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The in situ stress distribution is one of the driving factors for the design and construction of underground engineering. Numerical analysis methods based on artificial neural networks are the most common and effective methods for in situ stress inversion. However, conventional algorithms often have some drawbacks, such as slow convergence, overfitting, and the local minimum problem, which will directly affect the inversion results. An intelligent inverse method optimizing the back-propagation (BP) neural network with the particle swarm optimization algorithm (PSO) is applied to the back analysis of in situ stress. The PSO algorithm is used to optimize the initial parameters of the BP neural network, improving the stability and accuracy of the inversion results. The numerical simulation is utilized to calculate the stress field and generate training samples. In the application of the Shuangjiangkou Hydropower Station underground powerhouse, the average relative error decreases by about 3.45% by using the proposed method compared with the BP method. Subsequently, the in situ stress distribution shows the significant tectonic movement of the surrounding rock, with the first principal stress value of 20 to 26 MPa. The fault and the lamprophyre significantly influence the in situ stress, with 15–30% localized stress reduction in the rock mass within 10 m. The research results demonstrate the reliability and improvement of the proposed method and provide a reference for similar underground engineering.

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Landslides are usually caused by geological processes such as rainstorms or earthquakes and may have a massive impact on human production and life. The hazard chain of landslide-river blockage-outburst flood is the most common hazard chain caused by landslides. A database based on existing landslide cases was established in this paper to investigate the assessment formulas of river blocking risk, dam stability, and peak flood discharge after the dam break. A risk assessment model of the landslide-river blocking-breaching hazard chain was established with the vulnerability downstream. The case of the Baige landslide verifies the applicability of the model. This model can be used in a landslide-prone area to predict whether it will form a relatively massive river blockage after the landslide occurs, whether the landslide dam formed by river blockage will breach in a short time, as well as the impact of the outburst flood on the downstream area.

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