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Typhoon debris flows are recurrent phenomena with a high capacity to cause significant economic and life loss in the coastal areas. Accurately predicting the movement process and determining the potential zones and risk assessment are crucial to design mitigation strategies and to reduce societal and economic losses. In this study, the Wangzhuangwu (WZW) gully was chosen as the study object, which once broke out a debris flow induced by the Typhoon Likima on 10 August 2019. First, a detailed field investigation and interpretation of remote sensing imagery were carried out to study the trigger mechanism and quantify the characteristics of the debris flow. Second, the movement and deposition process of the 2019 WZW debris flow were reconstructed based on the Soil Conservation Service-curve number (SCS-CN) approach and a two-dimensional finite model (FLO-2D PRO model). The debris flow inundation and evolutionary trajectory were shown to be reasonably comparable with historical debris flows. Then, the potential hazard zones of debris flows with different recurrence intervals were determined based on the validated rheological parameters. Here we established a two-factors model that couples maximum flow depth with momentum to classify the hazard zones. Finally, we calculated the vulnerability distribution and economic risk of the buildings with different recurrence intervals based on a quantitative risk formula. This study provides a complete and efficient mean to determine the values of debris flow parameters and to implement a hazard and risk assessment based on numerical simulation. This proposed approach efficiently generated a debris flow risk distribution map that can be used for effective disaster prevention in the debris flow-prone areas.

Rockfall hazards frequently occur along national road G318 in west Hubei, China. To better understand the distribution and potential for hazards along road G318, we combined the results of a 3-year engineering geological investigation, statistical modeling, and kinematics-based method in this study to identify high-risk road sections. Two types of rockfall sources were considered in the case of road cut slopes and natural cliff slopes. Rockfall source area cells were preliminarily identified via slope angle threshold (SAT) analysis, which is based on the slope angle distribution (SAD) method, and then improved via the machine learning model, considering rockfall controlling factors. The temporal and size probabilities of source areas were separately calculated via the Poisson distribution and power-law distribution theory. To obtain the runout probabilities and potential influence areas of release sources, rockfall trajectory simulation was performed with the Flow-R tool. In this process, the reach angle was determined through back analysis and then validated against field investigation data. The rockfall hazard probability was finally calculated by integrating the spatial, temporal, size, and runout probabilities of rockfall sources. The results indicated that the potential source area grids decreased from 160,823 to 4,002 units with only a 34.2% loss of historical rockfall samples. The simulation efficiency increased approximately 40 times, which highly reduced the trajectory simulation burden. The rockfall trajectory simulation results matched the field measurements well, with an approximately 96% accuracy. Under 5-, 20-, and 50-year return period scenarios, potential high-risk road sections were found at two size levels (larger than 1 000 m3 and 10 000 m3). This research could help the local government to better understand rockfalls from source area existence and potential risks to roads.

We present a graphics processing unit (GPU)-based real time distributed dynamic strain sensing in optical frequency domain reflectometry (OFDR). GPU can accelerate the data processing rate distributed sensing in OFDR because it contains many parallel processing steps. We analyze parameters and performance of tunable laser source (TLS), data acquisition card (DAQ) and GPU. We present a choice method of the thread number in a block based on the GPU streaming multiprocessor utilization efficiency. We construct several kernel functions based on process steps of strain sensing and discuss the selection of blocks number for each kernel function. In the experiment, we find that total process time by the parallel computing in GPU is enhanced about 81 times compared with the serial computing in CPU. The measurement rate of this system is up to 60 Hz for real time distributed dynamic strain sensing. We achieve a dynamic strain sensing with a peak-to-peak variation of $2.5~\mu \varepsilon $ , a vibration frequency of 20 Hz and a sensing spatial resolution of 20 cm at a sensing range of 200 m. More importantly, two strain areas simultaneously loaded with a distance interval of 20 cm can be distinguished clearly.

