven to be a valuable additive for composites, but its expensive synthesis has hindered its industrial application. However, a method called Flash Joule heating can rapidly synthesize graphene from coal materials like metallurgical coke. This study investigates the properties of graphene-epoxy composites with higher nanofiller content than previously reported. The results show that these composites exhibit improved mechanical properties and reduced environmental impact.
MACROMOLECULAR MATERIALS AND ENGINEERING
(2023)
Article
Engineering, Mechanical
Sudheendra Herkal, Muhammad M. Rahman, Satish Nagarajaiah, Vijay Vedhan Jayanthi Harikrishnan, Pulickel Ajayan
Summary: Schwarzites are 3D solids with negative Gaussian curvatures that possess unique mechanical properties such as high ductility and strength. The energy dissipation mechanism of Schwarzites allows for the removal of energy from dynamic systems and reduction of system response. Experimental results show that Schwarzite geometry can increase the damping ratio by almost 80% with the same material volume as a solid support. Additionally, Schwarzites exhibit vibration isolation properties.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Materials Science, Characterization & Testing
Homin Song, Yongchao Yang
Summary: A Bayesian deep learning approach is proposed to quantify and interpret uncertainties in super-resolution guided wave array imaging. The approach successfully quantifies two types of uncertainties: aleatoric uncertainty inherent in the data and epistemic uncertainty associated with the Bayesian deep learning model.
NDT & E INTERNATIONAL
(2023)
Article
Chemistry, Analytical
Debasish Jana, Satish Nagarajaiah
Summary: In civil, mechanical, and aerospace structures, full-field measurement is necessary for precise damage estimation and control purposes. Conventional sensing methods require dense installation of contact-based sensors, which is impractical in real-life scenarios. This paper proposes a technique to accurately estimate the full-field responses of a structural system using a few randomly placed contact/non-contact sensors. The proposed method, based on compressive sensing, demonstrates significant potential in health monitoring and control of engineering systems.
Article
Engineering, Civil
Yongkui Wen, Lin Chen, Satish Nagarajaiah
Summary: Damped outrigger is an effective approach for reducing dynamic responses of tall buildings. This study focuses on generalized damped outrigger (GDO) systems, which consist of a damper, a negative stiffness device and an inerter. By optimizing GDOs at different floors of the tall building, significant improvements in multimode damping effects can be achieved.
INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS
(2023)
Article
Engineering, Mechanical
Sutanu Bhowmick, Satish Nagarajaiah, Anastasios Kyrillidis
Summary: Full-field discrete measurements of continuous spatiotemporal processes generate large datasets. Previous regression-based and deep learning-based methods fail to estimate higher-order PDE models in the presence of moderate noise. The proposed SNAPE method addresses these drawbacks by simultaneously fitting basis functions and estimating parameters of PDEs.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Mechanical
Charle Dorn, Yongchao Yang
Summary: Identifying modal parameters is crucial for modal analysis and structural dynamics modeling based on vibration measurements. This study presents an approach that quantifies spatial features of high-resolution response measurements to enable automated identification of modal parameters. It is found that the local variances of the physical and spurious mode shapes are significantly different, especially with high spatial resolution measurements, allowing for effective identification of physical modes from spurious modes.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Civil
Liangkun Wang, Ying Zhou, Satish Nagarajaiah, Weixing Shi
Summary: In this study, a bi-directional semi-active TMD with adaptive stiffness, variable mass, and semi-active damping is proposed to improve the seismic protection performance. The BSTMD can adapt to X and Y directional frequencies simultaneously and reduce the stroke of unidirectional STMD. Numerical results show that the BSTMD has a comparable control effect with less electromechanical devices.
ENGINEERING STRUCTURES
(2023)
Article
Engineering, Civil
Sutanu Bhowmick, Satish Nagarajaiah
Summary: The authors introduced a new method called SimultaNeous Basis Function Approximation and Parameter Estimation (SNAPE) to address the issues of previous methods in estimating the parameters of higher-order PDE models and dealing with noise. SNAPE fits basis functions to the measured response and simultaneously infers the parameters of the PDE model, demonstrating its applicability on various boundary conditions.
ENGINEERING STRUCTURES
(2023)
Article
Acoustics
Lin Chen, Zhanhang Liu, Yiqing Zou, Meng Wang, Satish Nagarajaiah, Feifei Sun, Limin Sun
Summary: The negative stiffness mechanism improves the damping performance of dampers on a stay cable. The study provides a practical negative stiffness device with adjustable negative stiffness and validates its effect through experiments. The device is combined with a viscoelastic damper in parallel or series for cable damping improvement.
JOURNAL OF SOUND AND VIBRATION
(2023)
Article
Materials Science, Multidisciplinary
Paul Andrade Advincula, Wei Meng, Jacob L. Beckham, Satish Nagarajaiah, James M. Tour
Summary: CO2 emissions and waste plastic contribute greatly to environmental problems, and researchers are developing technologies for capturing, sequestrating, and utilizing CO2. Two methods, molten carbonate electrolysis and flash Joule heating, have shown promise in converting gaseous CO2 into solid carbon feedstocks and flash graphene. The combination of flash graphene and waste plastic as a reinforcing additive in composite applications can increase material properties and reduce CO2 emissions, water consumption, and energy consumption.
MACROMOLECULAR MATERIALS AND ENGINEERING
(2023)
Article
Chemistry, Analytical
Debasish Jana, Satish Nagarajaiah
Summary: In the field of structural vibration response sensing, mobile sensors provide numerous benefits such as acquiring dense spatial information and not being limited to a specific structure. This study introduces a formation control framework for automatic multi-agent mobile sensing, which effectively controls the behavior of multi-agent systems for structural response sensing purposes. The proposed method leverages vibration data collected by these mobile sensors to estimate the full-field vibration response of structures, achieving high reconstruction accuracy.
Article
Chemistry, Analytical
Ashish Pal, Wei Meng, Satish Nagarajaiah
Summary: In this study, a Convolutional Neural Network (CNN) is developed to detect subsurface damage (SSD) using surface strain measurements. The trained network is capable of accurately detecting damage in different materials and can be used for subsurface crack detection and localization in real-life scenarios.