基于压电智能骨料的混凝土渗水监测方法研究
发布时间：2018-11-11 01:28
【摘要】：近年来随着智能材料以及智能传感器的迅速发展，，结构健康监测技术得到了进一步的拓展。其中，压电陶瓷材料因为具有正逆压电效应、频响高、成本低、可重复性好、适合实时监测等优点，使得其在结构健康监测领域广泛应用。目前埋入式压电智能骨料（Smart Aggregate简称SA）传感器主要研究和应用于混凝土结构损伤裂缝、早期水化、钢管与混凝土脱粘和桩基等领域的监测，而在混凝土结构渗水监测方面鲜有报道，本文将搭建压电渗水监测系统，围绕传感器在水中传输特性、温度效应、波速表征及数值实现等方面展开相关研究，具体包括以下几方面的工作： 将压电智能骨料浸泡于自来水中，以浸泡时间描述水侵入智能骨料内部的过程，试验研究了压电智能骨料的工作性能（信号幅值）随浸泡时间的变化，并考虑了激励信号频率的影响。研究结果表明：水对压电智能骨料工作性能的影响是非常明显的，在浸泡的前几个小时信号幅值迅速增大后趋于稳定。 通过对处于温度变化环境中的混凝土试件进行主动监测，并将监测信号幅值作为评价指标，对基于压电智能骨料的混凝土结构健康监测系统功能单元的温度敏感性进行了试验研究。研究结果表明：P波幅值具有较强的温度敏感性，随温度降低监测信号幅值也随之降低，反之亦然。并且，随激励信号频率的增加，P波幅值受温度影响减小。 基于混凝土渗水深度监测系统，采用不同频率激励信号，分别对处于同一渗水深度及不同渗水深度的两根素混凝土短柱进行主动监测，研究了监测信号幅值及波速信息随混凝土渗水深度的变化规律。对比讨论了峰值点法、互相关法以及互功率谱法对P波总传播时间的影响，并基于等效P波波速定性评价了混凝土的渗水情况。研究结果表明：同一渗水深度下，监测信号幅值随浸水时间呈先迅速下降后趋于稳定；不同渗水深度下，监测信号幅值随渗水深度的增加，呈降低趋势，当试件渗水饱和时幅值会略有提高。另外，随渗水深度增加等效P波波速有非线性增大趋势，因此基于等效P波波速定性评价混凝土渗水状况是可行的。最后，研究表明利用互相关法确定总传播时间具有很大的优越性。 利用有限元软件Ansys建立了埋有压电智能骨料的素混凝土短柱有限元模型，对混凝土的渗水监测进行了数值仿真模拟，通过P波幅值及传播速度等信号指标变化情况，探讨了引起试验结果和仿真结果差异性的因素。研究表明：仿真结果在波形幅值及P波波速变化趋势方面与试验结果吻合良好，引起两者数值上差异的主要因素是数值仿真忽略了材料不均匀性及孔隙结构的影响，进而表明混凝土材料孔隙结构对P波幅值及P波波速影响明显。
[Abstract]:In recent years, with the rapid development of smart materials and smart sensors, structural health monitoring technology has been further expanded. Among them, piezoelectric ceramic materials are widely used in the field of structural health monitoring because of their advantages of positive and inverse piezoelectric effect, high frequency response, low cost, good repeatability and suitable for real-time monitoring. At present, the embedded piezoelectric intelligent aggregate (Smart Aggregate (SA) sensor is mainly used to monitor the damage and crack of concrete structure, early hydration, debonding of steel pipe and concrete and pile foundation, etc. However, there are few reports on water seepage monitoring of concrete structure. In this paper, the piezoelectric water seepage monitoring system will be set up to study the characteristics of sensor transmission in water, temperature effect, wave velocity characterization and numerical realization, etc. It includes the following work: the piezoelectric intelligent aggregate is immersed in tap water to describe the process of water invading the intelligent aggregate. The variation of working performance (signal amplitude) of piezoelectric intelligent aggregate with immersion time was studied, and the effect of excitation signal frequency was considered. The results show that the effect of water on the working performance of piezoelectric intelligent aggregate is very obvious, and the amplitude of the signal increases rapidly several hours before soaking and tends to be stable. Through the active monitoring of the concrete specimen in the environment of temperature change, the amplitude of the monitoring signal is taken as the evaluation index. The temperature sensitivity of functional unit of concrete structure health monitoring system based on piezoelectric intelligent aggregate is studied experimentally. The results show that the amplitude of P wave is sensitive to temperature, and the amplitude of monitoring signal decreases with the decrease of temperature, and vice versa. Moreover, with the increase of the frequency of excitation signal, the amplitude of P wave decreases with the influence of temperature. Based on the concrete seepage depth monitoring system, two short plain concrete columns with the same seepage depth and different seepage depth were actively monitored by different frequency excitation signals. The variation of monitoring signal amplitude and wave velocity with the depth of concrete seepage is studied. The effects of peak point method, cross-correlation method and cross-power spectrum method on the total propagation time of P-wave are discussed, and the seepage of concrete is evaluated qualitatively based on the equivalent P-wave velocity. The results show that the amplitude of the monitoring signal decreases rapidly with the immersion time and then tends to be stable at the same seepage depth. The amplitude of the monitoring signal decreases with the increase of the seepage depth, and increases slightly when the sample is saturated. In addition, there is a nonlinear increasing trend of the equivalent P wave velocity with the increase of the seepage depth, so it is feasible to evaluate the concrete seepage condition qualitatively based on the equivalent P wave velocity. Finally, it is shown that the cross-correlation method has great superiority in determining the total propagation time. The finite element model of plain concrete short column embedded with piezoelectric intelligent aggregate is established by using finite element software Ansys. The seepage monitoring of concrete is simulated numerically. The change of signal indexes such as P wave amplitude and propagation velocity is obtained. The factors that cause the difference between the test results and the simulation results are discussed. The results show that the simulation results are in good agreement with the experimental results in terms of waveform amplitude and P wave velocity variation trend. The main factor causing the difference is that the numerical simulation neglects the influence of material inhomogeneity and pore structure. The results show that the pore structure of concrete material has an obvious effect on the amplitude of P wave and the velocity of P wave.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TU317;TU37