The traditional Stability INdex MAPping (SINMAP) model does not perform detailed divisions of study areas and neglects differences caused by the asymmetrical spatial distribution of geotechnical parameters; thus, the accuracy of the evaluation results is insufficient. In this study, the evaluation results of the SINMAP model were improved based on a combination with the certainty factor (CF) model, and the proposed method is referred to as the CF-SINMAP model. The Wuling Mountain area in Cili County of Hunan Province (China) was selected to verify the CF-SINMAP model. First, eight geological environmental factors in the region were analyzed by the CF method, including the slope, distance from fault, slope direction, distance from water, rock and soil type, elevation, distance from road and vegetation coverage. The rock and soil type, vegetation coverage and human engineering activities were determined as the key factors underlying landslide hazards. Then, the study area was divided into six regions based on the key factors, and the physical and mechanical parameters of each region were refined by the natural environment, formation lithology and human activities. Finally, the CF-SINMAP model was used to calculate and analyze the landslide hazard assessment results under different rainfall conditions. The results show that the CF-SINMAP model is more sensitive to rainfall compared with the traditional method and the unstable areas are mainly distributed along river valleys, reservoir banks and areas with continual human engineering activities. The area under the receiver operating characteristic (ROC) curve values was 0.75 and 0.61 for the CF-SINMAP and SINMAP models, respectively. Compared with the traditional SINMAP model, the CF-SINMAP model produces more reliable results. The rainfall threshold that induced the landslide disaster in Cili County, Hunan Province, was 90 mm/d. In summary, the CF-SINMAP model provides new ideas for the prediction of regional rainfall-induced landslides.

A demodulation method of spatial domain based on low-coherence interferometry is proposed. The original signal of spatial domain acquired by linear CCD is filtered with all-phase filter to obtain the fringe pattern, and the phase signal of the spatial frequency domain is retained after filtering. The fringe pattern analysis is employed to avoid phase ambiguity and realize large measurement range in demodulation. Afterwards, using the centroid position of the fringe, the peak position of a single fringe pattern can be obtained, and finally the accurate peak position is calculated with seven-step phase-shifting interferometry. The demodulation method is demonstrated experimentally with Fabry-Perot sensing system. At room temperature, it is proved that the full-scale measurement error is less than 0.019% in the range of 11 kPa to 290 kPa. From -20 °C to 70 °C, the accuracy is stable and the maximum error is less than 0.054 kPa. The demodulation method has the potential to enhance the measurement accuracy and adaptability in actual environment for universal applications.

Machine learning models are regarded as efficient and popular models for natural disaster susceptibility prediction. However, few studies have focussed on the applications of the latest popular machine learning models in collapse susceptibility assessment (CSA). This paper proposes a 3S (RS, GPS and GIS) technology-based support vector machine (SVM) to map collapse susceptibility in the Nantian area of China. The 3S technology-based back-propagation neural network (BPNN) and radial basis function neural network (RBFNN) models are also proposed for comparison. First, 44 recorded collapses are identified through field investigation, and fourteen collapse-related causal factors are acquired using 3S technology. Second, among these recorded collapses and randomly selected ‘non-collapses’, 70% of the collapse and non-collapse grid cells are used to train the three models, while the remaining 30% are used to test the models. Third, the collapse susceptibility maps of the Nantian area are produced using the three models. Finally, the prediction accuracies of these models are evaluated. The results indicate that the SVM model has the highest prediction accuracy, while the RBFNN model has the lowest prediction accuracy for CSA. In addition, the distribution characteristics of collapse susceptibility in the Nantian area are produced well by all three models.

Rockfall hazard is frequent along the national road (G318) in west Hubei, China. To understand the distribution and potential hazard probability, this study combines the result of a 3-years engineering geological investigation, statistical modeling, and kinemics-based method to identify risky road sections. Rockfall hazard probability is calculated by integrating spatial, temporal, size probability, and reaching probabilities of source areas. Rockfall source areas are preliminarily identified first by slope angle threshold (SAT) analysis. Random Forest model (RFM) and multivariate logistic regression model (MLRM) are then applied and compared to get the final susceptible source areas, considering eight factors, including slope, aspect, elevation, lithology, joint density, slope structure, land-use type, distance to the road. Temporal and size probability of source areas are separately obtained by Poisson distribution and power-law distribution theory. An important parameter (reach angle) for rockfall trajectory simulation was determined by back analysis in Flow-R and validated by field investigation. The results show good fitness with the measurements by field investigation. In the conditions of 5, 20, and 50 years return period, potential risky road sections are found out under two size scenarios (larger than 1 000 m3, 10 000 m3). This research helps the local government to completely understand the rock falls from source area existence and potential risk to roads.

