Real-time Structural Monitoring Research Articles

Crack Detection of Structures using Artificial Intelligence System

Abstract: Structural Health Monitoring (SHM) having wider scope of operations such as structural audit, rehabilitation of structures and along with it a major scope of crack detection is also included. Effective crack detection is vital for ensuring the safety and longevity of civil engineering structures. Crack Detection in existing methods is tedious process, it requires costly equipment’s as well as time consuming, and inaccuracy in mechanical equipment’s. The technological advancements especially in the field of Artificial Intelligence (AI) maybe an acceptable solution for crack identification through mobile app using Image processing techniques. The mobile app can be used to accurately identify crack pattern and measure the size of cracks in structures (concrete and steel). The app needed to capture and process data from diverse sources, such as images, in real time. It allows for real-time monitoring of structures and ensures safety and integrity of it. The image processing algorithm is used for to accurately identifying various types of cracks in different lighting, environmental conditions, effective and optimized solution. This project explained about real-time data processing for crack detection and its challenges. This demanded robust backend infrastructure and efficient data processing techniques to deliver prompt and accurate results. User Interface and Experience developing an intuitive and user-friendly interface for the app while maintaining high performance.

Recent Progress in Self-Powered Sensors for Structural and Human Monitoring Systems Using Thermoelectric Energy Harvesters

With the evolution of Internet of Things (IoT), it has become essential to build systems that enable real-time monitoring of structures, the environment, and the human body. Among them, various studies have identified self-powering of real-time monitoring systems as one of the most important technologies . Thermoelectric generators are reported to be one of the most suitable power sources for real-time monitoring systems because they have the advantage of converting thermal energy into electrical energy through temperature gradients without using additional power sources. In this review, we will discuss structural/environmental and human health monitoring systems utilizing thermoelectric generators (TEGs). First, we will introduce the type of thermoelectric generators. Then, we will provide an overview of the thermoelectric generators utilized in structural/environmental monitoring and biomonitoring systems. The latest applications of thermoelectric generator-based systems will also be introduced to showcase their potential in industrial applications.

Piezoresistive Behavior of a Conductive Polyurethane Based-Foam for Real-Time Structural Monitoring.

Smart flexible materials with piezoresistive property are increasingly used in the field of sensors. When embedded in structures, they would allow for in situ structural health monitoring and damage assessment of impact loading, such as crash, bird strikes and ballistic impacts; however, this could not be achieved without a deep characterization of the relation between piezoresistivity and mechanical behavior. The aim of this paper is to study the potential use of the piezoresistivity effect of a conductive foam made of a flexible polyurethane matrix filled with activated carbon for integrated structural health monitoring (SHM) and low-energy impact detection. To do so, polyurethane foam filled with activated carbon, namely PUF-AC, is tested under quasi-static compressions and under a dynamic mechanical analyzer (DMA) with in situ measurements of its electrical resistance. A new relation is proposed for describing the evolution of the resistivity versus strain rate showing that a link exists between electrical sensitivity and viscoelasticity. In addition, a first demonstrative experiment of feasibility of an SHM application using piezoresistive foam embedded in a composite sandwich structure is realized by a low-energy impact (2 J) test.

Real-time monitoring of structures under extreme loading using smart composite-based embeddable sensors

Cementitious composites reinforced with conductive nanoparticles, also known as smart composite-based sensors, are emerging as novel sensors for real-time monitoring of structures. The present study deals with the development of embeddable smart composite-based sensor (called as eSCS), for monitoring the damage evolution in large-scale beam-column (B-C) sub-assemblages. Firstly, a systematic approach has been developed to fabricate the sensors through incorporation of functionalized (-COOH) multi-walled carbon nanotubes (MWCNTs). The optimum dosage of CNTs required for developing sensors with maximum conductivity and piezo-resistivity was ascertained by determining the percolation threshold limit. The best performing eSCS was then successfully embedded at the critical locations of full-scale B-C joint specimens. The embedded sensors are used, for the first time, for damage monitoring of the B-C sub-assemblages subjected to reverse cyclic loadings. From the study, it is found that eSCS with 0.50 wt.% COOH-MWCNT shows excellent piezoresistive behaviour and gauge factors are found to be approximately 730, 457 and 396 for the applied compressive stress ranges of 2–4, 2–6 and 2–8 MPa, respectively. The embedded sensors showed the excellent strain sensing capability under reverse cyclic loading. The present study demonstrates the suitability of embedded eSCS sensors for robust structural health monitoring applications.

