Gongcheng Kexue Yu Jishu/Advanced Engineering Science

For the landslide susceptibility prediction (LSP) based on machine learning (ML) models, the reasonable selection of negative samples has an important influence on the LSP performance. Generally, the main selection methods include randomly selecting from the whole study area or from the specific attribute areas such as low slopes. The negative samples selected by the above methods are often inaccurate or biased, resulting in low accuracy and low reliability of LSP. To solve this problem, the coupling model of ML and information value (IV) method was proposed for LSP. Taking Ruijin City as the study area, the attribute values of the environmental factors were transformed into the IV values of the contribution to the landslide to obtain the very low and low susceptibility areas. The negative samples were randomly selected in the above areas for the training and validation of machine learning models. The new coupling models of IV–SVM and IV–RF were constructed for the LSP of Ruijin. Further, IV–SVM and IV–RF models were compared with the single SVM and RF model with negative samples randomly selected from the whole study area, as well as the low-slope SVM and RF model with negative samples randomly selected from specific attribute areas with a slope less than 2°. Finally, Kappa coefficient (KC) and receiver operating characteristic (ROC) curve were used to verify and compare the modeling results. The AUC values of the ROC curve and KC of IV–SVM and IV–RF models were 0.828, 0.920 and 0.876, 0.988, which were higher than those of single SVM, RF model and low-slope SVM, RF model, respectively. Meanwhile, IV–SVM and IV–RF models have a smaller mean value and larger standard deviation of a susceptibility probability distribution. Results showed that: 1) IV–SVM and IV–RF models had the higher LSP accuracies than those of the single SVM, RF model and low-slope SVM, RF model, respectively; 2) RF model had higher LSP accuracy compared to the SVM model; 3) The coupling model such as IV–RF could address the inaccuracy of negative sample sampling existing in the single model and the shortcomings of the low slope model in the selection of slope interval, thus improving the LSP accuracy. In conclusion, this study provided a new idea for the negative sample sampling method for LSP using ML models

To verify the applicability of wave theory in studying the free vibration characteristics and impact response of the cable–beam structure, and to preliminarily explore the behaviors of elastic wave propagation through the structure under moving load, the dynamic response function was derived from the transverse vibration differential equation of Timoshenko beam and the longitudinal wave equation of cables. The reverberation-ray matrix was used to obtain the waveform solution of the structural response. Based on the idea of discrete Fourier transform (DFT), the series solution of structural transient response was derived to solve the inverse problem of the traditional reverberation-ray matrix (MRRM). The improved MRRM was verified by experiment and finite element method (FEM). The results showed that at the speed of 30 km/h, the deviation between the theoretical maximum strain and FEM results was 5% and that between theoretical and experimental results was 8%. When the vehicle speed was 40 km/h, the deviation between the theoretical maximum strain and FEM results was 4.0%, which was 9.8% between theoretical and experimental results. Taking the beam-cable system as the research object, the maximum deviation between the first five natural frequencies calculated by improved MRRM and FEM was 0.29%, and the deviation between the first two natural frequencies was 0. The wave response characteristics of the cable-stayed beam under moving load were analyzed and the theoretical results were in good agreement with the FEM results. It could be inferred that the improved MRRM had high reliability in calculating the transient wave response of bridge structure under moving load. Based on the analyses of the frequency domain response, it was found that the flexural waves in the Timoshenko beam under moving load were mainly low-frequency responses whose frequencies were lower than 2 times the fundamental frequency of the structure. Furthermore, the reasonable selection criteria of frequency range were explored in the process of finding the wave response, to further improve the calculation efficiency of MRRM

To scientifically and reasonably analyze the bending vibration frequency of the new type composite box girder bridge with corrugated steel webs, taking into account the shear deformation effect of corrugated steel webs and the shear lag effect of box girder, the Galerkin method and the Hamilton principle were used to deduce the free vibration control differential equation and natural boundary conditions of the bridge type. According to the natural boundary conditions, the calculation formula of the bending vibration frequency of the new type composite box girder bridge with corrugated steel webs under the influence of shear deformation effect and shear lag effect was solved. The results were compared with the measured results and ANSYS finite element results, and the influencing factors of the bending vibration frequency were analyzed. The results showed that the calculation results of the bending vibration frequency were in good agreement with the measured results and the ANSYS finite element results, which verified the correctness of the derived frequency calculation formula. The bending vibration frequency increased with the increase of the high-span ratio. When it was less than 0.05, the increase of the bending vibration frequency was relatively gentle. When the height-span ratio was greater than 0.05, the increase of the bending vibration frequency was more significant. The bending vibration frequency increased with the increase of the width-span ratio, but the overall increase was not obvious. The frequency increased with the increase of the thickness of the corrugated steel web, and the higher the order, the more significant the increase. The shear lag effect of the box girder had little effect on the bending vibration frequency, and the maximum error of the first 5 orders was only 6.73%. The shear deformation of the corrugated steel webs had a great influence on the bending vibration frequency, and the maximum error of the first five orders was as high as 51.18%