In catheter based polarization sensitive optical coherence tomography (PS-OCT), a optical fiber with a rapid rotation in the catheter can cause low signal-to-noise ratio (SNR), polarization state instability, phase change of PS-OCT signals and then heavy noise-induced depolarization, which has a strong impact on the phase retardation measurement of the sample. In this paper, we analyze the noise-induced depolarization and find that the effect of depolarization can be reduced by polar decomposition after incoherent averaging in the Mueller matrix averaging (MMA) method. Namely, MMA can reduce impact of noise on phase retardation mapping. We present a Monte Carlo method based on PS-OCT to numerically describe noise-induced depolarization effect and contrast phase retardation imaging results by MMA and Jones matrix averaging (JMA) methods. The peak signal to noise ratio (PSNR) of simulated images processed by MMA is higher than about 8.9 dB than that processed by JMA. We also implement experiments of multiple biological tissues using the catheter based PS-OCT system. From the simulation and experimental results, we find the polarization contrasts processed by the MMA are better than those by JMA, especially at areas with high depolarization, because the MMA can reduce effect of noise-induced depolarization on the phase retardation measurement.

In general the determination of landslide rainfall threshold is mainly based on the empirical statistics of historical landslide disaster and rainfall. However, which often results in unsound prediction accuracy of regional rainfall-type landslide due to neglecting the difference effect of rainfall on diverse types of landslide disaster. In order to obtain accurate critical threshold of rainfall inducing landslides, based on the influence of rainfall on landslide mechanism and hydrological, in this paper a precise geological model is established and the soil water, ground water level and slope position shift of the landslides are monitored in real-time. By coupling the simulation results with the relationship between rainfall process and slope deformation, the regulation of slope failure induced by rainfall is discussed. The results indicate that a cumulative rainfall of 150 mm can make the landslide fully saturated, and generate the overall landslide instability along the soil-rock interface. Moreover, when the cumulative rainfall reaches 90 millimeter and lasts for more than 3 days, the displacement of bedding rock landslide exceeds 10 cm. This may because of the deterioration of the mechanical properties and the increase of the pore water pressure caused by the rainfall infiltration. The prediction criteria for landslide instability established from mechanism analysis can provide a theoretical basis for accurate prediction of rain-sensitive landslides.

At present, the verification method of hand-held laser range finder has characteristics of high labor intensity, low working efficiency and low measuring accuracy. To solve these problems, an automatic hand-held laser range finder verification system is built in this paper. This system contains a 50m marble calibration platform, a measuring trolley with visual measuring module, and a multi-degree of freedom holder of range finder. It can automatically identify the observed readings of rangefinder through optical character recognition (OCR) technology, compare with the standard ranging value obtained by the trolley, and then evaluate error of indication. Thus achieving the purpose of calibrating the rangefinder. All of the communication between PC and trolley is realized by WiFi. Experiments show this device has comprehensive functions, high level of automation, practical application value and broad market prospects.

Landslide displacement time series can directly reflects landslide deformation and stability characteristics. Hence, forecasting of the non-linear and non-stationary displacement time series is necessary and significant for early warning of landslide failure. Traditionally, conventional machine learning methods are adopted as forecasting models, these forecasting models mainly determine the input and output variables experientially and does not address the non-stationary characteristics of displacement time series. However, it is difficult for these conventional machine learning methods to obtain appropriate input-output variables, to determine appropriate model parameters and to acquire satisfied prediction performance. To deal with these drawbacks, this study proposes the wavelet analysis (WA) to decompose the displacement time series into low- and high-frequency components to address the non-stationary characteristics; then proposes thee chaos theory to obtain appropriate input-output variables of forecasting models, and finally proposes Volterra filter model to construct the forecasting model. The GPS monitoring cumulative displacement time series, recorded on the Shuping and Baijiabao landslides, distance measuring equipment monitoring displacements on the Xintan landslide in Three Gorges Reservoir area of China, are used as test data of the proposed chaotic WA-Volterra model. The chaotic WA-support vector machine (SVM) model and single chaotic Volterra model without WA method, are used as comparisons. The results show that there are chaos characteristics in the GPS monitoring displacement time series, the non-stationary characteristics of landslide displacements are captured well by the WA method, and the model input-output variables are selected suitably using chaos theory. Furthermore, the chaotic WA-Volterra model has higher prediction accuracy than the chaotic WA-SVM and single chaotic Volterra models.