Phased array guided wave propagation in curved plates

For the monitoring of plate structures, phased array guided wave has great application prospects to effectively determine damage location and size. Locating damage via phased array focusing is a challenging task, especially in curved plates. Hence, a systematic understanding of the behavior of the phased array guided wave propagation in curved plates is needed. In this study, a phased array focusing method was proposed for the damage localization of curved plates. Different damage scenarios and phased array focusing schemes are investigated in the finite element plate models with different radii. A high-speed signal generation and acquisition system including PZT actuators and sensors is set up. Experimental tests are carried out on a curved aluminum plate with embedded piezoelectric elements to verify the finite element models. Parametric studies are then performed to evaluate the effect of different radii and damage scenarios on the behavior of phased array guided wave propagation in curved plates. Numerical simulations and experimental results show that the proposed PA method has the capabilities of wave focusing and damage identification in curved plates. The damage localization method using phased array guided wave has the potential to be applied to the real-time monitoring of structures with curved plates.

Electromechanical impedance-based embeddable smart composite for condition-state monitoring

In the present study, an alternating current (AC) based electro-mechanical impedance (EMI) technique was employed to evaluate the performance of smart cementitious composite-based impedance sensor (SCC- i S) over a frequency range, and then was employed as embeddable sensor for monitoring and assessing the health of concrete structure. For this purpose, a method for developing SCC-iS by reinforcing the carboxylic group (COOH-) multi-walled carbon nanotube (COOH-MWCNT) in the cementitious composite matrix is described. The optimal CNT concentration in SCC- i S was obtained based on the AC impedance measurement technique over a specified frequency range. After finding the optimal CNT concentration in SCC- i S, their piezoresistive behaviour (in terms of change in electrical impedance or electrical conductance) was examined using an electro-mechanical impedance measurement approach under uniaxial compressive loading. Finally, the developed sensor is employed as an embedded sensor for damage monitoring of structures under flexural loading. The acquired electrical impedance- and conductance- responses from the SCC- i S were utilized to evaluate the occurrence and progression of damage in real-time. The damage metric and the shift in frequency band were used to quantify the level of damage in the structures. From the experimental study, 0.50 wt% CNT concentration is found to be optimum for developing the efficient SCC- i S. Under the uniaxial compressive loading, the electrical variation in impedance (EVZ) and electrical variation in conductance (EVC) in SCC- i S exhibit excellent correlation with compressive strain and are capable of detecting the damage. The acquired signals from embedded SCC- i S show the effectiveness and sensitivity (in both qualitative and quantitative terms) for damage monitoring of structures; even they are capable of tracing the early-stage damage initiation in concrete structures. The electrical conductance spectra within a specified frequency range show a leftward shift, indicating the degradation in stiffness of concrete structure. Overall, the developed SCC- i S is the first of its kind and is found to be very effective and durable for real-time monitoring of structures, especially in the aggressive environment. • Development of smart nanoengineered composite using functionalized CNT. • Embedded sensors for capturing the electro-mechanical impedance (EMI) signature. • Sensor characterization and evaluation of piezoresistive behavior in frequency bands. • Demonstration for damage monitoring of structures under flexural loading. • Damage metric and shift in frequency band for accurate damage quantification.

