scholarly journals Connecting concrete technology and machine learning: proposal for application of ANNs and CNT/concrete composites in structural health monitoring

RSC Advances ◽  
2020 ◽  
Vol 10 (39) ◽  
pp. 23038-23048
Author(s):  
Sofija Kekez ◽  
Jan Kubica
Keyword(s):  
Machine Learning ◽  
Carbon Nanotube ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Large Scale ◽  
Concrete Structures ◽  
Concrete Technology ◽  
Structural Health

Carbon nanotube/concrete composite possesses piezoresistivity i.e. self-sensing capability of concrete structures even in large scale.

scholarly journals Structural Health Monitoring Using Machine Learning and Cumulative Absolute Velocity Features

2021 ◽  
Vol 11 (12) ◽  
pp. 5727
Author(s):  
Sifat Muin ◽  
Khalid M. Mosalam
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Degree Of Freedom ◽  
Absolute Velocity ◽  
Support Vector ◽  
Damage State ◽  
Structural Health ◽  
Cumulative Absolute Velocity

Machine learning (ML)-aided structural health monitoring (SHM) can rapidly evaluate the safety and integrity of the aging infrastructure following an earthquake. The conventional damage features used in ML-based SHM methodologies face the curse of dimensionality. This paper introduces low dimensional, namely, cumulative absolute velocity (CAV)-based features, to enable the use of ML for rapid damage assessment. A computer experiment is performed to identify the appropriate features and the ML algorithm using data from a simulated single-degree-of-freedom system. A comparative analysis of five ML models (logistic regression (LR), ordinal logistic regression (OLR), artificial neural networks with 10 and 100 neurons (ANN10 and ANN100), and support vector machines (SVM)) is performed. Two test sets were used where Set-1 originated from the same distribution as the training set and Set-2 came from a different distribution. The results showed that the combination of the CAV and the relative CAV with respect to the linear response, i.e., RCAV, performed the best among the different feature combinations. Among the ML models, OLR showed good generalization capabilities when compared to SVM and ANN models. Subsequently, OLR is successfully applied to assess the damage of two numerical multi-degree of freedom (MDOF) models and an instrumented building with CAV and RCAV as features. For the MDOF models, the damage state was identified with accuracy ranging from 84% to 97% and the damage location was identified with accuracy ranging from 93% to 97.5%. The features and the OLR models successfully captured the damage information for the instrumented structure as well. The proposed methodology is capable of ensuring rapid decision-making and improving community resiliency.

scholarly journals Infrared Thermography Measurement for Vibration-Based Structural Health Monitoring in Low-Visibility Harsh Environments

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7067
Author(s):  
Jia-Hao He ◽  
Ding-Peng Liu ◽  
Cheng-Hsien Chung ◽  
Hsin-Haou Huang
Keyword(s):  
Structural Health Monitoring ◽  
Infrared Thermography ◽  
Mode Shape ◽  
Health Monitoring ◽  
Large Scale ◽  
Frame Structure ◽  
Harsh Environments ◽  
Thermal Imager ◽  
Heat Sources ◽  
Structural Health

In this study, infrared thermography is used for vibration-based structural health monitoring (SHM). Heat sources are employed as sensors. An acrylic frame structure was experimentally investigated using the heat sources as structural marker points to record the vibration response. The effectiveness of the infrared thermography measurement system was verified by comparing the results obtained using an infrared thermal imager with those obtained using accelerometers. The average error in natural frequency was between only 0.64% and 3.84%. To guarantee the applicability of the system, this study employed the mode shape curvature method to locate damage on a structure under harsh environments, for instance, in dark, hindered, and hazy conditions. Moreover, we propose the mode shape recombination method (MSRM) to realize large-scale structural measurement. The partial mode shapes of the 3D frame structure are combined using the MSRM to obtain the entire mode shape with a satisfactory model assurance criterion. Experimental results confirmed the feasibility of using heat sources as sensors and indicated that the proposed methods are suitable for overcoming the numerous inherent limitations associated with SHM in harsh or remote environments as well as the limitations associated with the SHM of large-scale structures.

