The Aryskum Depression in the South Turgay Basin has shown improving exploration prospects for subtle reservoirs, due to investment in the exploration workload and more comprehensive geological research. Among them, lithologic stratigraphic reservoirs have gradually become one of the focuses of oil and gas exploration. At present, deduction of the sedimentary characteristics of the target layer through core wells using artificial exploration has become an urgent problem to be solved. We selected 16 artificially interpreted coring wells in the Aryskum Graben for this study. Using the parameters of the gamma-ray (GR) curve of coring wells and support vector machine (SVM) classification algorithms, we developed an automatic identification model of sedimentary facies in the study area. The application of the SVM includes the following steps: Firstly, using the GR curve of 16 coring wells, six quantitative indexes defined as standard deviation, relative gravity, curve amplitude ratio, average median, average slope, and mutation amplitude, are selected to quantify the logging curve in the study area, thus realizing the description of the logging curve form. Secondly, training samples are selected to establish an SVM classification model. Finally, a quantitative discrimination model based on the SVM algorithm is established to realize the classification of depositional facies. Field application shows that this solution can be effectively used in uncored wells to identify depositional facies with a rate of accuracy approaching 70%. Our results provide new methods for the identification of sedimentary facies in the study area. The results will also provide a theoretical basis, as well as data basis, for further fine division of microfacies in the study area.

The yield performances and failure mode of reinforced concrete (RC) columns, which are critical structural performances to the design and research of engineering structures, have a significant impact on the dynamic response, the performance level, and the design of seismic ductility. The traditional empirical theoretical method used to predict the yield performances and failure mode leads to large dispersions in most cases. To better estimate the yield performances and failure mode of RC columns, this paper developed a novel neural network method. Empirical theoretical models are used to determine the input parameters of the neural network by analyzing the factors that affect the yield performance and failure mode of RC columns, and the rationality of these parameters is verified by sensitivity analysis. The back-propagation (BP) neural network method was adopted. The influence of the number of hidden neurons was studied to improve the model accuracy. Comparative analysis revealed that the prediction results of the neural network are in good agreement with the experimental results and are more accurate than other traditional empirical theoretical models. The initial parameters were optimized using particle swarm optimization (PSO), which has been proven to be superior to the genetic algorithm (GA) and sparrow search algorithm (SSA) optimization methods in terms of effectiveness and computation time. The high generalization ability of the prediction model was calibrated using the test and validation sets and another eight additional sets of experimental data. The proposed method provides a new way to predict the structural performance under seismic actions when experimental data are insufficient.

Cable-stayed bridges have been widely used in large-span bridge engineering because of their large span capacity and novel structure. The frequent traffic of vehicles transporting flammable and explosive materials has increased the incidence of bridge fires. After being burned, the cable-stayed bridge will suffer from varying degrees of damage, which affects its performance. Therefore, mechanical analysis and evaluation of the fire-damaged cable-stayed bridge are necessary. Due to the development of technology, the structural analysis of cable-stayed bridges has gradually shifted from experimental methods to numerical simulation or artificial intelligence methods, and from local performance research to holistic research. In this paper, a fire accident in the Sifangtai Bridge in Harbin, China, is taken as a case study. Finite element software and damage theory calculation methods were used, and the static and dynamic performances of the bridge under the condition of cable fire damage were analyzed. The results show that the variation of cable force during the movement of vehicle load along the bridge is relatively small, within the range of 7% to 12%. The fusing of the upper cables of the bridge tower has the greatest impact on the deflection of the beam, while the fusing of lower cables has the same impact on the deflection of the entire bridge as the undamaged state. Near the fused cables, cable forces change significantly, increasing by over 20%. As the degree of damage increases, the increase in deflection of the beam becomes more pronounced. The impact of different degrees of cable damage on the dynamic performance of cable-stayed bridges is reflected in quantitative changes. As the degree of cable damage and the amount of fusing increase, the change in structural frequency becomes more pronounced. This paper not only provides technical support and a theoretical basis for the performance analysis of cable-stayed bridges damaged by fire, but it also improves the research content of combining static and dynamic performance, which provides important reference values for similar research in the future.

China is now undergoing the largest and fastest urbanization process in the world, developing various reinforced concrete high-rise buildings during this process. Most high-rise structures are constructed in densely populated and economically active areas and earthquakes pose a potential possibility of serious economic loss and casualties, which cannot be ignored. This paper conducts on-site dynamic tests on 129 high-rise RC buildings in eastern China by utilizing ambient vibration measurement. Fundamental vibration periods, second vibration periods, and third vibration periods are statistically analyzed in translational directions based on two high-rise building types: shear wall slab building and shear wall tower building (including framed shear wall structures and framed tube structures). After fully considering building usage conditions and comparing the fitting formulas in existing codes, fitting empirical formulas of the vibration periods in two translational directions based on the above structural types are given. The ratio distribution ranges of vibration periods are calculated by utilizing the vertex displacement method, concentrated mass method, and Rayleigh-Ritz energy method. In addition, by analyzing the vibration period ratio in horizontal directions of 543 high-rise RC buildings, different ranges are distinguished as well as suggested boundaries are given. The conclusion of this paper provides a reference and convenient method for future on-site dynamic testing and a new proposal is provided to the empirical formula of vibration period in the current building seismic design code.