Acoustic emission signal filtering based on artificial intelligence

One of the factors affecting the performance of the acoustic emission method used for real-time monitoring of structures is the interference of noise signals with the damage-related AE signals. Since the signals detected from the sensors are analyzed to obtain damage information, noise-related signals cause inaccurate inspection results. Especially, in the conditions where electrical or mechanical friction is prominent, noise causes big data and errors in damage detection In this study, to solve this problem, it is aimed to develop an AE signal filtering model by artificial intelligence that can filter the noise-related signals. For this purpose, a wide range of AE data were filtered using conventional filtering techniques and noise- and damage-related signals were used to train artificial intelligence. The noise-related signals were successfully separated from the damage-related signals.

High Resolution Distributed Optical Fiber Sensing Using Time-Expanded Phase-Sensitive Reflectometry

We have demonstrated a novel scheme for distributed optical fiber sensing based on the use of a dual frequency comb, which enables the development of a high-resolution (in the cm range) distributed sensor with significantly relaxed electronic requirements compared with previous schemes. This approach offers a promising solution for real time structure monitoring in a variety of fields, including transportation, manufacturing or mechatronics. In this work, we review the principle of operation of the technique, recent advances to improve its performance and different experimental tests.

Real-Time Structural Monitoring of the Multi-Point Hoisting of a Long-Span Converter Station Steel Structure.

In the process of using a long-span converter station steel structure, engineering disasters can easily occur. Structural monitoring is an important method to reduce hoisting risk. In previous engineering cases, the structural monitoring of long-span converter station steel structure hoisting is rare. Thus, no relevant hoisting experience can be referenced. Traditional monitoring methods have a small scope of application, making it difficult to coordinate monitoring and construction control. In the monitoring process, many problems arise, such as complicated installation processes, large-scale data processing, and large-scale installation errors. With a real-time structural monitoring system, the mechanical changes in the long-span converter station steel structure during the hoisting process can be monitored in real-time in order to achieve real-time warning of engineering disasters, timely identification of engineering issues, and allow for rapid decision-making, thus avoiding the occurrence of engineering disasters. Based on this concept, automatic monitoring and manual measurement of the mechanical changes in the longest long-span converter station steel structure in the world is carried out, and the monitoring results were compared with the corresponding numerical simulation results in order to develop a real-time structural monitoring system for the whole long-span converter station steel structure’s multi-point lifting process. This approach collects the monitoring data and outputs the deflection, stress, strain, wind force, and temperature of the long-span converter station steel structure in real-time, enabling real-time monitoring to ensure the safety of the lifting process. This research offers a new method and basis for the structural monitoring of the multi-point hoisting of a long-span converter station steel structure.

Structural damage identification based on fast S-transform and convolutional neural networks

Measured signals from installed sensors on structures cannot solely provide beneficial information about the presence of damage. Many signal processing methods have been developed to extract useful data from raw signals. However, each method has its advantages and disadvantages. In the present study, the fast S-transform signal processing method has been used for the damage detection of structures. Fast S-transform outperforms traditional S-Transform since it filters out unusable data using a scaling method. The method’s performance in obtaining correct natural frequencies of a structure has been evaluated using a 2 Degrees of Freedom (DOF) mass-spring dynamic system. Additionally, the ability of the method to detect damages in structural systems has been demonstrated using a 6 DOF structure subjected to Northridge earthquake record. The results of the mentioned numerical studies show that the fast S-transform can extract the accurate resonance frequencies of a structure, and also can detect the presence and the time of damage occurrence. In the experimental study, a 3 story plexi frame has been excited by a chirp sinusoidal signal. Furthermore, four different damage scenarios have been defined in the experimental study. To classify the four damage types, Convolutional Neural Network (CNN) has been used. The results of the experimental study show that fast S-transform can detect damages in real-time monitoring of structures, and the classification using CNN can identify the severity of damage.

