Prediction of residual friction angle of clays using artificial neural network

The residual strength of clay is very important to evaluate long term stability of proposed and existing slopes and for remedial measure for failure slopes. Various attempts have been made to correlate the residual friction angle (_r) with index properties of soil. This paper presents a neural network model to predict the residual friction angle based on clay fraction and Atterberg's limits. Different sensitivity analysis was made to find out the important parameters affecting the residual friction angle. Emphasis is placed on the construction of neural interpretation diagram, based on the weights of the developed neural network model, to find out direct or inverse effect of soil properties on the residual shear angle. A prediction model equation is established with the weights of the neural network as the model parameters.     

Extraction of impervious features from spectral indices using artificial neural network

An urban area comprises a complex mix of diverse land cover types and materials. Urban ecology and environment is significantly influenced by the proportion of impervious cover that is increasing considerably with time due to the continuous influx of people into urban areas. Therefore, it is of vital importance to determine the spatiotemporal pattern and magnitude of urbanization. In the present study, we have employed a supervised backpropagation neural network in order to extract the impervious features using five spectral indices, such as one vegetation index—Soil-Adjusted Vegetation Index (SAVI), one water index—Modified Normalized Water Index (MNDWI), and three urban indices—Normalized Difference Built-up Index (NDBI), Built-up Index (BUI), and Index-Based Built-up Index (IBI). The study has been performed using Landsat Thematic Mapper data of November, 2011, of the rapidly urbanizing city of Ranchi, capital of Jharkhand state, India. Using different combinations of these spectral indices while keeping SAVI and MNDWI constant, seven composite images were built, and from each of these composites, impervious features were classified and its accuracy assessed with reference to high-resolution images provided by Microsoft Bing Imagery and adequate ground truthing. It was observed that along with SAVI and MNDWI, whenever IBI was used in any combination, it decreased the classification efficiency. On the other hand, NDBI and BUI, individually or when used together, discriminated the impervious features from the others with high accuracy with the combination of SAVI, MNDWI, and BUI achieving the highest accuracy of 90.14 %.     

Seismic human loss estimation for an earthquake disaster using neural network

In Iran, earthquakes cause enormous damage to the people and economy. If there is a proper estimation of human losses in an earthquake disaster, it could be appropriately responded and its impacts and losses will be decreased. Neural networks can be trained to solve problems involving imprecise and highly complex nonlinear data. Based on the different earthquake scenarios and diverse kind of constructions, it is difficult to estimate the number of injured people. With respect to neural network’s capabilities, this paper describes a back propagation neural network method for modeling and estimating the severity and distribution of human loss as a function of building damage in the earthquake disaster. Bam earthquake data in 2003 were used to train this neural network. The final results demonstrate that this neural network model can reveal much more accurate estimation of fatalities and injuries for different earthquakes in Iran and it can provide the necessary information required to develop realistic mitigation policies, especially in rescue operation.     

A study on precursors leading to geomagnetic storms using artificial neural network

Space weather prediction involves advance forecasting of the magnitude and onset time of major geomagnetic storms on Earth. In this paper, we discuss the development of an artificial neural network-based model to study the precursor leading to intense and moderate geomagnetic storms, following halo coronal mass ejection (CME) and related interplanetary (IP) events. IP inputs were considered within a 5-day time window after the commencement of storm. The artificial neural network (ANN) model training, testing and validation datasets were constructed based on 110 halo CMEs (both full and partial halo and their properties) observed during the ascending phase of the 24th solar cycle between 2009 and 2014. The geomagnetic storm occurrence rate from halo CMEs is estimated at a probability of 79%, by this model.     

Forecasting of groundwater level in hard rock region using artificial neural network

In hardrock terrain where seasonal streams are not perennial source of freshwater, increase in ground water exploitation has already resulted here in declining ground water levels and deteriorating its’ quality. The aquifer system has shown signs of depletion and quality contamination. Thus, to secure water for the future, water resource estimation and management has urgently become the need of the hour. In order to manage groundwater resources, it is vital to have a tool to predict the aquifer response for a given stress (abstraction and recharge). Artificial neural network (ANN) has surfaced as a proven and potential methodology to forecast the groundwater levels. In this paper, Feed-Forward Network based ANN model is used as a method to predict the groundwater levels. The models are trained with the inputs collected from field and then used as prediction tool for various scenarios of stress on aquifer. Such predictions help in developing better strategies for sustainable development of groundwater resources.     