Landslide is a common natural hazard and responsible for extensive damage and losses in mountainous areas. In this study, Longju in the Three Gorges Reservoir area in China was taken as a case study for landslide susceptibility assessment in order to develop effective risk prevention and mitigation strategies. To begin, 202 landslides were identified, including 95 colluvial landslides and 107 rockfalls. Twelve landslide causal factor maps were prepared initially, and the relationship between these factors and each landslide type was analyzed using the information value model. Later, the unimportant factors were selected and eliminated using the information gain ratio technique. The landslide locations were randomly divided into two groups: 70% for training and 30% for verifying. Two machine learning models: the support vector machine (SVM) and artificial neural network (ANN), and a multivariate statistical model: the logistic regression (LR), were applied for landslide susceptibility modeling (LSM) for each type. The LSM index maps, obtained from combining the assessment results of the two landslide types, were classified into five levels. The performance of the LSMs was evaluated using the receiver operating characteristics curve and Friedman test. Results show that the elimination of noise-generating factors and the separated modeling of each landslide type have significantly increased the prediction accuracy. The machine learning models outperformed the multivariate statistical model and SVM model was found ideal for the case study area.

It is important to monitor the displacement time series and to explore the failure mechanism of reservoir landslide for early warning. Traditionally, it is a challenge to monitor the landslide displacements real-timely and automatically. Globe Position System (GPS) is considered as the best real-time monitoring technology, however, the accuracies of the landslide displacements monitored by GPS are not assessed effectively. A web-based GPS system is developed to monitor the landslide displacements real-timely and automatically in this study. And the discrete wavelet transform (DWT) is proposed to assess the accuracy of the GPS monitoring displacements. Wangmiao landslide in Three Gorges Reservoir area in China is used as case study. The results show that the web-based GPS system has advantages of high precision, real-time, remote control and automation for landslide monitoring; the Root Mean Square Errors of the monitoring landslide displacements are less than 5 mm. Meanwhile, the results also show that a rapidly falling reservoir water level can trigger the reactivation of Wangmiao landslide. Heavy rainfall is also an important factor, but not a crucial component.

Displacement predictions are essential to landslide early warning systems establishment. Most existing prediction methods are focused on finding an individual model that provides a better result. However, the limitation of generalization that is inherent in all models makes it difficult for an individual model to predict different cases accurately. In this study, a novel coupled method was proposed, combining the long short-term memory (LSTM) neural networks and support vector regression (SVR) algorithm with optimal weight. The Shengjibao landslide in the Three Gorges Reservoir area was taken as a case study. At first, the moving average method was used to decompose the cumulative displacement into two components: trend and periodic terms. Single-factor models based on LSTM neural networks and SVR algorithms were used to predict the trend terms of displacement, respectively. Multi-factors LSTM and SVR models were used to predict the periodic terms of displacement. Precipitation, reservoir water level, and previous displacement are considered as the candidate factors for inputs in the models. Additionally, ensemble models based on the SVR algorithm are used to predict the optimal weight to combine the results of the LSTM and SVR models. The results show that the LSTM models display better performance than SVR models, the ensemble model with optimal weight outperforms other models. The prediction accuracy can be further improved by also considering results from multiple models.

Landslides in Jurassic red-strata make up a great part of geohazards in the Three Gorges Reservoir (TGR) in China. Most of them begin to move slowly with the accumulated displacement increasing stepwise, which corresponds to seasonal rainfall and 30 m of reservoir water level fluctuation (145 m to 175 m on elevation). We analyzed the movement of 21 slow moving landslides in the Jurassic red-strata in TGR, and found that all these landslides involved two differing processes; one is the sliding process with different shear speeds of soils within the sliding zone (landslide activity), and the other one is in steady state with different durations (dormant state). This means that the soil within the sliding surface may experience shearing at different shear rates and recovery in shear strength during the dormant period. To clarify the mechanism of this kind of movement, we took soil samples from the sliding surface of Xiangshanlu landslide, which occurred on August 30, 2008 in the Jurassic red-strata in TGR, and examined the shear rate dependency and recovery of shear resistance by means of ring shear tests. The results of tests at different shear rates show that the shear strength is positively dependent on the shear rate, and can be recovered within a short consolidation duration after the shearing ceased. By increasing the pore-water pressure (PWP) from the upper layer of the sample, we also examined the initiation of shearing which can simulate the restart of landsliding due to the fluctuation of groundwater level caused by rainfall or changes in reservoir water level. The monitored PWP near the sliding surface revealed that there was a delayed response of PWP near the sliding surface to the applied one. This kind of delayed response in pore-water pressure may provide help for the prediction of landslide occurrence due to rainfall or fluctuation of reservoir water level.

