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1.
The present paper mainly deals with the prediction of blast-induced ground vibration level in Bakhtiary formation at intake of waterway system in Gotvand dam, Iran. For this research the ground vibration components were recorded carefully by means of 3 sets of vibration monitors for 32 blast events during the bench blasting in front of tunnels. Then, the data pairs of scaled distance and particle velocity were analyzed by using the USBM equation. At the end of statistical evaluations, a relationship between peak particle velocity and scaled distance for this site was established with good correlation. Again, other data measurements during tunnel excavation near concrete structures were used to validate the predicted PPV and optimize the blasting patterns to omit the effects of resonance and vibration in USBM (RI-8507) standard. Based on the vibration tests done in Bakhtiary conglomerate, constant dynamic factors of the rock mass related to vibration velocity are 159.07 and 1.077. 相似文献
2.
M. Monjezi M. Ahmadi M. Sheikhan A. Bahrami A.R. Salimi 《Soil Dynamics and Earthquake Engineering》2010
Prediction of vibration is very important in mining operations as well as civil engineering projects. In this paper, multi layer perceptron neural network (MLPNN), radial basis function neural network (RBFNN) and general regression neural network (GRNN) were utilized to predict ground vibration level in a Sarcheshmeh copper mine, Iran. It was observed that the MLPNN gives the best results. For this technique root mean square error and coefficient of correlation were found 0.03 and 0.954, respectively. Sensitivity analysis showed that distance from the blast, number of holes per delay and maximum charge per delay are the most effective parameters in making ground vibration in the blasting operation. 相似文献
3.
Study of blasting vibrations in Sarcheshmeh copper mine 总被引：1，自引：0，他引：1
K. Najm) A. Javaherian) H. B. Amnieh) ) Department of Mining Metallurgy Petroleum Engineering Amirkabir University Tehran I R Iran ) Institute of Geophysics Tehran University Tehran I R Iran 《地震学报(英文版)》2002,15(6):683-690
Introduction In spite of development of mechanized methods of ground excavation, drilling and blasting is still extensively employed because of its low capital investment and simplicity. Its extensive use is not even limited by extension of mines close to residential areas and vital establishments. If it is not used in a controlled way, blasting operation can cause instability, failure of mine slopes and damps and damage to the nearby structures. The main objective here could be to reduce the … 相似文献
4.
Current codes of practice in assessing the blast ground motion effect on structures are mainly based on the ground peak particle velocity (PPV) or PPV and the principal frequency (PF) of the ground motion. PPV and PF of ground motion from underground explosions are usually estimated by empirical formulae derived from field blast tests. Not many empirical formulae for PF, but many empirical formulae for PPV are available in the literature. They were obtained from recorded data either on ground surface or in the free field (inside the geological medium). Owing to the effect of surface reflection, blast motions on ground surface and in the free field are very different. But not many publications in the open literature discuss the differences of blast motions on ground surface and in the free field. Moreover, very few publications discuss the blast ground motion spatial variation characteristics. As ground motion directly affects structural responses, it is very important to study its characteristics in order to more reliably assess its effects on structures. In this paper, a validated numerical model is used to simulate stress wave at a granite site owing to explosion in an underground chamber. Using the simulated stress wave, the relations such as PPV and PF attenuation as well as spatial variation of motions on ground surface and in the free field are derived. Discussions on the differences of the characteristics of surface and free field motions are made. Results presented in this paper can be used in a more detailed assessment of ground motion effect on structures. 相似文献
5.
Ali Kahriman 《Soil Dynamics and Earthquake Engineering》2004,24(11):887-892
The aim of this study is to predict peak particle velocity level at a limestone quarry located in Istanbul, Turkey. The ground vibration components were measured for 73 blast events during the bench blast optimization studies during a long period. In blasting operations; ANFO (blasting agent), gelatine dynamite (priming) and NONEL detonators (firing) were used as explosives at this site. Parameters of scaled distance (charge quantity per delay and the distance between the source and the station) were recorded carefully and the ground vibration components were measured by means of vibration monitors for every event. Then, the data pairs of scaled distance and particle velocity were analyzed. The equation of scaled distance extensively used in the literature was taken into consideration for the prediction of peak particle velocity. At the end of statistical evaluations, an empirical relationship with good correlation was established between peak particle velocity and scale distance for this site. The established relationship and the results of the study are presented. 相似文献
6.
