Material defects localization in concrete plate-like structures – Experimental and numerical study

Hybrid approach for two dimensional damage localization using piezoelectric smart aggregates

Ultrasound measurements are used for monitoring the structural health of concrete. The elastic waves propagating in concrete structures are attenuated by scattering and intrinsic attenuation. Both quantities have to be determined for a detailed material characterization and for realistic numerical simulations of wave propagation in such structures. We present in this paper a numerical approach to determine the intrinsic attenuation of concrete. For this purpose, a realistic digital three-dimensional concrete specimen is created that accounts for correct volume fractions of aggregates of different sizes embedded into a mortar matrix. Using a viscoelastic finite difference scheme, we perform simulations of the ultrasound wave field in these digital models with different attenuation levels. The numerical observations are compared with results from ultrasonic measurements on a sample of saturated concrete to estimate the intrinsic attenuation. Embedded sensors are used for those long-term laboratory experiments. For this saturated concrete, we obtain Q=130 in a frequency band around 60 kHz.

s The evacuation process in buildings provides an efficient method for engineers to evaluate the rationality of the architectural design and structural design scheme. The simulation of the authentic evacuation process is conducive to accurately evaluate the casualties of buildings under earthquake. The economic loss estimation in the performance-based seismic design method can then be implemented. This study improves the traditional cellular automata model in order to simulate the crowd evacuation process in buildings under earthquake. The modified model simulates the attraction of exits to crowds, herd behavior of crowds, avoidance behavior for obstacles, decision-making behavior for paths/exits selection, and conflict between pedestrians in the evacuation process. Based on the variation coefficient of evacuation time and video which records authentic evacuation under earthquake, the influence coefficients of each factors are determined. In addition, the modified cellular automata model uses the refined cellular space to describe the geometric dimensions of the evacuation environments and obstacles, and therefore it improves the accuracy of the evacuation model. The explicit finite element method is used to simulate the seismic damage process of structural and non-structural components. The judgment criterion of casualties which combines the finite element model with the evacuation model, is proposed. The number and distribution of casualties are predicted based on the criteria. The prediction method for casualties helps the architects to optimize the size and layout of the building components. In the structural design stage, identifying areas with high population density or high casualties is conducive to optimize the structural design scheme, and provide a basis for the formulation of a post-earthquake rescue plan. Finally, a seven-story official building with reinforced concrete frame structure located in Dujiangyan City, Sichuan Province, China is considered as example to verify the rationality and applicability of the proposed method.

Though the impact echo method is commonly used for thickness measurement of plate concrete structures, the method to detect the internal defects in concrete structures is still not clearly established. Basically the impact echo technology is used to obtain the resonant frequency of flutter echo between the measuring surface and the opposite surface of a concrete plate. The thickness can be calculated by dividing the elastic wave speed of the concrete by twice the measured dominant frequency. By this method, if the thickness of the plate is known, the wave speed of the flutter echo can be calculated. We found out that the travel path condition of the wave affect the wave speed. The wave speed is almost always reduced if there is defected area on the path. In case the cross section of defect is circular, higher harmonic frequencies are generated near the direct top surface of the defected areas. From these observations, distribution of the flutter echo's wave speed on the measuring plane shows the location of the projection area of the defect. The results of the experiments using concrete plate with voids show that the location of the voids are indicated by the area where the wave speeds of the flutter echo are slower than the areas with no voids. Also the method and the results for location detection and degree of filling of the actual PC bridge sheaths are shown in this paper.

Monitoring ambient air temperature is one of the important operations to ensure resilience and efficiency in large-scale data centers. However, deployment of a data center monitoring system requires recording the location of thousands of sensors which is a labor-intensive task if is done manually. Since Radio-Frequency (RF) based localization solutions in literature are inadequate in the multipath rich environment of data centers, we investigated the possibility of utilizing the measurements of the sensors in localizing themselves. The idea of thermal piloting is to correlate sensor measurements with the expected temperature values at their locations. It can be treated as a classification problem, in which the feature vector is formed by the temperature values at each sensor location across different cooling configurations. The training set is provided by Computational Fluid Dynamic (CFD) simulations. Since classical supervised learning techniques fail to account for the bijective relation between sensor indices and locations, we formulated an extra step based on the Maximum Weighted Bi-partite Matching (MWBM) problem. Experimental results show that the proposed methods can achieve an average localization error of 0.64 meters.

