Seismic Response of a Water Transmission Pipeline Across a Fault Zone Adopting a Large-Scale Vibration Table Test

The seismic response is generally amplified significantly near the fault zone due to the influence of discontinuous interfaces and weak-broken geotechnical structures, which imposes a severe geologic hazard risk on the engineering crossing the fault. The Hanjiang to Weihe River Project (phase II) crosses many high seismic intensity regions and intersects with eight large-scale regional active faults. Seismic fortification of the pipelines across the fault zone is significant for the design and construction of the project. A large-scale vibration table test was adopted to investigate the seismic response and fault influences. The responses of accelerations, dynamic stresses, strains, and water pressures were obtained. The results show that the dynamic responses were amplified significantly by the fault zone and the hanging wall. The influence range of fault on acceleration response is approximately four times the fault width. The acceleration amplification ratio in the fault zone generally exceeds 1.35, even reaching 1.8, and the hanging wall amplification ratio is approximately 1.2. The dynamic soil pressure primarily depends on the acceleration distribution and is apparently influenced by pipeline location and model inhomogeneity. The pipeline is bent slightly along the axial direction, accompanied by expansion and shrinkage in the radial direction. The maximum tensile and compressive strains appear at the lower and upper pipeline boundaries near the middle section, respectively. Massive y-direction cracks developed in the soil, accompanied by slight seismic subsidence. The research findings could provide reasonable parameters for the seismic design and construction of the project.

Reproduction Method of Rockfall Geologic Hazards Based on Oblique Photography and Three-Dimensional Discontinuous Deformation Analysis

Rockfall geologic hazards are widely distributed. Due to their concealed nature, rockfalls are difficult to investigate using traditional contact survey methods, and the hazards they pose affect major projects and people’s safety. Reproducing methods, including scene survey and movement process analysis, are primary tasks used to prevent these hazards; however, few reconstruction methods can directly apply the parameters of the rockfall geologic hazards obtained by the scene survey to evaluate the movement process. To address this problem, a method of reproduction based on oblique photography and three-dimensional discontinuous deformation analysis (3D-DDA) is proposed; the method consists of three key techniques (oblique photography, 3D rock block system modeling, and 3D rock block system analysis). First, geometric characteristic parameters of the terrain, rockfall, and discontinuities are extracted based on oblique photography using an unmanned aerial vehicle (UAV). Second, the block system model of rockfall is reconstructed by using 3D computational geometry theory and taking these geometric characteristic parameters as an input. Finally, the whole evolution process of rockfall geologic hazard, including initiation, movement, and accumulation, is simulated by the 3D-DDA method. To verify the practicability of this reproduction method, a typical rockfall geologic hazard, located in the K8 + 050 section of the Gaohai expressway, Yunnan, China, is studied. In addition, the characteristics of 19 dangerous rock masses in the survey area are clarified, and the geometric features of the discontinuities in the rock masses are extracted based on oblique photography using an UAV. The block system model of a potential rockfall is reconstructed, the movement trajectory is simulated by the 3D-DDA method, and the evolution process of velocity and kinetic energy of the rockfall verifies that the spatial layout of the current three-level passive protective nets system is reasonable. The case study indicates that the proposed method provides a geological and mechanical model for the risk assessment of rockfall geologic hazards.

Numerical experimental comparison of mudflow by smoothed particle hydrodynamics (SPH)

Flow-like landslides are a serious geologic hazard that can cause life and property loss all over the world. Mudflow is a kind of debris flow that has been classified as a non-Newtonian flow. The Smoothed particle hydrodynamics method (SPH) is a powerful tool for modeling fluids, such as debris/mudflows, which can be described in terms of local interactions of their constituent parts. In this paper, the Herschel-Buckley rheology model and SPH are used to simulate free-surface mudflow under the gate. The run-out distance and velocity of mudflow during the time are calculated with numerical simulation and compared with the laboratory result. Our results indicate the rate of increase of run-out and viscosity in the computer model is more than the experimental model and it is because of friction that is assumed to be zero. In the computer simulation, friction is exactly zero but in the experimental model, it could be measured and assumed zero. Finally, Abacus had a good result and can be used for mudflow simulation and protection of run-out distance and viscosity.