Feed forward neural network and interpolation function models to predict the soil and subsurface sediments distribution in Bam,Iran 总被引：1，自引：1，他引：0
Khalil Rezaei Bernard Guest Anke Friedrich Farajollah Fayazi Muhammad Nakhaei Ali Beitollahi Seyed Mahmoud Fatemi Aghda 《Acta Geophysica》2009,57(2):271-293
An application of the artificial neural network (ANN) approach for predicting mean grain size using electric resistivity data
from Bam city is presented. A feed forward back propagation network was developed employing 45 sets of input data. The input
variables in the ANN model are the electrical resistivity, water table as a Boolean value and depth; the output is the mean
grain size. To demonstrate the authenticity of this approach, the network predictions are compared with those from interpolation
methods and the same data. This comparison shows that the ANN approach performs better results. The predicted and observed
mean grain size values were compared and show high correlation coefficients. The ANN approach maps show a high degree of correlation
with well data based grain size maps and can therefore be used conservatively to better understand the influence of input
parameters on sedimentological predictions. 相似文献
7.
Elaine M. FitzGerald Christopher J. Bean & Ronan Reilly 《Geophysical Prospecting》1999,47(6):1031-1044
Borehole-wall imaging is currently the most reliable means of mapping discontinuities within boreholes. As these imaging techniques are expensive and thus not always included in a logging run, a method of predicting fracture frequency directly from traditional logging tool responses would be very useful and cost effective. Artificial neural networks (ANNs) show great potential in this area. ANNs are computational systems that attempt to mimic natural biological neural networks. They have the ability to recognize patterns and develop their own generalizations about a given data set. Neural networks are trained on data sets for which the solution is known and tested on data not previously seen in order to validate the network result. We show that artificial neural networks, due to their pattern recognition capabilities, are able to assess the signal strength of fracture-related heterogeneity in a borehole log and thus fracture frequency within a borehole. A combination of wireline logs (neutron porosity, bulk density, P-sonic, S-sonic, deep resistivity and shallow resistivity) were used as input parameters to the ANN. Fracture frequency calculated from borehole televiewer data was used as the single output parameter. The ANN was trained using a back-propagation algorithm with a momentum learning function. In addition to fracture frequency within a single borehole, an ANN trained on a subset of boreholes in an area could be used for prediction over the entire set of boreholes, thus allowing the lateral correlation of fracture zones. 相似文献
8.
Observations on the application of artificial neural network to predicting ground motion measures 
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下载免费PDF全文 Application of the artificial neural network (ANN) to predict pseudospectral acceleration or peak ground acceleration is explored in the study. The training of ANN model is carried out using feed-forward backpropagation method and about 600 records from 39 California earthquakes. The statistics of the residuals or modeling error for the trained ANN-based models are almost the same as those for the parametric ground motion prediction equations, derived through regression analysis; the residual or modeling error can be modeled as a normal variate. The similarity and differences between the predictions by these two approaches are shown. The trained ANN-based models, however, are not robust because the models with almost identical mean square errors do not always lead to the same predictions. This undesirable behaviour for predicting the ground motion measures has not been shown or discussed in the literature; the presented results, at least, serve to raise questions and caution on this problem. A practical approach to ameliorate this problem, perhaps, is to consider several trained ANN models, and to take the average of the predicted values from the trained ANN models as the predicted ground motion measure. 相似文献
9.
Blasting induced vibration is one of the fundamental problems in the open-pit mines and intense vibration can cause critical damage to structures and plants nearby the open-pit mines, especially to the final pit wall's stability. It is very important to study how to control vibration induced by blasting in the mitigation of negative effects of blasting in open-pit mines. This study aims to examine the propagation of blasting induced ground vibrations and find the feasible approaches to reduce the harmful effects of vibrations induced by blasting on the final pit wall's stability. For this purpose, a series of field experiments were conducted in XinQiao Mining Co. Ltd. Sixty-six events and the blasting parameters of these shots were carefully recorded. During the statistical analysis of the collected data, the predictor equation proposed by the United States Bureau of Mines (USBM) was used to establish a relationship between the Peak Particle Velocity (PPV) and the Scaled Distance (SD) factor. The relationship between PPV and SD was determined and proposed to be used in this open-pit mine. Control of maximum charge amount per delay and the selection optimum interval time to reduce the intensity of vibration by waveform interference were applied in practice. Based on the field experiments, we can determine the maximum charge amount per delay and 15 ms delay were proposed to be used in this site, and a decrease in vibration of 24.5% was obtained. 相似文献
10.
