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The steel box girder bridge is a structure composed of mutually vertical stiffening ribs (longitudinal ribs and transverse ribs) that carry the loads of vehicles. Since the external loads are usually complex and variable, the rational design of the bridge components is a topic that deserves more attention. The purpose of this study is to explore the optimal range of some of the component design parameters, expecting to reduce costs while ensuring the stress-carrying capacity. A finite element model (FEM) based on ABAQUS was built and the results were verified by laboratory experiments. The varied thicknesses of the bridge deck, diaphragm, and U-rib were explored based on the validated FEM. The simulation results fit well with the experimental results, which proved that the FEM was quite reliable. The stress analysis results demonstrated an optimal range of 18–20 mm for bridge deck thickness, 14–16 mm for diaphragm thickness, and 8–10 mm for U-rib thickness. The present study holds significant reference value for the design and optimization of multiple steel box girder bridge components, which could further provide a theoretical foundation for related research in this field.

The lack of reliable negative samples is an important factor limiting the quality of machine learning-based debris flow susceptibility mapping (DFSM). The purpose of this paper is to propose multiple negative-sample acquisition strategies for DFSM considering different sample representation forms. The sample representation forms mainly include a single grid, multi-grid, and watershed unit, and the negative-sample acquisition strategies are based on support vector machine (SVM), spy technique, and isolation forest (IF) methods, respectively. These three strategies can assign a value to all the samples based on different assumptions, and reliable, negative samples can be generated from samples with values below a predefined threshold. Combining different sample representation forms with negative sample acquisition strategies, nine datasets were then involved in random forest (RF) modeling. The receiver operating characteristic (ROC) curves and related statistical results were used to evaluate the models. The results show that the strategy based on the spy technique is suitable for multiple datasets, while the IF-based strategy is well-adapted to the watershed unit datasets. This study can provide more options for improving the quality of datasets in DFSM, which can further improve the performance of machine learning models.

Light steel keel fireproof exterior walls are one of the new composite walls that have been widely used in recent years. This paper analyzes the factors affecting the displacements of the connectors and wall panels and modifies the corresponding displacement model. ABAQUS was used to establish the simulation analysis of the L-shaped and special-shaped steel angle component, and the results were compared with the experimental model to verify the rationality. The effect of vertical keel spacing and layout, the number of special-shaped steel angles, the L-shaped steel angle thickness, and the number of self-tapping screws for L-shaped steel angles to steel beams were explored based on the validated finite element model. The calculation model was modified based on the results of the simulation. The results showed that the simulation results fit well with the experimental results, which verified the reliability of the finite element model. The number of connectors and the number of self-tapping screws are the main factors affecting the special-shaped and L-shaped steel angle components, respectively. By modifying the rotation constraint factor of the L-shaped steel angle and adding a discount factor for the wall panel, the accuracy of the associated displacement calculation model could be improved.

Debris flow susceptibility mapping (DFSM), which has proven to be one of the most effective tools for risk management, faces a variety of problems. To realize the rational use of debris flow sample resources and improve the modeling efficiency, a unified model based on transfer learning was established for cross-regional DFSM. First, samples with 10 features collected from two debris flow-prone areas were separately used to perform factor prediction ability analysis (FPAA) based on the information gain ratio (IGR) method and then develop traditional machine learning models based on random forests (RF). Secondly, two feature matrices representing different areas were projected into a common latent feature space to obtain two new feature matrices. Then, the samples with new features were used together for FPAA and developing a unified machine learning model. Finally, the performance of the models was obtained and compared based on the area under curves (AUC) and some statistical results. All the conditioning factors played different roles in debris flow prediction in the two study areas, based on which two traditional models and a unified model were established. The unified model based on feature transferring realized efficient cross-regional modeling, solved the unconvincing problem of limited sample modeling, and enabled more accurate identification of some debris flow samples.

Reasonable depth of pile embedment is one of the key factors for the success of deep foundation pit projects. This paper has taken a deep foundation pit project in a granite residual soil area in Shenzhen as an example and used physical model tests to study the deformation law of the piles and the surrounding soil during the excavation of the deep foundation pit, revealing the variation law of earth pressure in time and space in the pit and then verified it by numerical simulation. The influence of the embedded depth of the pile on the deformation and earth pressure of the deep foundation pit is then explicitly discussed. The study shows that the embedded depth has a significant effect on the deformation and earth pressure distribution of the foundation pit. The earth pressure in front of the pile tends to approach the passive earth pressure as the embedment depth decreases, while the earth pressure behind the pile is in between the Rankine active earth pressure and the static soil pressure; the settlement value and settlement range of the surrounding soil are doubled. The pile displacement increases as the maximum displacement point rises. The maximum displacement of the pile body was used as the basis for determining the instability of the foundation pit. The optimum embedded depth is obtained when the depth of embedment of the pile is 0.22 H (H is the excavation depth of the foundation pit).

Rock resistant coefficient is a key parameter in rock engineering and can reflect the resistance of surrounding rocks. According to the load-structure method, there are many different formulas in the determination of rock resistant coefficient using elasticity or plasticity theory. Based on the generalized Hoek-Brown failure criterion, this paper develops a closed-form formula for calculating the rock resistant coefficient. The sensibility of different parameters on the rock resistant coefficient is also analyzed. The fact shows that the formula is sensitive to a, σc, and Kd comparing with mb and s especially when they are in a low value, laying a foundation for the determination of rock resistant coefficient in similar lining tunnels.