Motorcycle Structural Fatigue Monitoring Using Smart Wheels

The paper is devoted to the measurement and to the processing of load spectra of forces and moments acting at the wheel hub of a motorcycle. Smart wheels (SWs) have been specifically developed for the scope. Throughout the paper, the extreme case of a race motorcycle is considered. Accurate load spectra were measured in two race circuits. Standardized load spectra are derived by processing measured data. A way to easily generalize the measured load spectra is proposed for the first time for motorcycles. Several loading conditions, related to the motorcycle straight line motion, cornering, curb hit and gear shift, are identified and extracted from the experimental measures. For each loading condition, by means of simple semi-analytical models (SAMs), a relationship is found between the vertical force on the wheel, the tilt angle of the motorcycle and the remaining forces and moments acting at the wheel hub. Such relationships are nothing else than the standardized load spectra. Additionally, a simple and efficient method based on smart wheels for real-time structural monitoring is proposed. Standardized load spectra prove to provide consistent results even when compared to real-time structural monitoring data. By means of the presented smart wheels, advanced lightweight motorcycle construction is enabled by derivation of standardized load spectra or real time estimation of the damage of structural components.

Wireless Sensor Networks for Continuous Structural Health Monitoring of Historic Masonry Towers

ABSTRACT The recent developments of micro-electro-mechanical systems and wireless sensor networks allow today the use of low-cost and small-size sensors for continuous monitoring of civil structures. Both these features are very important for the low impact of the sensor grid in heritage structures, ensuring a low-cost and sustainable dynamic monitoring system. Over the last 20 years the use of sensor networks for continuous monitoring has received a growing interest. Anyway, still numerous questions remain opened about the sensitivity of measurement devices, the optimization of number and positioning of sensors, the energy efficiency of the network, and the development of algorithms for real-time data analysis. This paper, based on the aforementioned motivations, discusses about a monitoring system made of micro-electro-mechanical sensors connected through a wireless network. The architecture of the wireless sensor network and the automatized procedure proposed for the continuous processing of the recorded signals are discussed and described with reference to an explicative masonry tower case study. It is believed that the proposed technologies can provide an economical and relatively non-invasive tool for real-time structural monitoring and that, moreover, the availability of large amounts of data from actual measurements can give effective information on the structural behaviour of historic constructions.

Structural Health Monitoring with Sensor Data and Cosine Similarity for Multi-Damages.

There is a large risk of damage, triggered by harsh ocean environments, associated with offshore structures, so structural health monitoring plays an important role in preventing the occurrence of critical and global structural failure from such damage. However, obstacles, such as applicability in the field and increasing calculation costs with increasing structural complexity, remain for real-time structure monitoring offshore. Therefore, this study proposes the comparison of cosine similarity with sensor data to overcome such challenges. As the comparison target, this method uses the rate of changes of natural frequencies before and after the occurrence of various damage scenarios, including not only single but multiple damages, which are organized by the experiment technique design. The comparison method alerts to the occurrence of damage using a normalized warning index, which enables workers to manage the risk of damage. By comparison, moreover, the case most similar with the current status is directly figured out without any additional analysis between monitoring and damage identification, which renders the damage identification process simpler. Plus, the averaged rate of errors in detection is suggested to evaluate the damage level more precisely, if needed. Therefore, this method contributes to the application of real-time structural health monitoring for offshore structures by providing an approach to improve the usability of the proposed technique.

A review on sensors and systems in structural health monitoring: current issues and challenges

