Testing and modeling of the mechanical behavior of dolomite in the Wudongde hydropower plant

In order to understand the mechanical behaviours of the surrounding rocks in the underground caverns of the Wudongde hydropower plant, triaxial tests are performed on a type of dolomite. It is revealed that damage induced by crack development is the main factor controlling the nonlinear plastic deformation and failure behaviour of the dolomite in both pre- and post-peak regimes. Based on this understanding, a coupled elastoplastic damage model is developed for capturing the dolomite’s mechanical behaviours. In the model, the effects of plasticity and damage on rocks is described by introducing plastic hardening and damage softening commonly in the plastic yield surface. Which are both derived from a suitable Helmholtz free energy function. The model is used to simulate the triaxial tests. Comparisons between test results and the numerical modelling show that the developed model is capable of describing the macro mechanical behaviours of the Wudongde dolomite.     

Prediction of Seaward Slope Recession in Berm Breakwaters Using M5' Machine Learning Approach

In the design process of berm breakwaters, their front slope recession has an inevitable rule in large number of model tests, and this parameter being studied. This research draws its data from Moghim’s and Shekari’s experiment results. These experiments consist of two different 2D model tests in two wave flumes, in which the berm recession to different sea state and structural parameters have been studied. Irregular waves with a JONSWAP spectrum were used in both test series. A total of 412 test results were used to cover the impact of sea state conditions such as wave height, wave period, storm duration and water depth at the toe of the structure, and structural parameters such as berm elevation from still water level, berm width and stone diameter on berm recession parameters. In this paper, a new set of equations for berm recession is derived using the M5'' model tree as a machine learning approach. A comparison is made between the estimations by the new formula and the formulae recently given by other researchers to show the preference of new M5'' approach.     

Development of A Depth-Integrated Longshore Current Model with Unstructured Grids

A depth-integrated model for simulating wave-induced longshore current was developed with unstructured grids. Effects of surface roller and horizontal mixing under combined waves and currents were incorporated in the numerical model. Recommended values of model coefficients were also proposed based on sensitivity analysis. Field observations and three series of laboratory measurements including two cases conducted on the plane beach and one implemented on the ideal inlet were employed to examine the predictive capability of this model. For the field case and laboratory cases conducted on the plane beach, numerical results were compared favorably with the measured data. For the case with an ideal inlet, simulated circulation pattern is supposed to be reasonable although some deviations between numerical results and measured data still can be detected.     

Laboratory Study for Influence of Clay Content (CC) on Wave-Induced Liquefaction in Marine Sediments

In this artice, the influence of clay content on the wave-induced liquefaction in marine sediments was reported. The one-dimensional (1-D) equipment was setup with a vertical cylinder and 1.8–m–thick clayey sandy deposit and 0.2–m–thick water above the deposit. Unlike the previous experimental study for a single soil layer, this study used sand-kaolin mixtures, sand-illite mixtures, and sand-bentonite mixtures as the experimental samples. A series of experiments with 3,000 wave cycles in each test were conducted under numerous wave and soil conditions, which allowed us to examine the influence of clay content (CC) on wave-induced liquefaction in marine sediments. The experimental results showed that the clayey sandy deposit will become prone to liquefaction with the increase of CC when CC is less than a critical value, which depends on the type of clay. However, when CC is greater than the critical value, liquefaction depth will decrease as CC increases. Furthermore, when the CC value reaches a certain level, liquefaction will not occur. For example, no liquefaction occurs when CC ≥ 33% for both kaolin-sand and illite-sand mixtures and CC ≥ 16.36% for bentonite-sand mixtures.     

Study on Longitudinal Deformation Profile of Rock Mass in a Subsea Tunnel

During tunnel excavation, the deformation of surrounding rock due to the unloading of rock mass will vary with time. However, the measured displacement of surrounding rock is only a part of the actual longitudinal deformation profile. There is a need to analyze the longitudinal deformation profile to identify the deformation state and evaluate the stability of surrounding rock mass. In the present article, the variation of pre-deformation of surrounding rock due to excavation was analyzed, and the release coefficient was obtained from the measured results. For the Qingdao subsea tunnel, the measured crown settlement of surrounding rock was analyzed using the regression analysis method, and the longitudinal deformation profile of rock mass was simulated using the numerical calculation method. Moreover, based on the conditions of the subsea tunnel, a solid-fluid coupling model test was carried out to check the reliability of the numerical calculation results. The results of the model test were consistent with the numerical calculation results.     