There are abundant moraine soil sources distributed in the Qinghai–Tibet Plateau of China, with complex historical causes, changeable material structure, and large differences in mechanical properties. Moraine landslide often occurs under the action of rainfall and ice and snow melting. The debris of landslide enters the gully and mixes with water flow, which is very easy to produce the moraine landslide—debris flow—dammed lake disaster chain, which is the key and difficult point of disaster prevention and reduction in the plateau. In recent years, the Sichuan—Tibet railway project and major hydropower development in Tibet have faced a great threat from the moraine landslide—debris flow—dammed lake disaster chain. It is a major national demand to improve the capacity of disaster prevention, reduction and relief. Moraine landslide—debris flow—dammed lake—dam-breaking flood is a chain disaster process of cascade amplification, which involves complex dynamic evolution mechanisms such as landslide initiation, movement behavior transformation, erosion amplification along the process, multi-stage river blocking superposition, and dam break water–sediment coupling. Especially under complex meteorological conditions, there are some problems such as unclear mechanism, inaccurate model, and lack of simulation technology. It is urgent to carry out the research on the complex dynamic mechanism, control theoretical model, and whole process numerical simulation of moraine landslide—debris flow—dammed lake disaster chain process. Combined with the research status at home and abroad, five research contents are further proposed: 1) Evolution of mechanical properties of moraine soil under complex meteorological conditions. 2) Dynamic disaster process and migration model of moraine landslide—debris flow. 3) Formation mechanism and simulation of moraine landslide—debris flow—dammed lake. 4) Mechanism of erosion and outburst of moraine dam and the process of flow channel expansion. 5) Mechanism of water-sediment interaction in the downstream channel and the simulation of flood evolution. Many preliminary explorations and research work have been carried out to preliminarily reveal the migration of moraine landslide debris flow and multi-stage river blocking mechanism, a dam break evolution model that can consider water erosion and intermittent collapse of the broken slope has been built, and the simulation method of moraine landslide—debris flow—dammed lake disaster chain evolution process has been discussed. The results lay a foundation for further understanding of the complex dynamic mechanism of the moraine landslide—debris flow—dammed lake disaster chain process, constructing the control theoretical model and developing the whole process numerical simulation system. And the results are expected to provide a theoretical basis for the mechanism of moraine landslide—debris flow—dammed lake disaster chain and technical support for non-engineering risk reduction and emergency disposal decision-making

In view of the problem that it is difficult or even impossible to obtain the class labels of new working condition samples under actual variable working conditions, which leads to the low fault diagnosis accuracy, a novel fault diagnosis method based on inter-class repulsive slack discriminant transfer learning (IRSDTL) is proposed. In the proposed IRSDTL, a nonnegative extended slack matrix is constructed to transform the strict binary label matrix into an extended slack label matrix for increasing the distance between different class label vectors in source domain and making the common subspace dimension no longer limited to the number of class labels. As a result, the classification error in source domain is reduced, and the generalization ability of IRSDTL is improved. Moreover, the joint distribution difference is introduced to reduce the difference between auxiliary and target domains, which can better realize the cross-domain transfer learning between two domains; the inter-class repulsive force term is constructed to promote the discriminative learning effect by increasing the distance between one class subdomain samples and the other class subdomains in the two domains. Finally, the whole framework of IRSDTL is optimized by the alternating direction multiplier (ADM) method to easily obtain the optimal parameter values of IRSDTL. The labeled samples under historical working conditions can be used by IRFDTL to perform high-precision class discrimination on the testing samples under new working conditions when there are no class labels of testing samples. Thus, precise fault diagnosis of the testing samples under new working conditions can be achieved by the proposed IRSDTL-based fault diagnosis method. The experimental results of rolling bearing fault diagnosis show that the diagnosis accuracy of the proposed method is higher than that of the other four transfer learning-based methods, and its misdiagnosis rates of misdiagnosing the three types of faults as normal state and the normal state as three types of faults are low, and the effectiveness and practicability of the proposed method are verified