When colossal gravity-driven mass flows enter a body of water, they may generate waves which can have destructive consequences on coastal areas. A number of empirical equations in the form of power functions of several dimensionless groups have been developed to predict wave characteristics. However, in some complex cases (for instance, when the mass striking the water is made up of varied slide materials), fitting an empirical equation with a fixed form to the experimental data may be problematic. In contrast to previous empirical equations that specified the mathematical operators in advance, we developed a purely data-driven approach which relies on datasets and does not need any assumptions about functional form or physical constraints. Experiments were carried out using Carbopol Ultrez 10 (a viscoplastic polymeric gel) and polymer–water balls. We selected an artificial neural network model as an example of a data-driven approach to predicting wave characteristics. We first validated the model by comparing it with best-fit empirical equations. Then, we applied the proposed model to two scenarios which run into difficulty when modeled using those empirical equations: (i) predicting wave features from subaerial landslide parameters at their initial stage (with the mass beginning to move down the slope) rather than from the parameters at impact; and (ii) predicting waves generated by different slide materials, specifically, viscoplastic slides, granular slides, and viscoplastic–granular mixtures. The method proposed here can easily be updated when new parameters or constraints are introduced into the model.

Displacement data modelling is of great importance for the safety control of concrete dams. The commonly used artificial intelligence method modelled the displacement data at each monitoring point individually, i.e., the data correlations between the monitoring points are overlooked, which leads to the over-fitting problem and the limitations in the generalization of model. A novel model combines Gaussian mixture model and Iterative self-organizing data analysing (ISODATA-GMM) clustering and the random coefficient method is proposed in this article, which takes the temporal-spatial correlation among the monitoring points into account. By taking the temporal-spatial correlation among the monitoring points into account and building models for all the points simultaneously, the random coefficient model improves the generalization ability of the model through reducing the number of free model variables. Since the random coefficient model supposed the data follows normal distributions, we use an ISODATA-GMM clustering algorithm to classify the measuring points into several groups according to its temporal and spatial characteristics, so that each group follows one distribution. Our model has the advantage of having a stronger generalization ability.

Structural health monitoring is an effective method to evaluate the safety status of dams. Measurement error is an important factor which affects the accuracy of monitoring data modeling. Processing the abnormal monitoring data before data analysis is a necessary step to ensure the reliability of the analysis. In this paper, we proposed a method to process the abnormal dam displacement monitoring data on the basis of matrix manipulation and Cuckoo Search algorithm. We first generate a scatter plot of the monitoring data and exported the matrix of the image. The scatter plot of monitoring data includes isolate outliers, clusters of outliers, and clusters of normal points. The gray scales of isolated outliers are reduced using Gaussian blur. Then, the isolated outliers are eliminated using Ostu binarization. We then use the Cuckoo Search algorithm to distinguish the clusters of outliers and clusters of normal points to identify the process line. To evaluate the performance of the proposed data processing method, we also fitted the data processed by the proposed method and by the commonly used 3-$\sigma$ method using a regression model, respectively. Results indicate that the proposed method has a better performance in abnormal detection compared with the 3-$\sigma$ method.

Coastal regions are increasingly vulnerable to sea-level rise and extreme storm events, making the accurate prediction of wave run-up on seawalls crucial for effective flood and erosion protection. This study presents a novel hybrid approach combining K-means clustering with artificial neural networks (ANNs) to predict wave run-up distance. The method begins with dimensionless analysis to scale all the variables, followed by data segmentation using K-means clustering to group wave characteristics such as the Froude number, scaled distance from the wave front to the shoreline, and wave nonlinearity. These clusters help to focus the ANN on more homogeneous wave conditions, significantly improving prediction accuracy. Two-dimensional flume experiments systematically varied wave height, period, and steepness, producing a robust dataset that accounts for a range of wave conditions. The model’s performance is demonstrated through a high $R^2$ value of 0.97 and low mean squared error (MSE) of 0.0092, surpassing traditional ANN models in its ability to capture complex wave dynamics.

Settlement prediction is of great importance for safety control of concrete-face rockfill dams (CFRDs) during the operation stage. However, the prediction accuracy achieved by the commonly used hydrostatic–seasonal–time (HST) methods, without the consideration of the previous conditions of influencing factors, is not competitive. Moreover, in most methods, settlement data at each monitoring point are modeled individually; the correlation relationships between settlements are neglected. In this paper, a method based on an optimized long short-term memory (LSTM) model is proposed to predict the settlement of CFRDs, modeling multiple monitoring data series with strong correlation relationships simultaneously. In the method, settlement data series are classified into several categories, firstly according to a global relevance measure. Then, the cuckoo search (CS) algorithm is applied to optimize the hyper-parameters in the neural network structure of LSTM. Ultimately, the LSTM model is utilized to predict the multiple settlement data series classified in the same category. Results indicate that the proposed method has a better prediction performance compared with the LSTM model, the back propagation neural network (BPNN) model, and the HST with single monitoring point.