Storm surge prediction using an artificial neural network model and cluster analysis

In this study, an artificial neural network model was developed to predict storm surges in all Korean coastal regions, with a particular focus on regional extension. The cluster neural network model (CL-NN) assessed each cluster using a cluster analysis methodology. Agglomerative clustering was used to determine the optimal clustering of 21 stations, based on a centroid-linkage method of hierarchical clustering. Finally, CL-NN was used to predict storm surges in cluster regions. In order to validate model results, sea levels predicted by the CL-NN model were compared with results using conventional harmonic analysis and the artificial neural network model in each region (NN). The values predicted by the NN and CL-NN models were closer to observed data than values predicted using harmonic analysis. Data such as root mean square error and correlation coefficient varied only slightly between CL-NN and NN model results. These findings demonstrate that cluster analysis and the CL-NN model can be used to predict regional storm surges and may be used to develop a forecast system.     

Optimized height of noise barrier for non-urban highway using artificial neural network

This study applies artificial neural network (ANN) for the determination of optimized height of a highway noise barrier. Field measurements were carried out to collect traffic volume, vehicle speed, noise level, and site geometry data. Barrier height was varied from 2 to 5 m in increments of 0.1 m for each measured data set to generate theoretical data for network design. Barrier attenuation was calculated for each height increment using Federal Highway Administration model. For neural network design purpose, classified traffic volume, corresponding traffic speed, and barrier attenuation data have been taken as input parameters, while barrier height was considered as output. ANNs with different architectures were trained, cross validated, and tested using this theoretical data. Results indicate that ANN can be useful to determine the height of noise barrier accurately, which can effectively achieve the desired noise level reduction, for a given set of traffic volume, vehicular speed, highway geometry, and site conditions.     

Prediction and controlling of flyrock in blasting operation using artificial neural network

Flyrock is one of the most hazardous events in blasting operation of surface mines. There are several empirical methods to predict flyrock. Low performance of such models is due to complexity of flyrock analysis. Existence of various effective parameters and their unknown relationships are the main reasons for inaccuracy of the empirical models. Presently, application of new approaches such as artificial intelligence is highly recommended. In this paper, an attempt has been made to predict and control flyrock in blasting operation of Sangan iron mine, Iran incorporating rock properties and blast design parameters using artificial neural network (ANN) method. A three-layer feedforward back-propagation neural network having 13 hidden neurons with nine input parameters and one output parameter were trained using 192 experimental blast datasets. It was also observed that in ascending order, blastability index, charge per delay, hole diameter, stemming length, powder factor are the most effective parameters on the flyrock. Reducing charge per delay caused significant reduction in the flyrock from 165 to 25 m in the Sangan iron mine.     

Seismic random noise attenuation using artificial neural network and wavelet packet analysis

In this paper, we present a method for attenuating background random noise and enhancing resolution of seismic data, which takes advantage of semi-automatic training of feed forward back propagation (FFBP) artificial neural network (ANN) in a multiscale domain obtained from wavelet packet analysis (WPA). The images of approximations and details of the input seismic sections are calculated and utilized to train neural network to model coherent events by an automatic algorithm. After the modeling of coherent events, the remainder data are assumed to be related to background random noise. The proposed method is applied on both synthetic and real seismic data. The results are compared with that of the adaptive Wiener filter (AWF) in synthetic shot gather and real common midpoint gather and also with that of band-pass filtering on real common offset gather. The comparison indicates substantially higher efficiency of the proposed method in attenuating random noise and enhancing seismic signals.     

Predicting moisture content of soil from thermal properties using artificial neural network

Monitoring of soil moisture contents is an important practice for irrigation water management. The benefit of periodic soil water content data includes improved irrigation scheduling in order to optimize water usage for improved crop productivity. However, the in situ equipment for measuring soil water contents have high maintenance and operation cost and are highly affected by neighboring soil conditions, and some have overwhelming calibration and data interpretation, whereas the common standard laboratory procedure requires much effort and can be time-consuming for large dataset. The objective of this study is to evaluate the applicability of artificial neural network (ANN) to predict moisture content of soil using available or measured thermal properties (thermal conductivity, thermal diffusivity, specific heat, and temperature) of soil. We used both multilayered perception (MLP) and radial basis function (RBF) types of ANN. The study area is a farmland situated within the premises of the University of Ibadan campus. Thermal properties were measured with KD2 Pro at 42 points along seven transects. Soil samples were also collected at these points to determine their moisture contents in the laboratory. ANN analysis carried out effectively predicted the soil moisture content with very low root-mean-square error (RMSE) and high correlation coefficient (R) of approximately 0.9 for the two methods evaluated. The overall results suggest that ANN can be incorporated to predict the moisture content of soil in this area where thermal properties are known.     

Undrained lateral load capacity of piles in clay using artificial neural network

This paper describes the application of the artificial neural network model to predict the lateral load capacity of piles in clay. Three criteria were selected to compare the ANN model with the available empirical models: the best fit line for predicted lateral load capacity (Q_p) and measured lateral load capacity (Q_m), the mean and standard deviation of the ratio Q_p/Q_m and the cumulative probability for Q_p/Q_m. Different sensitivity analysis to identify the most important input parameters is discussed. A neural interpretation diagram is presented showing the effects of input parameters. A model equation is presented based on neural network parameters.     

