Dam behavior prediction is a fundamental component of dam structural health monitoring. By comparing the predictions and the observations, anomalies can be detected, and then the remedial measures can be executed in time. As the most intuitive monitoring indicators, deformation is often used to evaluate dam structural health status. In this research, we propose a novel combined model for predicting the dam displacement time series. First, the seasonal-trend decomposition based on Loess (STL)method is utilized to decompose the dam displacement time series into seasonal, trend, and remainder components. Then the extremely randomized trees(extra-trees) model is used to predict seasonal components based on the causal models and influencing factors, whereas the stacked Long-Short Term Memory (LSTM)model is used to predict trend and remainder components based on the numerical models and historical observation data. Finally, the predicted results of the three components are aggregated to obtain the total predicted dam displacement. Seven state-of-the-art methods are introduced as benchmark methods to verify the effectiveness and feasibility of the proposed model. To quantitatively evaluate and compare the prediction results, three evaluation indicators, and a statistic test method are introduced. The experimental results show that the proposed model is the best-performing method compared with other benchmark methods both in prediction accuracy and stability. This indicates the proposed novel combined model STL-extra-trees-LSTM is a promising method for predicting displacement time series.

Underwater structural defects in hydraulic tunnels are highly concealed and difficult to be identified by conventional manual methods. Remotely operated vehicle combined with visible light cameras can provide a noncontact and high spatial resolution damage detection solution. However, manually extracting useful structural damage-related information from massive data is time-consuming and involves high labor cost. This article proposes an integrated pixel-level underwater structural multi-defects instance segmentation and quantification framework for hydraulic tunnels via machine vision and deep learning. Firstly, a tunnel lining underwater structural multi-defects video dataset is developed. Next, an improved You Only Look At CoefficienTs for Edge devices is used to build the detector by exploiting temporal redundancy in videos. Three backbone detectors are used to trade off the balance between detection accuracy and efficiency, and a cross-domain transfer learning strategy is introduced to reduce model training costs and data dependencies. Various complicated tunnel underwater inspection scenarios, including uneven illumination, tilt shooting, high brightness, and motion blur scenarios, are used to evaluate model generalization capability. Experimental results show that ResNet50-based YolactEdge can well trade off the balance between accuracy and speed, which achieves 92.47 bbox mAP, 92.15 mask mAP, and 39.27 FPS in the testing set. A quantification evaluation method is proposed to quantify the detection results and extract the geometric features of structural defects based on digital image processing techniques. The proposed method can accurately identify the number, size, and area of tunnel underwater structural defects, providing data support for subsequent reinforcement.

Seepage monitoring is a vital task in the risk management of concrete dams. Considering the lag effect of input factors, this paper presents a novel seepage monitoring model for concrete dams and proposes an effective identification method of lag process. Firstly, extreme gradient boosting (XGBoost) were adopted to predict the dam seepage. Hybridizing grey wolf optimization (HGWO) which integrates differential evolution (DE) into grey wolf optimization (GWO) and five-fold cross validation were utilized to optimize the hyper-parameters of XGBoost. Secondly, under the same search range and four evaluation indicators, the models optimized respectively by HGWO and three other algorithms were compared to confirm the global optimization capability of HGWO. Six state-of-art methods were also introduced to verify the effectiveness and feasibility of the proposed model. Then, based on the computation method of factor importance in decision tree models, we evaluated the relative importance of each component in the proposed model. Finally, according to the factor importance, the lag process of upstream water level and rainfall was identified, meanwhile a new equivalent water level calculation method is proposed. Monitoring data from three piezometric tubes on a concrete dam were taken as the experimental object. The results show that the improved HGWO has stronger global optimization ability, and the HGWO-XGBoost model achieves satisfactory prediction for seepage in concrete dams. Compared with the traditional trial-and-error method, the lag process computation method proposed in this paper provides a better recognition effect, which is of great value to the seepage monitoring and control of concrete dams.