本文编号：2324114
[Abstract]:In recent years, with the rapid development of smart materials and smart sensors, structural health monitoring technology has been further expanded. Among them, piezoelectric ceramic materials are widely used in the field of structural health monitoring because of their advantages of positive and inverse piezoelectric effect, high frequency response, low cost, good repeatability and suitable for real-time monitoring. At present, the embedded piezoelectric intelligent aggregate (Smart Aggregate (SA) sensor is mainly used to monitor the damage and crack of concrete structure, early hydration, debonding of steel pipe and concrete and pile foundation, etc. However, there are few reports on water seepage monitoring of concrete structure. In this paper, the piezoelectric water seepage monitoring system will be set up to study the characteristics of sensor transmission in water, temperature effect, wave velocity characterization and numerical realization, etc. It includes the following work: the piezoelectric intelligent aggregate is immersed in tap water to describe the process of water invading the intelligent aggregate. The variation of working performance (signal amplitude) of piezoelectric intelligent aggregate with immersion time was studied, and the effect of excitation signal frequency was considered. The results show that the effect of water on the working performance of piezoelectric intelligent aggregate is very obvious, and the amplitude of the signal increases rapidly several hours before soaking and tends to be stable. Through the active monitoring of the concrete specimen in the environment of temperature change, the amplitude of the monitoring signal is taken as the evaluation index. The temperature sensitivity of functional unit of concrete structure health monitoring system based on piezoelectric intelligent aggregate is studied experimentally. The results show that the amplitude of P wave is sensitive to temperature, and the amplitude of monitoring signal decreases with the decrease of temperature, and vice versa. Moreover, with the increase of the frequency of excitation signal, the amplitude of P wave decreases with the influence of temperature. Based on the concrete seepage depth monitoring system, two short plain concrete columns with the same seepage depth and different seepage depth were actively monitored by different frequency excitation signals. The variation of monitoring signal amplitude and wave velocity with the depth of concrete seepage is studied. The effects of peak point method, cross-correlation method and cross-power spectrum method on the total propagation time of P-wave are discussed, and the seepage of concrete is evaluated qualitatively based on the equivalent P-wave velocity. The results show that the amplitude of the monitoring signal decreases rapidly with the immersion time and then tends to be stable at the same seepage depth. The amplitude of the monitoring signal decreases with the increase of the seepage depth, and increases slightly when the sample is saturated. In addition, there is a nonlinear increasing trend of the equivalent P wave velocity with the increase of the seepage depth, so it is feasible to evaluate the concrete seepage condition qualitatively based on the equivalent P wave velocity. Finally, it is shown that the cross-correlation method has great superiority in determining the total propagation time. The finite element model of plain concrete short column embedded with piezoelectric intelligent aggregate is established by using finite element software Ansys. The seepage monitoring of concrete is simulated numerically. The change of signal indexes such as P wave amplitude and propagation velocity is obtained. The factors that cause the difference between the test results and the simulation results are discussed. The results show that the simulation results are in good agreement with the experimental results in terms of waveform amplitude and P wave velocity variation trend. The main factor causing the difference is that the numerical simulation neglects the influence of material inhomogeneity and pore structure. The results show that the pore structure of concrete material has an obvious effect on the amplitude of P wave and the velocity of P wave.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TU317;TU37
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1 张富尧;基于压电智能骨料的混凝土渗水监测方法研究[D];哈尔滨工业大学;2014年
本文编号：2324114
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