Evaluation of the compression index of soils using an artificial neural network

The compression index is a one of the important soil parameters that is essential to geotechnical designs. As the determination of the compression index from consolidation tests is relatively time-consuming, empirical formulas based on soil parameters can be useful. Over the decades, a number of empirical formulas have been proposed to relate the compressibility to other soil parameters, such as the natural water content, liquid limit, plasticity index, specific gravity, and others. Each of the existing empirical formulas yields good results for a particular test set, but cannot accurately or reliably predict the compression index from various test sets. In this study, an alternative approach, an artificial neural network (ANN) model, is proposed to estimate the compression index with numerous consolidation test sets. The compression index was modeled as a function of seven variables including the natural water content, liquid limit, plastic index, specific gravity, and soil types. Nine hundred and forty-seven consolidation tests for soils sampled at 67 construction sites in the Republic of Korea were used for the training and testing of the ANN model. The predicted results showed that the neural network could provide a better performance than the empirical formulas.     

用神经网络方法预测复合射孔岩层裂缝扩展深度

以神经网络为基本工具,利用其强大的非线性映射能力,并结合有限元法动力分析成果,为预测在复合射孔条件下岩层裂缝扩展深度提供了一条新的途径。结果表明,用神经网络方法能够充分体现爆燃参数与岩层裂缝扩展深度之间的非线性隐含关系,具有较高的拟合精度,其结果与有限元方法的计算值接近,简单实用。     

Rock mass characterization using photoanalysis

Summary Rock formations are distinguished from each other by measuring first the properties of the intact rock, and second those of the jointing. Whereas simple methods are available for measuring intact rock properties, those available for measuring jointing remain slow, expensive, and sometimes dangerous. Digitized photographs (photoanalysis) may provide a solution. In this paper, the new techniques of photoanalysis are reviewed together with applications, promising areas for research, and also some obstacles that remain to be overcome. Aspects of the rock mass that lend themselves to photoanalytical measurement include those of individual joints, such as persistence, orientation and roughness, and those relating to the mass as a whole, such as block size and the spacing or intensity of jointing. Photoanalysis can also be applied to measurement of blasting. It allows characterization of the rock about to be blasted, helping the engineer to predict fragmentation and to design an appropriate blasting pattern. Afterwards, the same methods can be used to measure fragmentation, overbreak and backbreak, for quality control and for diagnosis of problems.Presented at the 28th US ROck Mechanics Symposium, Tucson, Arizon, 29 June–1 July 1987.     

Performance of artificial neural network and convolutional neural network on slope failure prediction using data from the random finite element method

Acta Geotechnica - The random finite element method has been widely used to evaluate slope uncertainty and reliability. To determine the probability of failure, the safety factor sampling often...     

Textural identification of basaltic rock mass using image processing and neural network

A new approach to identify the texture based on image processing of thin sections of different basalt rock samples is proposed here. This methodology uses RGB or grayscale image of thin section of rock sample as an input and extracts 27 numerical parameters. A multilayer perceptron neural network takes as input these parameters and provides, as output, the estimated class of texture of rock. For this purpose, we have use 300 different thin sections and extract 27 parameters from each one to train the neural network, which identifies the texture of input image according to previously defined classification. To test the methodology, 90 images (30 in each section) from different thin sections of different areas are used. This methodology has shown 92.22% accuracy to automatically identify the textures of basaltic rock using digitized image of thin sections of 140 rock samples. Therefore, present technique is further promising in geosciences and can be used to identify the texture of rock fast and accurate.     

基于进化神经网络混凝土大坝变形预测

根据丰满大坝多年变形观测数据,建立了基于进化神经网络混凝土大坝变形预测方法。经典的BP神经网络的缺陷在于收敛速度慢和泛化能力弱等特性。与普通的多元回归方法和传统的BP神经网络相比,采用遗传算法训练的人工神经网络预测模型预报大坝的变形具有精度高和全局收敛的特点。在丰满大坝工程实际应用表明,所建立的基于进化神经网络混凝土大坝变形预报方法与广泛采用的统计方法相比,可以显著提高大坝变形预报精度。     

基于神经网络的混沌时间序列预测

应用混沌方法对时间序列观测数据进行处理,计算出最大lyapunov指数,得到最大可预报时间尺度。在此基础上,建立人工神经网络预测预报混沌时间序列的模型。结合实例,对该预测方法进行了计算验证。     

Liquefaction resistance evaluation of soils using artificial neural network for Dhaka City,Bangladesh

Natural Hazards - Soil liquefaction resistance evaluation is an important site investigation for seismically active areas. To minimize the loss of life and property, liquefaction hazard analysis is...