An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine,Iran简

Blast-induced ground vibration is one of the inevitable outcomes of blasting in mining projects and may cause substantial damage to rock mass as well as nearby structures and human beings. In this paper, an attempt has been made to present an application of artificial neural network (ANN) to predict the blast-induced ground vibration of the Gol-E-Gohar (GEG) iron mine, Iran. A four-layer feed-forward back propagation multi-layer perceptron (MLP) was used and trained with Levenberg–Marquardt algorithm. To construct ANN models, the maximum charge per delay, distance from blasting face to monitoring point, stemming and hole depth were taken as inputs, whereas peak particle velocity (PPV) was considered as an output parameter. A database consisting of 69 data sets recorded at strategic and vulnerable locations of GEG iron mine was used to train and test the generalization capability of ANN models. Coefficient of determination (R²) and mean square error (MSE) were chosen as the indicators of the performance of the networks. A network with architecture 4-11-5-1 and R² of 0.957 and MSE of 0.000722 was found to be optimum. To demonstrate the supremacy of ANN approach, the same 69 data sets were used for the prediction of PPV with four common empirical models as well as multiple linear regression (MLR) analysis. The results revealed that the proposed ANN approach performs better than empirical and MLR models.     

An artificial neural network-based mathematical model for the prediction of blast-induced ground vibrations

ABSTRACT

This paper presents an artificial neural network (ANN) based mathematical model for the prediction of blast-induced ground vibrations using the data obtained from the literature. A feed-forward back-propagation multi-layer perceptron (MLP) was adopted, and the Levenberg–Marquardt algorithm was used in training the network. The powder factor, the maximum charge per delay, and distance from blasting face to monitoring point are the input variables. The peak particle velocity (PPV) is the targeted output variable. The model was then formulated using the weights and biases output from the ANN simulation. Multilinear regression (MLR) analysis was also performed using the same number of datasets, as in the case of ANN. The quality of the proposed ANN-based model was also tested with another 14 datasets outside the one used in developing the models and compared with more classical models. The coefficient of the determination (R²) of the proposed ANN-based model was the highest.     

Prediction of blast-induced flyrock in Indian limestone minesusing neural networks

Frequency and scale of the blasting events are increasing to boost limestone production. Mines areapproaching close to inhabited areas due to growing population and limited availability of land resourceswhich has challenged the management to go for safe blasts with special reference to opencast mining.The study aims to predict the distance covered by the flyrock induced by blasting using artificial neuralnetwork （ANN） and multi-variate regression analysis （MVRA） for better assessment. Blast design andgeotechnical parameters, such as linear charge concentration, burden, stemming length, specific charge,unconfined compressive strength （UCS）, and rock quality designation （RQD）, have been selected as inputparameters and flyrock distance used as output parameter. ANN has been trained using 95 datasets ofexperimental blasts conducted in 4 opencast limestone mines in India. Thirty datasets have been used fortesting and validation of trained neural network. Flyrock distances have been predicted by ANN, MVRA,as well as further calculated using motion analysis of flyrock projectiles and compared with the observeddata. Back propagation neural network （BPNN） has been proven to be a superior predictive tool whencompared with MVRA.
2014 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting byElsevier B.V. All rights reserved.     

Prediction of blast-induced ground vibration using artificial neural network

An attempt has been made to evaluate and predict the blast-induced ground vibration and frequency by incorporating rock properties, blast design and explosive parameters using the artificial neural network (ANN) technique. A three-layer, feed-forward back-propagation neural network having 15 hidden neurons, 10 input parameters and two output parameters were trained using 154 experimental and monitored blast records from one of the major producing surface coal mines in India. Twenty new blast data sets were used for the validation and comparison of the peak particle velocity (PPV) and frequency by ANN and other predictors. To develop more confidence in the proposed method, same data sets have also been used for the prediction of PPV by commonly used vibration predictors as well as by multivariate regression analysis (MVRA). Results were compared based on correlation and mean absolute error (MAE) between monitored and predicted values of PPV and frequency.     

Prediction of maximum surface settlement caused by earth pressure balance (EPB) shield tunneling with ANN methods

In order to determine the appropriate model for predicting the maximum surface settlement caused by EPB shield tunneling, three artificial neural network (ANN) methods, back-propagation (BP) neural network, the radial basis function (RBF) neural network, and the general regression neural network (GRNN), were employed and the results were compared. The nonlinear relationship between maximum ground surface settlements and geometry, geological conditions, and shield operation parameters were considered in the ANN models. A total number of 200 data sets obtained from the Changsha metro line 4 project were used to train and validate the ANN models. A modified index that defines the physical significance of the input parameters was proposed to quantify the geological parameters, which improves the prediction accuracy of ANN models. Based on the analysis, the GRNN model was found to outperform the BP and RBF neural networks in terms of accuracy and computational time. Analysis results also indicated that strong correlations were established between the predicted and measured settlements in GRNN model with MAE = 1.10, and RMSE = 1.35, respectively. Error analysis revealed that it is necessary to update datasets during EPB shield tunneling, though the database is huge.     