Sensors and systems in Civionics technology play an important role for continuously facilitating real-time structure monitoring systems by detecting and locating damage to or degradation of structures. An advanced materials, design processes, long-term sensing ability of sensors, electromagnetic interference, sensor placement techniques, data acquisition and computation, temperature, harsh environments, and energy consumption are important issues related to sensors for structural health monitoring (SHM). This paper provides a comprehensive survey of various sensor technologies, sensor classes and sensor networks in Civionics research for existing SHM systems. The detailed classification of sensor categories, applications, networking features, ranges, sizes and energy consumptions are investigated, summarized, and tabulated along with corresponding key references. The current challenges facing typical sensors in Civionics research are illustrated with a brief discussion on the progress of SHM in future applications. The purpose of this review is to discuss all the types of sensors and systems used in SHM research to provide a sufficient background on the challenges and problems in optimizing design techniques and understanding infrastructure performance, behavior and current condition. It is observed that the most important factors determining the quality of sensors and systems and their reliability are the long-term sensing ability, data rate, types of processors, size, power consumption, operation frequency, etc. This review will hopefully lead to increased efforts toward the development of low-powered, highly efficient, high data rate, reliable sensors and systems for SHM.

Noise isolation with phononic crystals to enhance fatigue crack growth detection using acoustic emission

The background noise imposes difficulties in monitoring crack activities in real-time monitoring of structures using the acoustic emission (AE) method. Here, a two-dimensional phononic crystal (PC) is designed to enhance fatigue crack growth detection capability of acoustic emission technique in plate-like structures, such that a single AE sensor is sufficient to monitor the crack activity. PCs, also known as acoustic metamaterials, are artificial composite, man-made periodic systems creating new responses through physical constraints in the constituent materials such that certain frequencies are prohibited by local the resonance of the periodic system. The integration of PC design with AE method can be utilized to block unwanted noise signals and enhance the crack detection ability of the AE method. The proposed PC structure to block certain frequencies is numerically studied using COMSOL Multiphysics software through applying two approaches: the unit cell analysis and the transmission loss. It is concluded that the primary reason of the band gap formation through the designed PC structure is the local resonance phenomenon. The final geometry is selected to provide a band gap near 150 kHz, which is a common frequency to monitor crack growth in metallic structures. The stubs forming the PC structure are placed on the compact specimen periodically, except along the crack growth path and sensor position, to block the friction emissions coming from the grip locations. The performance of the designed PC structure is demonstrated experimentally through the fatigue testing of aluminum standard compact tension specimens. The cumulative numbers of hits and AE energy obtained from the specimen integrated with the PC structure demonstrate the successful implementation of noise blocking in the AE testing of detecting active cracks.

Real-time structural monitoring of Building 350 at Del Valle University

Design errors, misuse, and natural or accidental events can cause small and cumulative damages or large and sudden damages that can deteriorate structural systems or cause their collapse. Structural monitoring consists in the permanent real-time tracking of information about the condition and performance of a structure. This paper presents the development, validation, and implementation of the structural monitoring system for building 350 at Del Valle University, located in Santiago de Cali, Colombia. The system uses the SSI and natural excitation technique with the eigensystem realization algorithm methodologies for modal identification and a specialized event-recognition algorithm based on the standard deviation analysis of vibrations. The dynamic behavior monitoring of the building has been conducted in real-time since January 2012, and six seismic events have been successfully recognized. The computational tool was validated using a finite element model with which the effect of variation in mass was evaluated.

Integrated non-destructive assessment of concrete structures under flexure by acoustic emission and travel time tomography

Elastic stress wave methods are attracting increasing attention from researchers and practitioners for real time monitoring of structures as well as quantitative evaluation of damage. The acoustic emission (AE) and travel time tomography (TTT) techniques are promising elastic waves-based diagnosis approaches for condition assessment of structures. In this research, fiber reinforced concrete beams loaded under flexure were monitored using both the AE and TTT techniques. The primary aim was to evaluate the development of fracture in beams in a non-destructive manner, with the two adopted techniques complementing each other to provide a more reliable assessment. There was satisfactory agreement between the results of AE parametric analysis and tomographic reconstruction, suggesting the feasibility of integrating the two techniques for a more comprehensive and effective condition assessment of structures.

