Characterization of concrete materials using non-destructive wave-propagation testing techniques

Non-destructive testing (NDT) of concrete members has been widely used for characterisation of material and assessment of functional structures without impairing their functions and performances. This thesis focuses on addressing critical challenges related to the practical implementation of NDT techniques based on wave-propagation approaches for characterisation of concrete members used in civil infrastructures. Specially, this research aims to achieve three interdependent objectives related to developing NDT techniques with piezoceramic-based transducers: monitoring of very early-age concrete hydration process, detection, and monitoring of cracking in concrete members of different complexity under loading. The concept of piezoceramic-based Smart Aggregate (SA) transducers is central to this research. Embedded SA transducers with an active sensing method have shown great potential for characterisation of construction materials such as concrete and concrete-steel composites. Based on the developed SA based approaches, an active sensing approach with appropriate arrangement of SAs in and on concrete members, and analysis of the received signal using the power spectral density, total received power and damage indexes is developed and applied in this thesis. To confirm its applicability for characterisation of very early-age concrete, a systematic investigation is performed into concrete specimens with different values of water-to-cement ratio due to slightly different initial water amounts, and different separation distances between the embedded SAs. For the detection and monitoring of cracking in concrete members under loading the mounted SA based approach is proposed and applied. It is shown that NDT systems, based on this approach, provide detection and monitoring of cracking in a variety of concrete members under loading, including relatively simple concrete beams and reinforced concrete beams under bending, and reinforced concrete slab as a part of a complex composite member under cyclic loading. Comparisons are provided between the proposed system and conventional load cell and strain gauge systems with each tested member

Assessment Of Nonlinear Static (Pushover) Procedures For Seismic Evaluation Of Reinforced Concrete Structures

In general, earthquake is one of the most serious natural disasters that mankind has ever suffered since the first day of civilization. Hence, the seismic performance of structures subjected to earthquake always becomes critical issues. This thesis presents the assessment of current nonlinear static procedures using nonlinear time history procedure. The selected pushover procedures in this research are consisting of Coefficient Method, Capacity Spectrum Method and Modal Pushover Method. Since plastic hinge length is an effective parameter in pushover analysis, this study discusses different plastic hinge lengths. These lengths are calculated for both default and user-defined cases. In this context, 2, 5, 8 and 12 storey frame were selected to represent the real low, medium and high rise regular reinforcement concrete structure. The results of the pushover analysis indicated that behaviour of the structures using modal pushover analysis method and coefficient method (under certain conditions) were more realistically than those analysed using capacity spectrum method. Moreover, the comparison of the results obtained from selected plastic hinge length reveals that, although the results of user-defined and default plastic hinge length in yielding state are almost similar, the results in ultimate state are significantly different. Therefore, it can be concluded that in this study proposed user-defined plastic hinge length shows better performance of hinge in analysis as compared to default plastic hinge length

Analysis of failure in concrete and reinforced-concrete beams for the smart aggregate–based monitoring system

Monitoring of structures and defining the severity of damages that occur under loading are essential in practical applications of civil infrastructure. In this article, we analyze failure using a smart aggregate sensor–based approach. The signals captured by smart aggregate sensors mounted on the structure under loading are de-noised using wavelet de-noising technique to prevent misdirection of the event interpretation of what is happening in the material. The performance of different mother wavelets on the de-noising process was investigated and analyzed. The objective is to identify the optimal mother wavelet for assessing and potentially reducing the effects of existing noise on signal properties for structural damage detection. In addition, we propose two innovative damage indices, entropy-based dispersion and entropy-based beta, for diagnostic purposes. The proposed entropy-based dispersion damage index is based on the modified wavelet packet tree and root mean square deviation, whereas the entropy-based beta damage index is based on the modified wavelet packet tree and slope of linear regression (beta). In both damage indices, the modified wavelet packet tree uses entropy as a high-level feature. Theoretical and experimental analyses are derived by computing indices on smart aggregate–based sensor data for concrete and reinforced-concrete beams. Validity assessment of the proposed indices was addressed through a comparative analysis with root mean square deviation damage index (benchmark) and the loading history. The proposed indices recognized the cracks faster than other measures and well before major cracking incurs in the structure. This article is expected to be beneficial for smart aggregate–based structural health monitoring applications particularly when damages occurred under loading

Analysis of failure in concrete and reinforced-concrete beams for the smart aggregate-based monitoring system

