Seismic fragility analysis is an efficient method to evaluate the structural failure probability during earthquake events. Among the existing fragility analysis methods, the probabilistic seismic demand model (PSDM) and the joint probabilistic seismic demand model (JPSDM) are generally used to compute the component and system fragility, respectively. However, the statistical significance behind the parameters related to the current PSDM and JPSDM are not comparable. Aside from that, when calculating the system fragility, the Monte Carlo sampling (MCS) method is time-consuming. To solve the two flaws, in this paper, the logarithm piecewise functions were used to generate the PSDM and the JPSDM, and the MCS was replaced by the univariate conditioning approximation (UCA) method. The concepts and application procedures of the proposed fragility analysis methods were elaborated first. Then, the UCA method was illustrated in detail. Finally, fragility curves of a steel arch truss case study bridge were generated by the proposed method. The research results indicate the following: (1) the proposed methods unify the data sources and statistical significance of the parameters used in the PSDM and the JPSDM; (2) the logarithmic piecewise function-based PSDM sensitively reflects the changing trend of the component’s demand with the fluctuation of the seismic intensity measure; (3) under transverse seismic waves, major injuries happen on the side bearings of the bridge, while slight damage may occur on each pier, and as the seismic intensity measure increases, the side bearings are more likely to be damaged; (4) for the severe damage and the absolute damage of the studied bridge, the system fragility curves are closer to the upper failure bounds; and (5) compared with the MSC method, the accuracy of the UCA method can be guaranteed with less calculation time.

In order to explore the correlation between the compactness of sand core samples and its surface image features and to provide the basis for rapid identification and recognition of core samples in engineering investigation, a typical image data set of sand core samples disturbed by drilling construction in practical engineering has been established, using Python language to compile algorithm to calculate one-dimensional entropy and two-dimensional entropy of 60 groups of sand core samples with different densities. The influence of different sand core compactness on surface entropy characteristics was discussed, and the following conclusions were obtained: (1) Affected by drilling construction and disturbance, the looser the sand core surface particles are, the worse the sorting is and the more irregular the shape characteristics are. There is a close relationship between grain texture and compactness. (2) The calculation results of sand image entropy of one-dimensional entropy and two-dimensional entropy showed that the entropy value of loose, slightly dense, and medium dense sand images is positively correlated with the compactness of sand. (3) The maximum variance of two-dimensional entropy of loose, slightly dense, and medium dense sand image in the same borehole is less than 0.09, and the data variance amplification effect of two-dimensional entropy of image is mainly between different boreholes. (4) The dense feature of core sample structure forms an ordered structure with a gray change boundary, which increases the roughness of the image and leads to the increase of entropy. The two-dimensional entropy reveals the internal correlation mechanism of the influence of the engineering state on the surface structure of sand more clearly than the one-dimensional entropy and more effectively characterizes the dense degree of sand particles. (5) Using two-dimensional entropy to judge the compactness of sand image in the same borehole, the data fluctuation is small, and the algorithm is stable and reliable. The research results have reference values for the detection and analysis of sand sample density in geotechnical engineering investigation.

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Based on the methods of geological drilling, literature research, induction and statistics, the site investigation of an old industrial park reconstruction project in Haizhu District of Guangzhou city was completed. The landform, formation lithology, structure and hydrogeological characteristics were systematically identified. Combined with indoor test and in-situ test, the variation rules of physical and mechanical parameters of argillaceous siltstone with different weathering degrees in the site were analyzed, and the evaluation results were given. The engineering suitability and the foundation stability are evaluated. The results showed that the weathering of soft rock makes the statistical standard values of compression coefficient, compression modulus, direct fast shear cohesion and internal friction angle of rock and soil decrease. Under the saturated state, the average value of strongly weathered argillaceous siltstone is 1.51 MPa, and that of moderately weathered argillaceous siltstone is 6.2 MPa. The strength of soft rock is low after softening with water, and the differential weathering of rock mass is strong. There are great differences in rock strength, local differences between fully and strongly weathered rock surfaces, and great changes in thickness. The weathering is extremely heterogeneous, in which the influence of structure, lithology, fissures and groundwater played a key role. The foundation is generally in a stable state, and the site is suitable for the construction of the project. The research results can provide reference for similar geotechnical engineering investigation and design.

