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Seepage failure and slope instability in dike engineering are the most common dangerous cases which can easily lead to the break accident. The two dangerous cases have remarkable features such as complex causes, many influencing factors and strong uncertainty. The advantages in the safety factor method and the reliability analysis method are integrated. A dual criterion, which combines the certain index (safety factor) with the uncertain index (reliability), is proposed to evaluate the seepage and stability safety of dike engineering. The dynamic determination approach for the safety status of dike engineering and the corresponding dangerous water level threshold is developed. A representative section of one actual dike engineering is taken as an example. The seepage and stability safety of dike section is analyzed. The more comprehensive and objective evaluation results on service safety status and development trend are obtained. The determined threshold of dangerous water level, which can ensure the service safety of dike engineering, is very helpful to guide the scientific management of dike engineering.

The methods on fractal, phase space reconstruction and wavelet are integrated to analyze the observed data of concrete dam cracks. The diagnosis model and appropriate criterion for evolution characteristics of concrete dam crack are developed. Firstly, a wavelet threshold value de-noising algorithm is introduced to process the crack observations of concrete dam. The phase space reconstruction for data series after noise reduction is investigated and the parameter selection method is presented. Then the correlation dimension and Kolmogorov entropy-based models are built to evaluate the crack status of concrete dam. The corresponding criteria are given. Finally, the identification capability of the proposed approach is demonstrated with an actual case. It is indicated that the noise reduction of observed data has the important effect on improving the diagnosis accuracy of crack status. The observed behavior of dam crack has obvious fractal characteristics. The effective diagnosis for crack status of concrete dam can be implemented using the correlation dimension and Kolmogorov entropy-based criteria.

Due to low tensile strength of concrete, seasonal temperature, shrinkage of concrete, and so on, many of hydraulic concrete structures experience cracking. An optical fiber–based approach is introduced to implement the crack detection in hydraulic concrete structures. The experimental and theoretical investigations on bending loss and time-domain reflection behaviors of optical fiber are performed. According to the bending loss mechanism of optical fiber, the monitoring method of hydraulic concrete crack is presented. First, the effect of optical fiber bending radius on optical loss is analyzed. The mathematical model between bending radius and optical loss is established. Then, the identification principle of concrete crack using bending loss data of optical fiber is studied. Considering the crack characteristics of hydraulic concrete structure, the arrangement forms of optical fiber, which are used to monitor the tension crack and hybrid crack in hydraulic concrete structure, are proposed. Finally, the relationship between crack development and optical loss is investigated using laboratory experiments with optical fiber.

Support vector machine, chaos theory, and particle swarm optimization are combined to build the prediction model of dam safety. The approaches are proposed to optimize the input and parameter of prediction model. First, the phase space reconstruction of prototype monitoring data series on dam behavior is implemented. The method identifying chaotic characteristics in monitoring data series is presented. Second, support vector machine is adopted to build the prediction model of dam safety. The characteristic vector of historical monitoring data, which is taken as support vector machine input, is extracted by phase space reconstruction. The chaotic particle swarm optimization algorithm is introduced to determine support vector machine parameters. A chaotic support vector machine–based prediction model of dam safety is built. Finally, the displacement behavior of one actual dam is taken as an example. The prediction capability on the built prediction model of dam displacement is evaluated. It is indicated that the proposed chaotic support vector machine–based model can provide more accurate forecasted results and is more suitable to be used to identify efficiently the dam behavior.

There are usually a small amount and short sequence of prototype monitoring data on structural behavior during dangerous dam reinforcement. According to above data characteristics, some methods, such as support vector machine, particle swarm optimization, genetic algorithm, rough set, are combined to build the real-time monitoring model of safety status during structural reinforcement of dangerous dam. Firstly, the construction principle on standard support vector machine-based monitoring model of safety status is demonstrated. To improve the modeling accuracy and efficiency, particle swarm optimization and genetic algorithm are introduced to implement the support vector machine parameters optimization. Secondly, the ability on data mining in rough set is developed to determine the input vector of support vector machine. An approach is presented to obtain the simplified non-linear mapping relationship between dam behavior (effect-quantity) and its cause (influence-quantity). An index and its calculating formula are proposed to measure the influence-quantity importance. A real-time monitoring model based on rough set-support vector machine is established to describe reasonably the working mechanism during dangerous dam reinforcement. Lastly, the fitting and prediction capability of above monitoring model is demonstrated with an actual case.

The concrete strength has the strong influence on service safety of mass hydraulic concrete structures. The real-time identification of concrete strength is very important to ensure the pouring quality during the construction and find timely the hidden troubles in service. Based on the analysis for the mapping relationship between concrete strength and stress wave amplitude, the method is proposed to obtain indirectly the concrete strength with self-made concrete piezoelectric ceramic smart module pairs. First, according to the service condition of hydraulic concrete structure, the concrete piezoelectric ceramic smart module is designed and the process manufacturing concrete piezoelectric ceramic smart module is presented. The protecting measures of piezoelectric ceramic piece, the peripheral material constituent, and the pouring links are introduced. Second, an integrated system, which is composed of self-made concrete piezoelectric ceramic smart modules in pair as sensor and actuator, fuzzy inference system, and other auxiliary equipments, is developed to monitor indirectly concrete strength. Its working principle is studied. Finally, an experiment of hydraulic concrete strength is implemented to demonstrate the feasibility and validity of the proposed method and developed system. The concrete piezoelectric ceramic smart modules in pair are taken as sensor and actuator embedded in hydraulic concrete specimens. The wave-based analysis method is adopted to collect the stress wave amplitude data during the pouring process of concrete specimens. The fuzzy inference approach is utilized to build the mathematical model describing the relationship between concrete strength and stress wave amplitude. Based on the above mathematical model, the concrete strength can be identified indirectly.