Monitoring of structures and defining the severity of damages that occur under loading are essential in practical applications of civil infrastructure. In this article, we analyze failure using a smart aggregate sensor–based approach. The signals captured by smart aggregate sensors mounted on the structure under loading are de-noised using wavelet de-noising technique to prevent misdirection of the event interpretation of what is happening in the material. The performance of different mother wavelets on the de-noising process was investigated and analyzed. The objective is to identify the optimal mother wavelet for assessing and potentially reducing the effects of existing noise on signal properties for structural damage detection. In addition, we propose two innovative damage indices, entropy-based dispersion and entropy-based beta, for diagnostic purposes. The proposed entropy-based dispersion damage index is based on the modified wavelet packet tree and root mean square deviation, whereas the entropy-based beta damage index is based on the modified wavelet packet tree and slope of linear regression (beta). In both damage indices, the modified wavelet packet tree uses entropy as a high-level feature. Theoretical and experimental analyses are derived by computing indices on smart aggregate–based sensor data for concrete and reinforced-concrete beams. Validity assessment of the proposed indices was addressed through a comparative analysis with root mean square deviation damage index (benchmark) and the loading history. The proposed indices recognized the cracks faster than other measures and well before major cracking incurs in the structure. This article is expected to be beneficial for smart aggregate–based structural health monitoring applications particularly when damages occurred under loading

Structural damage detection of a concrete based on the autoregressive all-pole model parameters and artificial intelligence techniques

Over the past few decades, damage identification in structural components has been the crucial concern in quality assessment and load capacity rating of infrastructure, as well as in the planning of a maintenance schedule. In this regard, structural health monitoring based on efficient tools to identify the damages in early stages has been focused by researchers to prevent sudden failure in structural components, ensure the public safety and reducing the asset management costs. Therefore, the development and application of sensing technologies and data analysis using machine learning approaches to enable the automatic detection of cracks have become very important. The purpose of this research is to develop a robust method for automatic condition assessment of real-life concrete structures for the detection of relatively small cracks at early stages. A damage identification approach is proposed using the parametric modeling and machine learning approaches to analyze the sensors data. The data obtained from transducers mounted on concrete beams under static loading in laboratory. These data are used as the input parameters. The method relies only on the measured time responses. After filtering and normalization of the data, Autoregressive all-pole model parameters (Yule-Walker method) are considered as features and used as the inputs of a newly developed Self-Advising Support Vector Machine (SA-SVM) for the classification purpose in civil Engineering area. Finally, the results are compared with traditional methods to investigate the feasibility of our proposed method. It is demonstrated that the presented method can reliably detect the crack in the structure and thereby enable the real-time infrastructure health monitoring. © 2017 Association for Computing Machinery

Characterization of cement concrete specimens during hydration process with piezoelectric-based smart aggregates

Characterization of cement concrete during hydration process has been performed using different methods. The determination of initial water-to-cement ratio is one of the challenging tasks since hydration process determines the microstructure of cement concrete and non-destructive methods with high accuracy are desired. This paper presents the results of the investigation into the stress wave transmission in cement concrete specimens with different initial water amount during hydration process. A non-destructive evaluation approach using embedded Smart Aggregates (SAs) at frequency range of 150 Hz to 150 kHz is used for this purpose. In this approach, one SA acts as an actuator when the swept sinusoidal wave is excited. The stress wave transmits through the specimen and it partially is received by the SA sensor. The time-domain receiving signals are recorded during hydration process and the corresponding power spectrum densities as transmission energy are computed and analysed, and then used for the material characterization. In addition, a destructive compression test is carried out on the 7th day and 28th day. The results show that changes of water amount in the standard-based specimens reduce their stress wave transmission performance and compressive strength value at 28th day. The feasibility of the SAs technique for the determination of water-to-cement ratio of cement concrete is also demonstrated

Detection and monitoring of flexural cracks in reinforced concrete beams using mounted smart aggregate transducers

Previous studies have successfully demonstrated the capability and reliability of the use of Smart Aggregate (SA) transducers to monitor reinforced concrete (RC) structures. However, they mainly focused on the applications of embedded SAs to new structural members, while no major attention was paid to the monitoring of existing RC members using externally mounted SAs. In this paper, a mounted SA-based approach is proposed for a real-time health monitoring of existing RC beams. The proposed approach is verified through monitoring of RC beams under flexural loading, on each of which SA transducers are mounted as an actuator and sensors. The experimental results show that the proposed SA-based approach effectively evaluates the cracking status of RC beams in terms of the peak of power spectral density and damage indexes obtained at multiple sensor locations. It is also shown that the proposed sensor system can also capture a precautionary signal for major cracking