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Highlights Wood plastic composite (WPC) is a kind of eco‐friendly material made of agricultural and forest industry waste and residues compounded with thermoplastic. The defects of traditional WPC, such as low strength and high creep, greatly limit its engineering applications. To solve the problem, this paper proposed two methods of glass fiber reinforced polymer (GFRP) reinforced PVC‐based WPC panels (G‐WPC) by bonding GFRP sheets or embedding GFRP bars in the tensile zone of the WPC panels. The effects of the thickness of GFRP sheet and reinforcement ratio of GFRP bar on the flexural property of G‐WPC were comparatively analyzed by carrying out four‐point bending tests and finite element simulations. The results showed that the failure modes of the GFRP sheets reinforced specimens were mainly flexural fracture and interface debonding, and the GFRP bars reinforced specimens were flexural fracture and excessive deformation. The ultimate flexural bearing capacity of both GFRP sheets/bars reinforced specimens could be improved by more than 200%. GFRP bar reinforced specimens had better ductility than reinforced with sheet. The effects of WPC density, GFRP sheet fiber layup angle and GFRP bar diameter on the flexural behavior of G‐WPC were further parametrically analyzed using the finite element model (FEM). Two effective flexural reinforcement methods of WPC were proposed. The optimum thickness of GFRP sheet was obtained based on experimental tests. The suitable reinforcement ratio of GFRP bars was studied by tests and FEM. Effects of WPC density, GFRP fiber layup angle and bar diameter were analyzed. [ABSTRACT FROM AUTHOR]

Three‐dimensional displacement monitoring over long distances has been a long‐standing concern in the structural health monitoring industry. In this study, a multi‐degree‐of‐freedom slope displacement monitoring method is developed by fusing computer vision and the 3D point triangulation method. Attributed to this method, the problems of outdoor binocular camera calibration, multi‐target mismatching, and outdoor illumination effects were solved. First, a two‐stage camera calibration method is proposed to accurately calibrate intrinsic and extrinsic camera parameters under a large field of view and long working distance conditions. Second, the adaptive spatial‐frequency method is proposed to calculate the coding and pixel coordinates of the monitored target. In this step, to solve the problem of mismatching monitored points in different camera frames, the Augmented Reality University of Cordoba code is introduced to provide a unique identity code for each monitored point. To mitigate the impact of illumination and other factors on pixel coordinate calculation, an adaptive pixel coordinate calculation method that combines information from the spatial and frequency domains is proposed., Third, based on the intrinsic and extrinsic parameters of the stereo camera and the pixel coordinates of the monitored points, the 3D coordinates of the monitored points are obtained through triangulation. Finally, the accuracy experiments and stability experiments are conducted. According to the results of the experiments, the measurement distance is positively correlated with the measurement error. And the baseline length is negatively correlated with the measurement error in the z‐direction. Ultimately, we suggest that the ratio of baseline length to measurement distance should be greater than 40%. When the recommended value is satisfied, the measurement error is less than 1 mm when the measurement distance is less than 40 m. When the measurement distance is equal to 90 m, the measurement error is less than 5 mm. Meanwhile, stability experiments of the algorithm were carried out, and in a period of outdoor validation experiments, the fluctuations were only sub‐millimeter, demonstrating good anti‐interference performance. Moreover, the method proposed in this study successfully monitored a landslide disaster in Guangxi, which demonstrated its outstanding practical application capabilities. [ABSTRACT FROM AUTHOR]

Reservoirs exert a profound influence on the cycling of dissolved organic matter (DOM) in inland waters by altering flow regimes. Biological incubations can help to disentangle the role that microbial processing plays in the DOM cycling within reservoirs. However, the complex DOM composition poses a great challenge to the analysis of such data. Here we tested if the interpretable machine learning (ML) methodologies can contribute to capturing the relationships between molecular reactivity and composition. We developed time‐specific ML models based on 7‐day and 30‐day incubations to simulate the biogeochemical processes in the Three Gorges Reservoir over shorter and longer water retention periods, respectively. Results showed that the extended water retention time likely allows the successive microbial degradation of molecules, with stochasticity exerting a non‐negligible effect on the molecular composition at the initial stage of the incubation. This study highlights the potential of ML in enhancing our interpretation of DOM dynamics over time. Plain Language Summary: As a comprehensive man‐made infrastructure, reservoirs significantly influence the chemical composition, reactivity, and turnover time of dissolved organic matter (DOM) within inland waters. However, it remains elusive how DOM molecules respond to microbial processing over different time scales. Besides the well‐recognized predictive power of machine learning (ML) methodologies, we delved into the processes of tuning the ML models to acquire additional interpretability. We used an under‐sampling strategy to improve model performance and simultaneously observed the variations in model performance metrics for different biological reactivity pools over incubations with different durations. We find that shorter incubation periods result in a broader range of molecules disappearing, with a greater contribution of stochasticity, while the longer incubation allows the successive biodegradation of oxygen‐poor compounds, with a greater contribution of directed degradation. As a complement to traditional geochemical methods, we unveiled a novel perspective in understanding the DOM dynamics over time using ML. Key Points: Machine learning (ML) models were built to correlate the molecular composition and biological reactivity at the world's largest reservoirShorter incubations result in a broader range of molecules disappearing, with a greater contribution of stochasticityTuning the ML model contributes to yield additional interpretability beyond its well‐recognized predictive power [ABSTRACT FROM AUTHOR]