The Underlying Factors of Soil Susceptibility to Erosion in Central Parts of Southeastern Nigeria

Soil erosion in southeastern Nigeria has a high devastating tendency which created a natural geologic hazard is causing loss of arable farm lands, destroying properties and other social infrastructures like pipelines, roads, bridges, over head and underground cables that are being exposed and or washed away by deep gully erosions. Investigations into the underlying factors of soil susceptibility to soil erosion in southeastern Nigeria led to this work. The study areas are the twenty six Local Government Areas within the centre of the zone which are Anaocha, Orumba North, Aguata, Nnewi South and Orumba South in Anambra State; Umunneochi, Bende, Ohafia, Arochukwu and Isuikwuato in Abia State; Afikpo North, Afikpo South, Ivo, Ohaozara and Onicha in Ebonyi State; Aninri, Oji River, Ezeagu, Udi and Awgu in Enugu State, and Idea to North, Idea to South, Okigwe, Orlu, and Orsu in Imo State. The dataset for this research work are from secondary and primary sources. Secondary Data were extracted from other journal publications among others, while primary data were in the form of measurement during field visit, photographs and geophysical soil survey and verification. Descriptive Statistics, Student t-test and Chi-square test analysis were used. The result shows that the soils across the study area generally are predominantly sandy with a mean of fine sand at 28.22% and coarse sand at 43.40%, while the mean of clay and silt are very low, 17.82% and 10.56% respectively. The study concludes that high sand content in the composition of soil in the study area is responsible for high rate of soil erosion in the area and therefore recommends a policy framework from the government of Nigeria that will encourage a paradigm shift from roots and tubers crop production that exposes the soil, to orchard plantation.

Integration and Fusion of Geologic Hazard Data under Deep Learning and Big Data Analysis Technology

To discuss the analysis and evaluation of highway landslides, the application of data mining methods combined with deep learning frameworks in geologic hazard evaluation and monitoring is explored preliminarily. On the premise of optimizing the processing of landslide images, first, the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) based on the natural statistical characteristics of the spatial domain is introduced, which is initially combined with Super-Resolution Convolutional Neural Network (SRCNN). Then, the AlexNet is fine-tuned and applied to highway landslide monitoring and surveying. Finally, an entropy weight gray clustering evaluation method based on data mining analysis is proposed, and the performances of several methods are verified. The results show that the average score of the BRISQUE algorithm in Image Quality Assessment (IQA) is above 0.9, and the average running time is 0.1523 s. The combination of BRISQUE and SRCNN can improve the image quality significantly. After fine-tuning, the recognition accuracy of AlexNet for landslide images can reach about 80%. The evaluation method based on gray clustering can effectively determine the correlation between soil moisture content and slope angle and thereby be applied to the analysis and evaluation of highway landslides. The results are beneficial to the judgment and assessment of highway landslide conditions, which can be extended to research on other geologic hazards.

Analysis of Radar Technology Identification Model for Potential Geologic Hazard based on Convolutional Neural Network and Big Data

To ensure the proper adoption of new technologies in identifying the potential geologic hazard on tourist routes, convolutional neural network (CNN) technology is applied in the radar image geologic hazard information extraction. A scientific and practical geologic hazard radar identification model is built, which is based on CNN’s image identification and big data algorithm calculation, and it can effectively improve the geologic hazard identification accuracy. By designing experiments, the geologic hazard radar image data are verified, and the practicality of radar image intelligent Identification under CNN and big data technology is also verified. The results show that the images of different resolution sizes all play a significant role in identification of geologic hazard performed by CNN. However, there are differences in the performance of different CNN models. With the continuous increase of training samples, the identification accuracy of various network models is also improved. By means of radar image test, the identification capability of CNN model is the best, the highest precision is 93.61%, and the geologic hazard recall rate is 98.27%. Apriori algorithm is introduced into data processing, and the running speed and efficiency of identification models are improved, with favorable identification effect in variable data sets. This research can provide theoretical ideas and practical value for the development of potential geologic hazard identification on tourist routes.

Monitoring Vehicle Communication and Road Condition in VANET

The connected vehicular ad-hoc network (VANET) and cloud computing technology allows entities in VANET to enjoy the advantageous storage and computing services offered by some cloud service provider. However, the advantages do not come free since their combination brings many new security and privacy requirements for VANET applications. In this article, we investigate the cloud-based road condition monitoring (RCoM) scenario, where the authority needs to monitor real-time road conditions with the help of a cloud server so that it could make sound responses to emergency cases timely. When some bad road condition is detected, e.g., some geologic hazard or accident happens, vehicles on site are able to report such information to a cloud server engaged by the authority. We focus on addressing three key issues in RCoM. First, the vehicles have to be authorized by some roadside unit before generating a road condition report in the domain and uploading it to the cloud server. Second, to guarantee the privacy against the cloud server, the road condition information should be reported in ciphertext format, which requires that the cloud server should be able to distinguish the reported data from different vehicles in ciphertext format for the same place without compromising their confidentiality. Third, the cloud server and authority should be able to validate the report source, i.e., to check whether the road conditions are reported by legitimate vehicles. To address these issues, we present an efficient RCoM scheme, analyze its efficiency theoretically, and demonstrate the practicality through experiments