Objective: Both serum uric acid (SUA) levels and left atrium diameter (LAD) associate with AF. However, the influence of SUA status for the associated risk of AF related to LAD in hypertension patients is currently unknown.Methods: We retrospectively analyzed a hospital-based sample of 9,618 hypertension patients. Standard electrocardiograms were performed on all patients and were interpreted by expert electro-physiologists.Results: Overall 1,028 (10.69%) patients had AF out of 9,618 patients. In men >65 years of age, the prevalence of AF in the1st, 2nd, and 3rd tertiles of SUA among those grouped in the third tertile of LAD were 9, 12.3, and 21.7%, respectively. In the hyperuricemia group, the OR (95% CI) of AF for the highest tertile of LAD in men ≤ 65 years of age was 3.150 (1.756, 5.651; P < 0.001). Similarly, the hyperuricemic men in the 3rd LAD tertile had a higher likelihood of AF than those belonging to the 1st tertile. The ORs and (95% CIs) were 3.150 (1.756, 5.651; P < 0.001) and 5.522 (2.932, 10.400; P ≤ 0.001) for patients ≤ 65 and >65 years of age. An increase in SUA values was significantly associated with an increased likelihood of AF among women at the top tertiles of LAD, with the OR (95% CI) = 4.593 (1.857, 11.358; P = 0.001). Also, men> 65 years of age with large LAD, present at the third tertile of SUA, had a higher likelihood of AF, with the OR (95% CI) = 2.427 (1.039, 5.667; P < 0.05).Conclusion: SUA levels and LAD are associated with AF in patients with hypertension and the risk of AF associated with LAD increases among those with hyperuricemia.

Secondary compression studies can provide insights for evaluating the engineering potential and environmental impact of soil. The objective of this research was to investigate the effect of bound water content on the secondary compression of marine silty clay. To this end, a novel method was established based on thermogravimetric analysis (TGA) to determine the contents and limits of strongly bound water and weakly bound water for three typical marine silty clays including Tianjin mucky silty clay (TJ), Qingdao mucky silty clay (QD), and Weihai silty clay (WH). A total of 17 groups of uniaxial confined compression tests were performed for reconstituted samples at different absolute water contents under the condition of multistage loading to investigate their secondary compression characteristics. The results show that the initial dehydration temperature of strongly bound water (Ts) corresponds to the peak of the derivative thermogravimetry (DTG) curve. The values of Ts for TJ, QD and WH are 112.35 °C, 109.67 °C and 118.46 °C, respectively. The initial dehydration temperatures of weakly bound water (Tw) for TJ, QD and WH are 55.26 °C, 52.56 °C and 56.56 °C, respectively. The secondary compression coefficient Cα changes little before the strongly bound water limit and increases dramatically as weakly bound water content increases at the same vertical stress. A piecewise linear model and a quadratic polynomial model are established for calculating the average secondary compression coefficient from bound water content. Weakly bound water is the determining factor controlling secondary compression. Increasing the bound water content weakens the connection force and friction force among the particles and the viscosity of weakly bound water. The results will guide decisions on long-term settlement assessment and facilitate understanding of the secondary compression mechanism of marine silty clays affected by bound water.

Landslides frequently occur because of natural or human factors. Landslides cause huge losses to the economy as well as human beings every year around the globe. Landslide susceptibility prediction (LSP) plays a key role in the prevention of landslides and has been under investigation for years. Although new machine learning algorithms have achieved excellent performance in terms of prediction accuracy, a sufficient quantity of training samples is essential. In contrast, it is hard to obtain enough landslide samples in most the areas, especially for the county-level area. The present study aims to explore an optimization model in conjunction with conventional unsupervised and supervised learning methods, which performs well with respect to prediction accuracy and comprehensibility. Logistic regression (LR), fuzzy c-means clustering (FCM) and factor analysis (FA) were combined to establish four models: LR model, FCM coupled with LR model, FA coupled with LR model, and FCM, FA coupled with LR model and applied in a specific area. Firstly, an inventory with 114 landslides and 10 conditioning factors was prepared for modeling. Subsequently, four models were applied to LSP. Finally, the performance was evaluated and compared by k-fold cross-validation based on statistical measures. The results showed that the coupled model by FCM, FA and LR achieved the greatest performance among these models with the AUC (Area under the curve) value of 0.827, accuracy of 85.25%, sensitivity of 74.96% and specificity of 86.21%. While the LR model performed the worst with an AUC value of 0.736, accuracy of 77%, sensitivity of 62.52% and specificity of 72.55%. It was concluded that both the dimension reduction and sample size should be considered in modeling, and the performance can be enhanced by combining complementary methods. The combination of models should be more flexible and purposeful. This work provides reference for related research and better guidance to engineering activities, decision-making by local administrations and land use planning.

Collapses, landslides, and debris flows are the main geological hazards faced by mankind, which bring heavy losses of life and property to people every year. The purpose of this paper is to establish a method for determining the optimal weighting scheme for multiple geological hazard susceptibility mapping. The information gain ratio (IGR) method was used to analyze the predictive ability of the conditioning factors. The support vector machine (SVM) algorithm was used to evaluate the susceptibility to collapse, landslide, and debris flow of the study area. The receiver operating characteristic curves (ROC) and classification statistics of geological hazard samples were applied to evaluate the performance of the models. The analytic hierarchy process (AHP) and frequency ratio (FR) method were combined to determine the optimal weighting scheme for collapse, landslide, and debris flow. All the conditioning factors have shown a certain predictive ability, making the models of collapse, landslide, and debris flow achieve very good performance. The multiple geological hazard susceptibility maps with the weights of 0.297, 0.539, and 0.164 for collapse, landslide, and debris flow was optimal for this study area with high-precision classification of all the geological hazard samples. The conclusions of this paper could provide meaningful references for risk migration and land use in the study area.