The bearing capacity of drilling with pre-stressed concrete pile cased pile (hereafter referred to as DPC pile) is closely related to the grouting effect on an annular gap between a pre-stressed high-strength concrete (PHC) pipe pile wall and a hole wall (hereafter referred to as the an annular pile–soil gap). A physical grouting model testing system for the DPC pile based on a high-precision three-dimensional (3D) scanner and a multi-functional grouting box has been independently developed. In this method, 3D geometric size and spatial point information of the grouting stone have been quantitatively characterized. The influences of the water–cement ratio, grouting pressure, collapsed holes, and falling sand have been studied. The conclusions are obtained as follows: (1) a quantitative characterization method of the 3D geometric dimensions of the grouting stone based on a 3D scan is accurate and reliable and can overcome the shortcomings of traditional manual measurement. (2) In the same horizontal plane, grouting body thickness gradually decreases as its horizontal distance from the grouting outlet increases, and the higher the elevation, the greater the rate of decrease; conversely, the lower the elevation, the slower the rate of decrease. When the horizontal distance from the pulp outlet is equal, slurry thickness gradually decreases as the height increases. (3) For the grouting liquid with a water–cement ratio of 0.5, grouting pressure should not be less than 0.6 MPa. (4) When the falling sand is not far above the grouting outlet, a grouting root system can be formed, whose grout veins, complexity, and grout coverage area can be optimally improved by changing the fluidity of the grout and grouting pressure. When the falling sand is on the side of the grouting outlet or the surface of the grouting outlet but far away from the grouting outlet, it is easy to be avoided by the grout, which can greatly reduce the grouting effect.

Sparse unmixing plays a crucial role in the field of hyperspectral image unmixing technology, leveraging the availability of pre-existing endmember spectral libraries. In recent years, there has been a growing trend in incorporating spatial information from hyperspectral images into sparse unmixing models. There is a strong spatial correlation between pixels in hyperspectral images (that is, the spatial information is very rich), and many sparse unmixing algorithms take advantage of this to improve the sparse unmixing effect. Since hyperspectral images are susceptible to noise, the feature separability of ground objects is reduced, which makes most sparse unmixing methods and models face the risk of degradation or even failure. To address this challenge, a novel robust dual spatial weighted sparse unmixing algorithm (RDSWSU) has been proposed for hyperspectral image unmixing. This algorithm effectively utilizes the spatial information present in the hyperspectral images to mitigate the impact of noise during the unmixing process. For the proposed RDSWSU algorithm, which is based on ℓ1 sparse unmixing framework, a pre-calculated superpixel spatial weighting factor is used to smooth the noise, so as to maintain the original spatial structure of hyperspectral images. The RDSWSU algorithm, which builds upon the ℓ1 sparse unmixing framework, employs a pre-calculated spatial weighting factor at the superpixel level. This factor aids in noise smoothing and helps preserve the inherent spatial structure of hyperspectral images throughout the unmixing process. Additionally, another spatial weighting factor is utilized in the RDSWSU algorithm to capture the local smoothness of abundance maps at the sub-region level. This factor helps enhance the representation of piecewise smooth variations within different regions of the hyperspectral image. Specifically, the combination of these two spatial weighting factors in the RDSWSU algorithm results in an enhanced sparsity of the abundance matrix. The RDSWSU algorithm, which is a sparse unmixing model, offers an effective solution using the alternating direction method of multiplier (ADMM) with reduced requirements for tuning the regularization parameter. The proposed RDSWSU method outperforms other advanced sparse unmixing algorithms in terms of unmixing performance, as demonstrated by the experimental results on synthetic and real hyperspectral datasets.

Aiming at the problem that the drilling core cannot be drilled and core is unable to be drilled to find the stratum due to the construction measures or large equipment occupation in the survey site, based on the thickness information of the adjacent hole position of the borehole to be measured, the BP neural network model for stratum prediction was constructed, and the training data sampling method and comprehensive analysis results were put forward, which were based on the training data sampling method and comprehensive analysis results in the front, back, left, right and circle positions of the borehole, combined with the actual investigation Engineering. The influence of soil layer training data collected by different methods on the accuracy of soil layer thickness prediction was analyzed. The results showed that: (1) By comparing the prediction results of 10 groups of actual engineering holes to be measured, it was revealed that the prediction accuracy of soil thickness had great correlation with the orientation selection. (2) In azimuth prediction, each group collects 21 adjacent boreholes as training data samples by continuous cycle of three longitudinal and seven horizontal directions. The prediction results of neural network were stable. (3) A selection strategy was proposed to analyze the preliminary prediction results of neural network. Based on the circular prediction results, the data with the difference within ±0.2m in azimuth prediction were compared and screened. The comprehensive prediction value obtained by test was very close to the true value, and the data error is within 9%, and the prediction effect was good. The research results provide a way of thinking for the prediction of small sample soil sequence, and can provide reference for geotechnical engineering investigation and design application.