A fiber optical anemometer using dual Fabry-Perot sensors with sealed cavity is proposed for high-speed airflow measurement. The airflow velocity is measured based on principle of differential pressure, and temperature compensation is realized by reference F-P sensor to improve measurement accuracy and environmental adaptability. The location of dual F-P sensors in the airflow field and quadratic functional relation between differential pressure and airflow velocity are obtained by the simulation of turbulence model. F-P sensors in this experiment can be employed to measure pressure from 100kPa to 107kPa and temperature from 5°C to 50°C. The full-scale error of F-P sensors is less than 0.53% by calibration. It is demonstrated experimentally this fiber optical anemometer is qualified for measuring air velocity in the range of 7.9-81m/s with velocity error less than 0.69%. The device has the potential to measure high speed airflow in various applications.

There are many closed limestone mines in Fengshan county, Guangxi autonomous region, Southwest China. The geologic environment in the mining area had been destroyed during the mining period without carrying efficient environment governance and land restoration. In order to mitigate the potential geological hazard risk induced by the closed mines, one representative closed mine is chosen to carry out the research of risk analysis. In the paper, firstly, the mine is divided into different zones according to rock mass structure, and geological environmental problems have been analyzed in each zone. Secondly, the failure mode and risk level are analyzed and determined by comprehensive methods of stereographic projection, rigid limit equilibrium method and numerical simulation (by using FLAC3D and ROCKFALL software). Lastly, risk assessments for the closed mine geological environment are proposed. The analysis shows that the mining face can be divided into eight zones (number from I to VIII), which are mainly composed of intact rock, blocky rock, thick platy rock, karstified rock and rock block accumulation body. The major risk in the area comes from rock collapses and rock falls on the mining face. Most of rock collapse will occur in the zone I and II. Rock fall will occur in the zone I to zone Ⅵ. The highest risk will occur in the zone I, II and VIII.

Geo-environmental treatment projects had not been implemented in many old mines of China. Those mines environment has got increasingly deteriorated. So the government provides specialized policies and funding to solve the environmental problems of those mines. Based on the characteristics of the geological environmental treatment projects of old mines, the paper puts forward the principle and index of the treatment projects design and applies in the design of Fengshan limestone mine, Guangxi. It indicated that: (1)The geological environment problems are complex and diverse, and the treatment projects involve a multi-disciplinary cross which are environmental geology, ecology geology, geotechnical engineering, environmental engineering and landscape works. It needs to consider the follow key indicators: Geo-environment problems, Feasibility, Benefit, and social demand. (2) the limestone mine in FengShan is high 134m, and there had happened a collapse which had 2.1×104 m3, and killed 6 people. The geo-environment problems include rockfall, rock collapse, land and vegetation destruction, and landscape damage. The mine local in the Leye-Fengshan International Geological Park in Guangxi, the requests for travel security and sight-viewing environment are extremely high. Therefore, drainage, anchoring, rockfalls platforms and rock carvings would be implemented for the purpose of transforming the mine into a sight spot in the park.(3) The treatment projects eliminate the geo-hazard and build the world-class carving attractions reflect the benefit of the project management from the environment, economy and society aspects.

Hydraulic conductivity (K) and scale effects in basalt in the dam area of Xiluodu hydroelectric station were investigated by three kinds of field hydraulic tests with different test scale, 2608 water pressure tests in single borehole, 54 water seepage tests in adit and groundwater tracer test. Statistical results show the high heterogeneity of fractured rock and K difference between two neighboring test intervals are often more than two orders of magnitude. However, there is a strong decreasing trend of hydraulic conductivity with the increase of vertical depth. Moreover, these three kinds of hydraulic test results demonstrate that hydraulic conductivity increases with the increase of test scale in heterogeneous basalt and the heterogeneous degree of K decreases with the increase of test scale. K from water seepage test in adit, with the test scale of 1-2 m, is dispersed from 0.00024 m/d to 3.46 m/d. K from water pressure test in single borehole, with the test scale of 4-7 m, is 0.0002-1.04 m/d. K from groundwater tracer test, with the test scale of 70-145 m, is concentrated between 0.46 m/d and 2.1 m/d. High heterogeneity of fractured rock and multi-level of fractures are thought as the major reason resulted in scale effects of hydraulic conductivity.

