Structural health monitoring is of great significance to ensure the safety of marine pipes, while powering the required monitoring sensors remains a problem because the ocean environment is not amenable to the traditional ways of providing an external power supply. However, mechanical energy due to the vortex-induced vibration of pipelines may be harvested to power those sensors, which is a convenient, economic and environmentally friendly way. We here exploit a contact-separation mode triboelectric nanogenerator (TENG) to create an efficient energy harvester to transform the mechanical energy of vibrating pipes into electrical energy. The TENG device is composed of a tribo-pair of dielectric material films that is connected to a mass-spring base to guarantee the contact-separation motions of the tribo-pair. Experimental tests are conducted to demonstrate the output performance and long-term durability of the TENG device by attaching it to a sample pipe. A theoretical model for the energy harvesting system is developed for predicting the electrical output performance of the device. It is established that the normalized output power depends only on two compound variables with all typical factors taken into consideration simultaneously. The simple scale law is useful to reveal the underlying mechanism of the device and can guideline the optimization of the device based on multi-parameters analyses. The results here may provide references for designing contact-mode TENG energy harvesting devices based on the vibration of marine pipes and similar structures.

Surface texturing has become a potential method to obtain a low friction coefficient under dry/lubricated conditions for different mechanical product surfaces. The mechanism of friction and wear reduction from a micro-texture on the surface of cemented carbide cutting tools was investigated by dry cutting a titanium alloy. Three kinds of micro-textures, namely, line, sinusoidal and rhombic grooves, with different area occupancy rates were produced by a laser on the cemented carbide surface. Experiments and finite element simulation of ABAQUS were used to investigate the tribological characteristics of micro-textured cemented carbide. The results indicated that the line-textured cemented carbide with an area occupancy rate of 10% showed a low friction coefficient of 0.076, which is approximately 34% of the non-textured sample. Few adhesives appeared on the textured surface, while a large number of adhesives were attached to the smooth surface after 30 min of dry friction between the cemented carbide and the titanium alloy balls. Moreover, among the three textures, the line-groove texture has the smallest friction coefficient and a good anti-wear effect. The results show that the existence of a groove texture can effectively reserve the wear debris, reduce the bond wear and weaken the furrow effect.

Green concrete has been widely used in recent years because its production compliments environmental conservation. The prediction of the compressive strength of concrete using non-destructive techniques is of interest to engineers worldwide. Such methods are easy to carry out because they require little or no sample preparation. Conventional models and artificial intelligence models are two main types of models to predict the compressive strength of concrete. Artificial intelligence models main include the artificial neural network (ANN) model, back propagation (BP) neural network model, fuzzy model etc. Since both conventional models and artificial intelligence models are flawed. This study proposes to build a concrete compressive strength development over time (CCSDOT) model by using conventional method combined with the artificial intelligence method. The CCSDOT model performed well in predicting and fitting the compressive strength development in green concrete containing cement, slag, fly ash, and limestone flour. It is concluded that the CCSDOT model is stable through the use of sensitivity analysis. To evaluate the precision of this model, the prediction results of the proposed model were compared to that of the model based on the BP neural network. The results verify that the recommended model enjoys better flexibility, capability, and accuracy in predicting the compressive strength development in concrete than the other models.

The displacement of concrete dams effectively reflects their structural integrity and operational status. Therefore, establishing a model for predicting the displacement of concrete dams and studying the evolution mechanism of dam displacement is essential for monitoring the structural safety of dams. Current data-driven models utilize artificial data that cannot reflect the actual status of dams for network training. They also have difficulty extracting the temporal patterns from long-term dependencies and obtaining the interactions between the targets and variables. To address such problems, we propose a novel model for predicting the displacement of dams based on the temporal convolutional network (TCN) with the attention mechanism and multioutput regression branches, named MLA-TCN (where MLA is multioutput model with attention mechanism). The attention mechanism implements information screening and weight distribution based on the importance of the input variables. The TCN extracts long-term temporal information using the dilated causal convolutional network and residual connection, and the multioutput regression branch achieves simultaneous multitarget prediction by establishing multiple regression tasks. Finally, the applicability of the proposed model is demonstrated using data on a concrete gravity dam within 14years, and its accuracy is validated by comparing it with seven state-of-the-art benchmarks. The results show that the MLA-TCN model, with a mean absolute error (MAE) of 0.05mm, a root-mean-square error (RMSE) of 0.07mm, and a coefficient of determination (R²) of 0.99, has a comparably high predictive capability and outperforms the benchmarks, providing an accurate and effective method to estimate the displacement of dams.

The detection of anomalies in dam deformation is paramount for evaluating structural integrity and facilitating early warnings, representing a critical aspect of dam health monitoring (DHM). Conventional data-driven methods for dam anomaly detection depend extensively on historical data; however, obtaining annotated data is both expensive and labor-intensive. Consequently, methodologies that leverage unlabeled or semi-labeled data are increasingly gaining popularity. This paper introduces a spatiotemporal contrastive learning pretraining (STCLP) strategy designed to extract discriminative features from unlabeled datasets of dam deformation. STCLP innovatively combines spatial contrastive learning based on temporal contrastive learning to capture representations embodying both spatial and temporal characteristics. Building upon this, a novel anomaly detection method for dam deformation utilizing STCLP is proposed. This method transfers pretrained parameters to targeted downstream classification tasks and leverages prior knowledge for enhanced fine-tuning. For validation, an arch dam serves as the case study. The results reveal that the proposed method demonstrates excellent performance, surpassing other benchmark models.