Feed forward Artificial Neural Network model to predict the average summer-monsoon rainfall in India
Surajit Chattopadhyay 《Acta Geophysica》2007,55(3):369-382
In the present research, possibility of predicting average summer-monsoon rainfall over India has been analyzed through Artificial
Neural Network model. In formulating the ANN — based predictive model, three-layer network has been constructed with sigmoid
non-linearity. The monthly summer monsoon rainfall totals, tropical rainfall indices and sea surface temperature anomalies
have been considered as predictors while generating the input matrix for the ANN. The data pertaining to the years 1950–1995
have been explored to develop the predictive model. Finally, the prediction performance of neural net has been compared with
persistence forecast and Multiple Linear Regression forecast and the supremacy of the ANN has been established over the other
processes. 相似文献
11.
Studies of structural responses and damage to high-frequency blast motion are very limited. Current practice uses some empirical allowable ground vibration limits in assessing structural performance. These empirical limits overlook the physical parameters that govern structural response and damage, such as the ground motion characteristics and inherent structural properties. This paper studies the response of RC frame structures to numerically simulated underground blast-induced ground motions. The structural response and damage characteristics of frame structures to ground motions of different frequencies are investigated first. The effects of blast ground motion spatial variations and soil–structure interaction on structural responses are also studied. A suitable discrete model that gives accurate response prediction is determined. A damage index defined based on the accumulated plastic hinge rotation is used to predict structural damage level. Numerical results indicated that both the low structural vibration modes (global modes) and the first elemental vibration mode (local) might govern the dynamic structural responses depending on the ground motion frequency and structural response parameters under consideration. Both ground motion spatial variations and soil–structure interaction effects are prominent. Neglecting them might yield inaccurate structural response prediction. The overall structural response and damage are highly ground motion frequency dependent. Numerical results of structural damage are also compared with some test results obtained in a previous study and with code specifications. Discussions on the adequacy of the code allowable ground vibration limits on RC frame structures are also made. 相似文献
12.
13.
A model of rainfall–runoff relationships is an essential tool in the process of evaluation of water resources projects. In this paper, we applied an artificial neural network (ANN) based model for flow prediction using the data for a catchment in a semi‐arid region in Morocco. Use of this method for non‐linear modelling has been demonstrated in several scientific fields such as biology, geology, chemistry and physics. The performance of the developed neural network‐based model was compared against multiple linear regression‐based model using the same observed data. It was found that the neural network model consistently gives superior predictions. Based on the results of this study, artificial neural network modelling appears to be a promising technique for the prediction of flow for catchments in semi‐arid regions. Accordingly, the neural network method can be applied to various hydrological systems where other models may be inappropriate. Copyright © 2004 John Wiley & Sons, Ltd. 相似文献
14.
Combined open channel flow is encountered in many hydraulic engineering structures and processes, such as irrigation ditches and wastewater treatment facilities. Extensive experimental studies have conducted to investigate combined flow characteristics. Nevertheless, there is no simple relationship that can fully describe the velocity profiles in a turbulent flow. The artificial neural network (ANN) has great computational capability for solving various complex problems, such as function approximation. The main objective of this study is to evaluate the applicability of the ANN for simulating velocity profiles, velocity contours and estimating the discharges accordingly. The velocity profiles measured by an acoustic doppler velocimeter in the open channel of the Chihtan purification plant, Taipei, with different discharges at fixed measuring section and different depths are presented. The total number of data sets is 640 and the data sets are split into two subsets, i.e. training and validation sets. The backpropagation algorithm is used to construct the neural network. The results demonstrate that the velocity profiles can be modelled by the ANN, and the ANN constructed can nicely fit the velocity profiles and can precisely predict the discharges for the conditions investigated. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献
15.
Accurate simulation and prediction of the dynamic behaviour of a river discharge over any time interval is essential for good watershed management. It is difficult to capture the high‐frequency characteristics of a river discharge using traditional time series linear and nonlinear model approaches. Therefore, this study developed a wavelet‐neural network (WNN) hybrid modelling approach for the predication of river discharge using monthly time series data. A discrete wavelet multiresolution method was employed to decompose the time series data of river discharge into sub‐series with low (approximation) and high (details) frequency, and these sub‐series were then used as input data for the artificial neural network (ANN). WNN models with different wavelet decomposition levels were employed to predict river discharge 48 months ahead of time. Comparison of results from the WNN models with those of the ANN models alone indicated that WNN models performed a more accurate prediction. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
16.
工程爆破场地地震动强度预测研究 总被引：3，自引：0，他引：3
本文介绍了爆破地震动强度的预测方法和该领域研究的国内外现状,以岭澳核电站场地为例,利用该场地土石方工程爆破时实监测获得的大量实验数据进行统计回归分析,提出了一种估计(折算)爆破药量的方法,给出预测爆破地震动加速度、速度、的经验公式。按此地震动衰减规律并结合时实监测数据指导爆破,确保大亚湾核电站的安全运行。这种折算爆破药量的方法和预测爆破地震动强度的经验公式对指导工程爆破具有重要参考价值。 相似文献
17.
