When applying reliability analysis to the monitoring of structural health, it is very important that gross errors–which affect prediction accuracy–are included within the monitoring information. An approach using gross errors identification and a dam safety monitoring model for deformation monitoring data of concrete dams is proposed in this paper. It can solve the problems of strong nonlinearity and the difficulty of identifying and eliminating gross errors in deformation monitoring data in concrete dams. This new method combines the advantages of an incremental extreme learning machine (I-ELM) method to seek an optimal network structure, the Least Median Squares (LMS) method with strong robustness to multiple failure points, the robust estimation IGG method with the good robustness to outliers (gross errors) and extreme learning machine (ELM) method with high prediction efficiency and handling of nonlinear problems. The proposed method can eliminate gross errors and be utilized to predict the behavior of concrete dams. The deformation monitoring data of an existing 305 m-high concrete arch dam is acquired by combining remote sensing technology with other monitoring methods. The LMS-IGG-ELM method is utilized to eliminate outliers from the dam monitoring sequence and is compared with the processing result from a DBSCAN clustering algorithm, Romanovsky criterion and the 3σ method. The results show that the proposed method has the highest gross errors identification rate, the strongest generalization ability and the best prediction effect.

For the structural health diagnostic of concrete dams, the mathematical monitoring model based on the measured deformation values is of great significance. The main purpose of this paper is to reconstruct the ageing component and the temperature component in the traditional Hydraulic-Seasonal-Time (HST) hybrid model by combining the measured values. On the one hand, a better mathematical model for the ageing displacement of concrete dams is proposed combined with the Burgers model to separate the instantaneous elastic hydraulic deformation and the hysteretic hydraulic deformation, and then it subsumes the latter into the ageing deformation to describe its reversible component. According to the Burgers model, the inverted elastic modulus of the Jinping-Ⅰ concrete dam is 46.5 GPa, which is closer to the true value compared with the HST model. On the other hand, the kernel principal component analysis (KPCA) method is used to extract the principal components of the dam thermometers for replacing the period harmonic thermal factor. A multiple linear regression (MLR) model is established to fit the measured displacement of the concrete arch dam and to verify the accuracy of the proposed hybrid model. The results show that the proposed model reaches higher accuracy than the traditional HST hybrid model and is helpful to improve the interpretation of the separated displacement components of the concrete dams.

In most dam deformation monitoring practices, some single-point models do not consider the spatial correlation, and the traditional regression models do not consider the nonlinear relationship between the environmental quantity and the deformation quantity, resulting in poor prediction accuracy. In view of the poor accuracy of the monitoring data, which reflect the overall deformation response in the current dam monitoring practices, this paper proposes an innovative solution of ensemble empirical mode decomposition and a wavelet noise reduction method. A high-precision prediction model considering spatial correlation is constructed. By studying the measured deformation data of an arch dam and comparing the performance parameters of various models, the superiority and universality of the proposed method are verified. Dam deformation monitoring data are of great significance to describe the operation behavior of dams. It is significant for us to optimize the health monitoring of dam safety structures and ensure dam safety and realize social harmony in our country.

Warning indicators are required for the real-time monitoring of the service conditions of dams to ensure safe and normal operations. Warnings are traditionally targeted at some “single point deformation” by deformation measuring points of concrete dam, and scientific warning theory on “overall deformation” measured is nonexistent. Furthermore, the influences of random factors are not considered. In this paper, the overall deformation of the dam was seen as a deformation system of single interactional observation points with different contribution degrees. The spatial deformation entropy, which describes the overall deformation, was established and the fuzziness indicator that measures the influence of complex random factors on monitoring values according to cloud theory was constructed. On this basis, multistage warning indicators of “spatial deformation” that consider fuzziness and randomness were determined. Analysis showed that the change law of information entropy of the dam’ overall deformation is identical to the real change law of the dam; thus, it reflects the real deformation state of the dam. Moreover, the identified warning indicators improved the warning ability of concrete dams.

It is very important for embankment engineering to consider the seepage factor. If the potential seepage is not discovered in time and seepage control measures are not appropriate, seepage is very likely to cause damage and deformation, resulting in embankment failure. Based on temperature and seepage fields theories, a temperature–seepage coupled model is established in this paper. It is combined with a distributed temperature sensing (DTS) system to measure the temperature field of the porous media. This approach allows for the inversion of the inner seepage field, realizing the real-time monitoring of embankment health to ensure its safety and long-term operation. According to the coupling analysis on the temperature–seepage fields, for practical engineering, the influence of temperature on the seepage field is small and neglectable. Only the effect of the seepage field on the temperature field is considered. The DTS optical fiber temperature measurement system is widely used in various projects nowadays because of its high stability and efficiency advantages. The optical fiber is sensitive to temperature and can give fast and accurate temperature feedback regarding seepage location. Combined with the Heat Transfer Module in COMSOL, the multi-line heat source method can be used to invert the seepage field according to the temperature field of the porous medium inside the embankment and derive the seepage flow rate of the stable seepage field. For unstable seepage, optical fiber is good at seepage measuring and location detecting. For different practical engineering, a different heating power can be used for different seepage conditions. By monitoring the temperature change, the seepage condition can be inverted which is one of the indicators for evaluating engineering safety.