An Instantaneous Integer Ambiguity Resolution for GPS Real-Time Structure Monitoring

GPS 반송파를 사용하여 센티미터 수준 정확도의 동적 측위를 위해서는 반송파 관측데이터에 포함하고 있는 미지정수를 결정하여 정밀한 기하거리로 환산하는 것이 필수적이다. 본 논문에서는 GPS에 의한 실시간 구조물 모니터링에 효율적으로 적용가능한 반송파 관측데이터 순간미지정수 결정 성능향상을 위한 알고리즘을 연구하였다. 이를 위하여 구조물에 설치한 GPS 수신기 이동 범위와 그 네트워크를 수학적으로 모형화하고 '정수제약 최소제곱법'을 통해 정확한 위치를 추정하는 절차를 제안하였다. 이 절차에는 추정해의 신뢰성 향상을 위해 실수해의 과대오차 최소화에 필요한 품질제어와 기하적 구속조건을 이용한 미지정수 타당성 검정을 포함하고 있다. 제안된 순간미지정수 결정절차를 과학기술용 계산용 소프트웨어인 MATLAB에 의해 실시간 적용 가능하도록 구현하고 장대교량에 해당하는 사장교 현장 관측데이터를 처리하여, 그 성능을 미지정수결정 성공률, 통계모형의 영향 그리고 연산시간에 대해 분석하고 결과를 요약하였다. In order to deliver a centimeter-level kinematic positioning solution with GPS carrier-phase measurements, it is prerequisite to use correctly resolved integer ambiguities. Based on the mathematical modeling of GPS network with application of its geometrical constraints, this research has investigated an instantaneous ambiguity resolution procedure for the so-called 'integer constrained least-squares' technique which can be effectively implemented in real-time structure monitoring. In this process, algorithms of quality control for the float solutions and hypothesis tests using the constrained baseline for the ambiguity validation are included to enhance reliability of the solutions. The proposed procedure has been implemented by MATLAB, the language of technical computing, and processed field trial data obtained at a cable-stayed bridge to access its real-world applicability. The results are summarized in terms of ambiguity successful rates, impact of the stochastical models, and computation time to demonstrate performance of the instantaneous ambiguity resolution proposed.

Development of an artificial neural network processing technique for the analysis of damage evolution in pultruded composites with acoustic emission

Acoustic Emission (AE) is a promising technique for the damage detection and the real-time structural monitoring of composite lightweight structures; however data interpretation and discrimination among failure modes from AE data is difficult to be carried out without proper data processing techniques. In this paper, a neural-network based classification of AE signals from tensile tests of pultruded glass-fiber specimens is proposed. A self-organizing map is trained with AE data from one specimen; then the map is clustered with the k-means algorithm. The optimal number of clusters is chosen by a voting procedure that takes into account a number of quality indexes; then the clustered neural network is used to classify AE data from other specimen. Results have shown that the classifier built from a smooth specimen was able to correctly classify other specimens with the same and with a different material layup, and is capable of recognizing signals from notched specimens, thus providing interesting and encouraging indications in view of the application on real structures.

효과적인 잡음 제거와 가속도 계측을 통한 구조적 변위 추정 연구

In this study, the estimation method for the structural displacements by the acceleration measurement is investigated for effective real-time structural monitoring. Theoretically, the velocity and displacement data during the specific period can be obtained by integration of the measured acceleration. But, the integrated data is highly affected by unexpected error sources such as noise signal. So, the effective and rational method for the noise elimination or minimization should be suggested and used. In this study, an effective noise filtering method for an useful displacement estimation by double integration is suggested based on the characteristics of structural dynamics. For the rational noise filtering technique applied to the measured acceleration data, the multi bandpass filter, which allows the signals nearby the peak frequencies of measured data, is newly suggested. By the suggested filtering and double integration method, the estimation method of structural displacements using an acceleration measurement is suggested in this study. The accuracy and rationality of the suggested method was validated using the numerical example obtained by finite element analysis. In the validation, the displacement data estimated by the suggested method was directly compared with those obtained by highpass filter and bandpass filter, formerly well known methods.