In order to more accurately grasp the exact shape, contour size and distribution of defects in the plate-like structures material and other detailed feature information, this paper designed the graphical user interface GUIGWT0.1, which is specifically used for ultrasonic guided wave imaging. This software integrates filtering back projection algorithm, damage probability reconstruction algorithm and diffraction tomography algorithm, which can image the plate-like structures structure as required, and develop the defect detection results to the visual direction. To investigate the validity of the interface, the projection data of lamb wave in the plate-like with a partial-thickness notch are numerically simulated by three-dimensional elastodynamic finite integration technique (EFIT). The resulting projected data is imported into the graphical user interface for imaging. For each existing reconstruction imaging, the result can be represented in terms of grayscale image of projected data, defect imaging, image color inversion and false color defect recognition. Through simple operation, reconstruction of image with defect of plate-like structure are obtained in the user interface.

A tailor-made cylindrical concrete implantable module for damage imaging in plate-like concrete structures

Accurate localization of nodes in a Wireless Sensor Network (WSN) is imperative for several important applications. The use of Global Positioning Systems (GPS) for localization is the natural approach in most domains. In WSN, however, the use of GPS is challenging because of the constrained nature of deployed nodes as well as the often inaccessible sites of WSN nodes’ deployment. Several approaches for localization without the use of GPS and harnessing the capabilities of Received Signal Strength Indicator (RSSI) exist in literature but each of these makes the simplifying assumption that all the WSN nodes are within the communication range of every other node. In this paper, we go beyond this assumption and propose a hybrid technique for node localization in large WSN deployments. The hybrid technique comprises a loose combination of a Machine Learning (ML) based approach for localization involving random forest and a multilateration approach. This hybrid approach takes advantage of the accuracy of ML localization and the iterative capabilities of multilateration. We demonstrate the efficacy of the proposed approach through experiments on a simulated set-up.KeywordsLocalizationMultilaterationRandom forestRSSIWSN

Monitoring of structures implies integration of sensors and actuators, smart materials, data transfer as well as computer analyses and simulations with the purpose of damage detection, localization, assessment and prediction of the state of damage at the certain moment and in time. This paper presents the application of the explicit finite element method for modeling of the wave propagation. The examples of concrete plates and thin steel plates in which the propagation of the Lamb waves occur were analyzed. Explicit finite element method was shown to be very efficient even for the waves in ultrasound range. Efficiency, ease of the use and reliability of the wave propagation modeling by the explicit finite element method can contribute to the development of a new and the improvement of the existing methods for the monitoring of structures. The main purpose of this paper is to demonstrate a waveform propagation model using an explicit FEM in ABAQUS software.

In this paper, the design of a 3-axis multifunctional imaging system with microwave and laser sensing techniques is described, and the results of its application to non-contact testing of construction materials are presented. This system can provide automated control of the movement of sensing units including a microwave antenna and a laser displacement sensor for contour following, optimization of standoff distance, and two imaging capabilities. First, the system is capable of generating images of the specimen under investigation using microwave data and displacement data, separately. In this case, the displacement data is used to generate 2-D images and a 1-D profile of the specimen surface while microwave images may provide information on interior structure of the specimen. Second, a combination of microwave and laser displacement sensor techniques can generate microwave images of the specimen by providing an optimal standoff distance and removing automatically, at hardware level, the change of the standoff distance. The applicability of the system to non-contact detection of metal and dielectric targets embedded in layered structures and flaws such as cracks on the surface of tilted and cylindrical specimens has been demonstrated.

The main principle of vibration-based damage detection in structures is to interpret the changes in dynamic properties of the structure as indicators of damage. In this study, the mode shape damage index (MSDI) method was used to identify discrete damages in plate-like structures. This damage index is based on the difference between modified modal displacements in the undamaged and damaged state of the structure. In order to assess the advantages and limitations of the proposed algorithm, we performed experimental modal analysis on a reinforced concrete (RC) plate under 10 different damage cases. The MSDI values were calculated through considering single and/or multiple damage locations, different levels of damage, and boundary conditions. The experimental results confirmed that the MSDI method can be used to detect the existence of damage, identify single and/or multiple damage locations, and estimate damage severity in the case of single discrete damage.