Nonparametric Estimation of Undrained Shear Strength for Normally Consolidated Clays

For decades, various well-known empirical correlations have been established and commonly used to estimate the undrained shear strength of normally consolidated clays in geotechnical practice. However, there are still many contradictory cases regarding the validity of these correlations, so their applicability and reliability are questionable. In this study, an extensive geotechnical database for normally consolidated clays has been compiled. Next, a Bayesian nonparametric method is exploited to identify the most critical soil index properties in inferring the undrained shear strength and a new predictive model is proposed. Finally, the performance of the proposed predictive model is assessed and compared with two well-known correlations using an independent testing database. It confirms that the proposed model not only possesses high predictability, but also provides superior performance over other existing empirical correlations.     

基于无机成因的油气勘查概念模型(BRCF)

基于超基性岩蛇纹石化成油理论,根据构造条件和沉积条件,提出了石油勘查概念模型——"BRCF"模型:B代表侵入地壳超基性岩;R代表储油层;C代表盖层;F代表深大断裂。运用该模型分析了波斯湾地区油气田和中国典型油气田油气成藏规律,并预测了中国大陆油气勘查靶区。     

Electrical analogy for modelling thermal regime and moisture distribution in sandy soils

The soil mass is subjected to temperature variation due to several human activities (viz. tanks storing heated fluids, buried cables and pipelines, air-conditioning ducts, disposal of nuclear and thermal power plant wastes etc.), which result in heat-induced migration of the moisture in it. Though several studies have been conducted in the past to investigate the mechanism of heat migration through the soil mass, a methodology for ‘real-time measurement of the variations in temperature, flux and moving moisture front, in tandem, with respect to space' has rarely been attempted. In this context, extensive laboratory investigations were conducted to measure real-time flux and temperature variations in the sandy soils, and the validation of results has been done by employing an equivalent electrical circuit programme, LTspice. Subsequently, a mathematical model PHITMDS (i.e. Prediction of Heat-Induced Temperature and Moisture Distribution in Soil) has been developed and its utility and efficacy, for predicting the depth-wise temperature and heat-induced moisture migration, due to evaporation, in terms of position of moving moisture front in the sandy soil has been critically discussed and demonstrated.     

RS & GIS-based landslide susceptibility mapping of the Balason River basin,Darjeeling Himalaya,using logistic regression (LR) model

The present study deals with the preparation of a landslide susceptibility map of the Balason River basin, Darjeeling Himalaya, using a logistic regression model based on Geographic Information System and Remote Sensing. The landslide inventory map was prepared with a total of 295 landslide locations extracted from various satellite images and intensive field survey. Topographical maps, satellite images, geological, geomorphological, soil, rainfall and seismic data were collected, processed and constructed into a spatial database in a GIS environment. The chosen landslide-conditioning factors were altitude, slope aspect, slope angle, slope curvature, geology, geomorphology, soil, land use/land cover, normalised differential vegetation index, drainage density, lineament number density, distance from lineament, distance to drainage, stream power index, topographic wetted index, rainfall and peak ground acceleration. The produced landslide susceptibility map satisfied the decision rules and ?2 Log likelihood, Cox &; Snell R-Square and Nagelkerke R-Square values proved that all the independent variables were statistically significant. The receiver operating characteristic curve showed that the prediction accuracy of the landslide probability map was 96.10%. The proposed LR method can be used in other hazard/disaster studies and decision-making.     

Calibration of cyclic constitutive models by oscillating functions

Most structures are subjected to more cyclic loads during their life time than static loads. These cyclic action could be a result of either natural or man-made activities and may lead to soil failure. In order to understand the response of the foundation and its interaction with these complex cyclic loadings, various researchers have over the years developed different constitutive models. Although a lot of research is being carried out on these relatively new models, little or no details exist in literature about the model-based identification of the cyclic constitutive parameters which to a large extent govern the quality of the model output. This could be attributed to the difficulties and complexities of the inverse modeling of such complex phenomena. A variety of optimisation strategies are available for the solution of the sum of least-squares problems as usually done in the field of model calibration. However, for the back analysis (calibration) of the soil response to oscillatory load functions, this article gives insight into the model calibration challenges and also puts forward a method for the inverse modeling of cyclic loaded foundation response such that high-quality solutions are obtained with minimum computational effort.