Structural Health Monitoring for Carbon Fiber/Carbon Nanotube (CNT)/Epoxy Composite Sensor

2006 ◽  
Vol 321-323 ◽  
pp. 290-293 ◽  
Author(s):  
Sang Il Lee ◽  
Dong Jin Yoon
Keyword(s):  
Electrical Resistivity ◽  
Carbon Nanotube ◽  
Carbon Fiber ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Epoxy Composite ◽  
Structural Safety ◽  
Structural Health ◽  
Nanotube Composites ◽  
Carbon Fiber Epoxy

Structural health monitoring for carbon nanotube (CNT)/carbon fiber/epoxy composite was verified by the measurement of electrical resistivity. This study has focused on the preparation of carbon nanotube composite sensors and their application for structural health monitoring. The change of the electrical resistance was measured by a digital multimeter under tensile loads. Although a carbon fiber was broken, the electrical connection was still kept by distributed CNT particles in the model composites. As the number of carbon fiber breakages increased, electrical resistivity was stepwise increased. The CNT composites were well responded with fiber damages during the electro-micromechnical test. Carbon nanotube composites can be useful sensors for structural health monitoring to diagnose a structural safety and to prevent a collapse.

Structural health monitoring of composites from carbon nanotube coated e-glass fibre

2021 ◽  
Vol 5 (1/2) ◽  
pp. 164
Author(s):  
Sergio Rafael Rodriguez ◽  
Sidney Wong ◽  
Omar Dwidar ◽  
Amro El Badawy ◽  
Ashraf Elbarbary ◽  
...  
Keyword(s):  
Carbon Nanotube ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Glass Fibre ◽  
Structural Health

scholarly journals Application of Machine Learning with Impedance Based Techniques for Structural Health Monitoring of Civil Infrastructure

2019 ◽  
Vol 8 (6S4) ◽  
pp. 1139-1148
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Soft Computing ◽  
Health Monitoring ◽  
Multiple Cracks ◽  
Civil Infrastructure ◽  
Structural Health ◽  
Soft Computing Techniques

Increased attentiveness on the environmental and effects of aging, deterioration and extreme events on civil infrastructure has created the need for more advanced damage detection tools and structural health monitoring (SHM). Today, these tasks are performed by signal processing, visual inspection techniques along with traditional well known impedance based health monitoring EMI technique. New research areas have been explored that improves damage detection at incipient stage and when the damage is substantial. Addressing these issues at early age prevents catastrophe situation for the safety of human lives. To improve the existing damage detection newly developed techniques in conjugation with EMI innovative new sensors, signal processing and soft computing techniques are discussed in details this paper. The advanced techniques (soft computing, signal processing, visual based, embedded IOT) are employed as a global method in prediction, to identify, locate, optimize, the damage area and deterioration. The amount and severity, multiple cracks on civil infrastructure like concrete and RC structures (beams and bridges) using above techniques along with EMI technique and use of PZT transducer. In addition to survey advanced innovative signal processing, machine learning techniques civil infrastructure connected to IOT that can make infrastructure smart and increases its efficiency that is aimed at socioeconomic, environmental and sustainable development.

scholarly journals Research on the Progress of Interdigital Transducer (IDT) for Structural Damage Monitoring

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wang Ziping ◽  
Xiong Xiqiang ◽  
Qian Lei ◽  
Wang Jiatao ◽  
Fei Yue ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Large Scale ◽  
Safety Information ◽  
Health And Safety ◽  
Guided Wave ◽  
Structural Safety ◽  
Structural Health ◽  
Damage Monitoring

In the application of Structural Health Monitoring (SHM) methods and related technologies, the transducer used for electroacoustic conversion has gradually become a key component of SHM systems because of its unique function of transmitting structural safety information. By comparing and analyzing the health and safety of large-scale structures, the related theories and methods of Structural Health Monitoring (SHM) based on ultrasonic guided waves are studied. The key technologies and research status of the interdigital guided wave transducer arrays which used for structural damage detection are introduced. The application fields of interdigital transducers are summarized. The key technical and scientific problems solved by IDT for Structural Damage Monitoring (SHM) are presented. Finally, the development of IDT technology and this research project are summarised.