Bond-slip effect in novel steel box-concrete composite structures part Ⅰ: Short-term loading

Forecasting future capacities and estimating the remaining useful life, while incorporating uncertainty quantification, poses a crucial yet formidable challenge in the realm of battery health diagnosis and management. In this study, a data-driven model based on artificial neural networks (ANN) and signal decomposition techniques including Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), and Empirical Wavelet Transform (EWT) is presented to predict the capacity value of lithium-ion batteries. Signal decomposition was performed using the discharge voltage values for four different batteries. A total of 22 features were obtained. The features of the signal decomposition methods were evaluated separately as well as hybrid approaches. Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) performance metrics are used in the proposed method and the values obtained are 3.67×10-6, 0.001351 and 0.002311, respectively. According to the findings, the hybrid model proposed demonstrated positive results in terms of accuracy, adaptability, and robustness.

Purpose This research paper adopts the fundamental tenets of advanced technologies in industry 4.0 to monitor the structural health of concrete beam members using cost-effective non-destructive technologies. In so doing, the work illustrates how a coalescence of low-cost digital technologies can seamlessly integrate to solve practical construction problems. Design/methodology/approach A mixed philosophies epistemological design is adopted to implement the empirical quantitative analysis of “real-time” data collected via sensor-based technologies streamed through a Raspberry Pi and uploaded onto a cloud-based system. Data was analysed using a hybrid approach that combined both vibration-characteristic-based method and linear variable differential transducers (LVDT). Findings The research utilises a novel digital research approach for accurately detecting and recording the localisation of structural cracks in concrete beams. This non-destructive low-cost approach was shown to perform with a high degree of accuracy and precision, as verified by the LVDT measurements. This research is testament to the fact that as technological advancements progress at an exponential rate, the cost of implementation continues to reduce to produce higher-accuracy “mass-market” solutions for industry practitioners. Originality/value Accurate structural health monitoring of concrete structures necessitates expensive equipment, complex signal processing and skilled operator. The concrete industry is in dire need of a simple but reliable technique that can reduce the testing time, cost and complexity of maintenance of structures. This was the first experiment of its kind that seeks to develop an unconventional approach to solve the maintenance problem associated with concrete structures. This study merges industry 4.0 digital technologies with a novel low-cost and automated hybrid analysis for real-time structural health monitoring of concrete beams by fusing several multidisciplinary approaches into one integral technological configuration.

Thanks to key advances in wireless communication and electronics, sensors have emerged as an appealing technology for several interesting applications, such as civilian (health and environment monitoring), natural (disaster detection), military (battlefield surveillance), and agricultural (precision agriculture) applications, to name a few. When grouped together, these sensors form a network to measure and gather data of the surrounding environment with respect to a specific phenomenon. The sensors are battery-powered, tiny devices that possess all the characteristics of a traditional computer, including storage, processing, and communication capabilities. In addition, these sensors are capable of sensing the environment and collecting data regarding several parameters, such as temperature, light, sound, vibration, etc. Unfortunately, all the sensors' capabilities are limited due to their physical size. In particular, the sensors have limited battery power as usually they are equipped with AA/AAA batteries whose lifetime is short. Therefore, the main challenge in the design of this type of network is the sensors' battery power (or energy), which is a critical component for the operation of the whole network. Moreover, these sensors communicate (possibly) wirelessly with each other to collect sensed data and accomplish the goals of their missions. To this end, the sensors are required to know their locations and those of their neighbors. Therefore, sensor localization is a crucial aspect for the design and development of wireless sensor networks. Various algorithms and protocols have been developed for sensor localization in both two-dimensional and three- dimensional wireless sensor networks. However, the problem of sensor localization in a three-dimensional space has not been investigated in the literature as extensively as its counterpart in a two-dimensional space. In this book chapter, we propose to study the sensor localization problem in three-dimensional wireless sensor networks. More precisely, this book chapter's sole focus will be on three-dimensional sensor deployment, and it aims to provide an overview of the existing solutions to the localization problem in a three-dimensional space. Basically, it proposes a classification of localization algorithms, and discusses different three-dimensional sensor localization approaches along with their motivation and evaluation.