3D inversion of DC data using artificial neural networks 总被引：2，自引：0，他引：2
Ahmad Neyamadpour W. A. T. Wan Abdullah Samsudin Taib Danesh Niamadpour 《Studia Geophysica et Geodaetica》2010,54(3):465-485
In this paper, we investigate the applicability of artificial neural networks in inverting three-dimensional DC resistivity
imaging data. The model used to produce synthetic data for training the artificial neural network (ANN) system was a homogeneous
medium of resistivity 100 Ωm with an embedded anomalous body of resistivity 1000 Ωm. The different sizes for anomalous body
were selected and their location was changed to different positions within the homogeneous model mesh elements. The 3D data
set was generated using a finite element forward modeling code through standard 3D modeling software. We investigated different
learning paradigms in the training process of the neural network. Resilient propagation was more efficient than any other
paradigm. We studied the effect of the data type used on neural network inversion and found that the use of location and the
apparent resistivity of data points as the input and corresponding true resistivity as the output of networks produces satisfactory
results. We also investigated the effect of the training data pool volume on the inversion properties. We created several
synthetic data sets to study the interpolation and extrapolation properties of the ANN. The range of 100–1000 Ωm was divided
into six resistivity values as the background resistivity and different resistivity values were also used for the anomalous
body. Results from numerous neural network tests indicate that the neural network possesses sufficient interpolation and extrapolation
abilities with the selected volume of training data. The trained network was also applied on a real field dataset, collected
by a pole-pole array using a square grid (8 ×8) with a 2-m electrode spacing. The inversion results demonstrate that the trained
network was able to invert three-dimensional electrical resistivity imaging data. The interpreted results of neural network
also agree with the known information about the investigation area. 相似文献
18.
Groundwater level (GWL) varies periodically or non-periodically with various factors including precipitation, river stage (RS) change, sea level, and dewatering activities. In this study, the effect of influence components on the prediction of GWL using an artificial neural network (ANN) was investigated. Six regions with different hydrologic and geologic conditions were collected and adopted in the investigation using various input combinations. In urban areas with a high surface paved ratio, GWL was mainly affected by RS. In rural areas, the permeability of ground showed a significant impact on GWL. For such cases, the moving average (MA) was a suitable component as it could reflect both time lag and the effect of preceding precipitation. It was shown that site-specific influence component should be firstly identified and introduced into input for more enhanced and reliable prediction of GWL using ANN. The effect of learning data length (LDL) was less significant. In urban and rural areas, the introduction of RS and MA into ANN input significantly improved the prediction performance, respectively, which was consistent with the correlation analysis of GWL influence components. 相似文献
19.
Debris flows have caused enormous losses of property and human life in Taiwan during the last two decades. An efficient and reliable method for predicting the occurrence of debris flows is required. The major goal of this study is to explore the impact of the Chi‐Chi earthquake on the occurrence of debris flows by applying the artificial neural network (ANN) that takes both hydrological and geomorphologic influences into account. The Chen‐Yu‐Lan River watershed, which is located in central Taiwan, is chosen for evaluating the critical rainfall triggering debris flows. A total of 1151 data sets were collected for calibrating model parameters with two training strategies. Significant differences before and after the earthquake have been found: (1) The size of landslide area is proportioned to the occurrence of debris flows; (2) the amount of critical rainfall required for triggering debris flows has reduced significantly, about half of the original critical rainfall in the study case; and (3) the frequency of the occurrence of debris flows is largely increased. The overall accuracy of model prediction in testing phase has reached 96·5%; moreover, the accuracy of occurrence prediction is largely increased from 24 to 80% as the network trained with data from before the Chi‐Chi earthquake sets and with data from the lumped before and after the earthquake sets. The results demonstrated that the ANN is capable of learning the complex mechanism of debris flows and producing satisfactory predictions. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
20.
A general approach for porosity estimation using artificial neural network method: a case study from Kansas gas field 总被引：1，自引：0，他引：1
This study aims to design a back-propagation artificial neural network (BP-ANN) to estimate the reliable porosity values from the well log data taken from Kansas gas field in the USA. In order to estimate the porosity, a neural network approach is applied, which uses as input sonic, density and resistivity log data, which are known to affect the porosity. This network easily sets up a relationship between the input data and the output parameters without having prior knowledge of petrophysical properties, such as porefluid type or matrix material type. The results obtained from the empirical relationship are compared with those from the neural network and a good correlation is observed. Thus, the ANN technique could be used to predict the porosity from other well log data. 相似文献