scholarly journals A multi-way data analysis approach for structural health monitoring of a cable-stayed bridge

2018 ◽  
Vol 18 (1) ◽  
pp. 35-48 ◽  
Author(s):  
Mehrisadat Makki Alamdari ◽  
Nguyen Lu Dang Khoa ◽  
Yang Wang ◽  
Bijan Samali ◽  
Xinqun Zhu
Keyword(s):  
Data Analysis ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Large Scale ◽  
Damage Identification ◽  
Tensor Analysis ◽  
Support Vector ◽  
Damage Scenarios ◽  
Structural Health ◽  
Cable Stayed Bridge

A large-scale cable-stayed bridge in the state of New South Wales, Australia, has been extensively instrumented with an array of accelerometer, strain gauge, and environmental sensors. The real-time continuous response of the bridge has been collected since July 2016. This study aims at condition assessment of this bridge by investigating three aspects of structural health monitoring including damage detection, damage localization, and damage severity assessment. A novel data analysis algorithm based on incremental multi-way data analysis is proposed to analyze the dynamic response of the bridge. This method applies incremental tensor analysis for data fusion and feature extraction, and further uses one-class support vector machine on this feature to detect anomalies. A total of 15 different damage scenarios were investigated; damage was physically simulated by locating stationary vehicles with different masses at various locations along the span of the bridge to change the condition of the bridge. The effect of damage on the fundamental frequency of the bridge was investigated and a maximum change of 4.4% between the intact and damage states was observed which corresponds to a small severity damage. Our extensive investigations illustrate that the proposed technique can provide reliable characterization of damage in this cable-stayed bridge in terms of detection, localization and assessment. The contribution of the work is threefold; first, an extensive structural health monitoring system was deployed on a cable-stayed bridge in operation; second, an incremental tensor analysis was proposed to analyze time series responses from multiple sensors for online damage identification; and finally, the robustness of the proposed method was validated using extensive field test data by considering various damage scenarios in the presence of environmental variabilities.

Enhancing Vibration-Based Structural Health Monitoring via Edge Computing: A Tiny Machine Learning Perspective

2021 ◽  
Author(s):  
Federica Zonzini ◽  
Francesca Romano ◽  
Antonio Carbone ◽  
Matteo Zauli ◽  
Luca De Marchi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Network Architecture ◽  
Binary Classification ◽  
Low Cost ◽  
Classification Problems ◽  
Computationally Efficient ◽  
Structural Health

Abstract Despite the outstanding improvements achieved by artificial intelligence in the Structural Health Monitoring (SHM) field, some challenges need to be coped with. Among them, the necessity to reduce the complexity of the models and the data-to-user latency time which are still affecting state-of-the-art solutions. This is due to the continuous forwarding of a huge amount of data to centralized servers, where the inference process is usually executed in a bulky manner. Conversely, the emerging field of Tiny Machine Learning (TinyML), promoted by the recent advancements by the electronic and information engineering community, made sensor-near data inference a tangible, low-cost and computationally efficient alternative. In line with this observation, this work explored the embodiment of the One Class Classifier Neural Network, i.e., a neural network architecture solving binary classification problems for vibration-based SHM scenarios, into a resource-constrained device. To this end, OCCNN has been ported on the Arduino Nano 33 BLE Sense platform and validated with experimental data from the Z24 bridge use case, reaching an average accuracy and precision of 95% and 94%, respectively.

Structural Health Monitoring Based on Electrical Impedance of a Carbon Nanotube Neuron

2006 ◽  
pp. 140-145
Author(s):  
In Pil Kang ◽  
Jong Won Lee ◽  
Gyeong Rak Choi ◽  
Joo Yung Jung ◽  
Sung Ho Hwang ◽  
...  
Keyword(s):  
Carbon Nanotube ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Electrical Impedance ◽  
Structural Health
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