A nonlinear controller is exploited and analyzed for a generic flexible Hypersonic Vehicle longitudinal dynamics in this paper. A combined High-Order Sliding Mode technique and neural network is adopted for designing a dynamic state-feedback controller, providing stable tracking of velocity and altitude reference trajectories. The stability analysis of the closed-loop system and the convergence of system outputs are verified based on the Lyapunov theorem. Numerical simulation results from the flexible Hypersonic Vehicle model demonstrate the effectiveness of the proposed control scheme.

This work reports a facile and rapid method assisted by microwave irradiation for the synthesis of hemin functionalized reduced graphene oxide (hemin–RGO) materials. Our investigation confirmed that the hemin molecules were covalently grafted to the surface of graphene by the amidation reaction of the NH2 groups on the edges of ethylenediamine functionalized graphene oxide with the COOH groups of hemin. Hemin–RGO exhibits a homogeneous dispersion in water, dimethylformamide, and acetone after more than one month, indicating that hemin can effectively improve the dispersion and solubility of RGO in the solvent. Hemin–RGO was used as a counter electrode in dye-sensitized solar cells and exhibited preferable electrocatalytic activity for I3− to I− reduction compared with RGO.

In order to maintain the normal running of economy in China, anti-counterfeiting detection of paper currency has been an important technology in the coinage company and the bank, but the detection using spectrum for Chinese paper currency anti-counterfeiting has not been applied in China. A real-time detection method, with broad spectrum including ultraviolet and infrared wavelengths, is proposed in this paper, which achieves the purpose of anti-counterfeiting by using anti-fake properties of paper currency's coating surface, through different lights stimulation the full spectrum light irradiation on currency surface, with its reflection spectrum detected by spectrometer. The proposed method has such advantages as high technology, high detection precision and easy to identify, and has been applied to a practical system, which satisfies the real-time requirement.

Based on the unique characteristic, the paper currency numbers can be put into record and the automatic identification equipment for paper currency numbers is supplied to currency circulation market in order to provide convenience for financial sectors to trace the fiduciary circulation socially and provide effective supervision on paper currency. Simultaneously it is favorable for identifying forged notes, blacklisting the forged notes numbers and solving the major social problems, such as armor cash carrier robbery, money laundering. For the purpose of recognizing the paper currency numbers, a recognition algorithm based on neural network is presented in the paper. Number lines in original paper currency images can be draw out through image processing, such as image de-noising, skew correction, segmentation, and image normalization. According to the different characteristics between digits and letters in serial number, two kinds of classifiers are designed. With the characteristics of associative memory, optimization-compute and rapid convergence, the Discrete Hopfield Neural Network (DHNN) is utilized to recognize the letters; with the characteristics of simple structure, quick learning and global optimum, the Radial-Basis Function Neural Network (RBFNN) is adopted to identify the digits. Then the final recognition results are obtained by combining the two kinds of recognition results in regular sequence. Through the simulation tests, it is confirmed by simulation results that the recognition algorithm of combination of two kinds of recognition methods has such advantages as high recognition rate and faster recognition simultaneously, which is worthy of broad application prospect.

Because of the drastic reduction in processing time and energy cost, the microwave radiation applied as an alternative method to synthesize solids has been recently investigated. Yb3+/Er3+-codoped CaF2 (CaF2:Yb3+/Er3+) nanocrystals are fabricated using this approach, and the phenomenon of microwave-enhanced mass transport is observed in this process. The upconversion mechanisms and properties of such nanocrystal have also been studied. Charge compensation and microwave ponderomotive effect are considered as the cause of the specific up-conversion spectrum. This synthesis method and the as-prepared CaF2:Yb3+/Er3+ nanocrystal may be potentially useful for advanced phosphor, imaging, anti-counterfeiting labeling, etc.