土石方控制爆破及振动传播规律分析

介绍了某工程大型土石方开挖的爆破参数选择、控制爆破方案和振动速度控制标准制定过程,阐述了爆破振动监测的流程和监测成果。对建筑物不同部位的爆破振动速度进行了对比和分析,提出了各不同部位的振动速度回归公式,并对建筑物顶部的振动放大效应进行了研究。认为建筑物顶部的爆破振动速度大于地面振动速度,存在放大效应,放大系数随比例距离增大而减小,进行控制爆破设计时应考虑放大效应的影响。     

Use of artificial neural network to evaluate the vibration mitigation performance of geofoam-filled trenches

Development activities in a city often generate ground vibration that can cause discomfort to the occupants in nearby buildings, disturbances to the activities undertaken in the buildings and possible damage to nearby structures. This ground vibration is caused by construction activities such as pile driving, ground compaction etc., and road and rail traffic. The use of trenches has been an effective way to mitigate the adverse effects of such ground vibration. The effectiveness of the trench depends on many parameters including the properties of the vibration source, soil medium and trench in-fill material, trench dimensions and the requirements of the receiver. The process of selecting an effective trench for vibration mitigation can therefore become complex due to the influence of a number of parameters and their wide range of values. This paper investigates the use of artificial neural network (ANN) as a smart and efficient tool to predict the effectiveness of geofoam-filled trenches to mitigate ground vibration. Towards this end, a database is developed from an extensive study on the effects of the controlling parameters through numerical simulations with a validated finite element (FE) model. At a certain distance from the vibration source, a geofoam-filled trench is introduced to evaluate the efficiency of vibration mitigation with changes in key parameters such as excitation frequency, amplitude of load, trench configuration (i.e. depth and width), soil shear wave velocity, soil density and damping ratio. These were selected as the input parameters for the ANN while amplitude reduction ratio and peak particle velocity (PPV) were considered as outputs. A multilayer feed forward network was used and trained with the Levenberg-Marquardt algorithm. Neural networks with different configurations were evaluated by comparing coefficient of determination (R²) and mean square error (MSE). The optimum architecture was then used to predict previous results, which revealed the accuracy and the effectiveness of the ANN approach. The findings of this study will provide useful information for vibration mitigation using geofoam-filed trenches.     

基于神经网络的爆破振动速度峰值预报

 以广东岭澳核电站二期工程20 m平台和核岛爆破开挖为实例，运用人工神经网络原理，以孔径、孔深、孔距、排距、最小抵抗线、最大单孔药量、最大段药量、堵塞长度、总药量、高程差和爆源距作为影响爆破振动速度的主要因素，建立BP神经网络模型，对质点爆破振动速度峰值进行预测。分析结果表明，运用提出的神经网络预测模型精确度明显高于传统的萨道夫斯基公式。     

Modified scaled distance regression analysis approach for prediction of blast-induced ground vibration in multi-hole blasting

The blast-induced ground vibration prediction using scaled distance regression analysis is one of the most popular methods employed by engineers for many decades. It uses the maximum charge per delay and distance of monitoring as the major factors for predicting the peak particle velocity (PPV). It is established that the PPV is caused by the maximum charge per delay which varies with the distance of monitoring and site geology. While conducting a production blasting, the waves induced by blasting of different holes interfere destructively with each other, which may result in higher PPV than the predicted value with scaled distance regression analysis. This phenomenon of interference/superimposition of waves is not considered while using scaled distance regression analysis. In this paper, an attempt has been made to compare the predicted values of blast-induced ground vibration using multi-hole trial blasting with single-hole blasting in an opencast coal mine under the same geological condition. Further, the modified prediction equation for the multi-hole trial blasting was obtained using single-hole regression analysis. The error between predicted and actual values of multi-hole blast-induced ground vibration was found to be reduced by 8.5%.     

地铁钻爆法施工对邻近建筑物的振动响应预测

钻爆法施工对邻近建筑物的振动响应预测是制定施工安全措施及预警的重要依据.以青岛地铁4号线某车站32炮次43个监测数据为例,利用实测数据分析了爆破振动速度传播规律,分别选用经验公式与灰色模型预测钻爆法施工对邻近建筑物的振动峰值速度,并分析对比所建模型的精度以及对爆破振动传播规律适应性.结果表明:本次工程中,爆破振动速度传...     

基于前馈网络的岩体爆破效应预测研究

将神经网络理论知识和爆破专业知识有机地结合在一起，提出了一种新的岩体爆破效应预测的前馈网络理论方法。该方法适合于不同的爆破参数和不同的岩体条件，是一种普遍适用的方法，同时也是一种“面向数据”的方法。通过对三峡工程左岸坝区岩体爆破效应预测的研究表明，本文方法与通常的经验公式法、回归分析法以及ＢＰ网络方法相比，具有较高的预报精度     

CPT-Based Liquefaction Evaluation Using Artificial Neural Networks

This article presents various artificial neural network (ANN) models for evaluating liquefaction resistance and potential of sandy soils. Various issues concerning ANN modeling such as data preprocessing, training algorithms, and implementation are discussed. The desired ANN is trained and tested with a large historical database of liquefaction performance at sites where cone penetration test (CPT) measurements are available. The ANN models are found to be effective in predicting liquefaction resistance and potential. The developed ANN models are ported to a spreadsheet for ease of use. A simple procedure for conducting uncertainty analysis to address the issue of parameter and model uncertainties is also presented using the ANN‐based spreadsheet model. This uncertainty analysis is carried out using @Risk, which is an add-in macro that works well with popular spreadsheet programs such as Microsoft Excel and Lotus 1-2-3. The results of the present study show that the developed ANN model has potential as a practical design tool for assessing liquefaction resistance of sandy soils.     

并行小净距隧道楔形掏槽爆破振动效应研究

以重庆鸭江隧道小净距段爆破掘进施工为背景,采用现场振动监测与数值计算相结合的方法研究了并行小净距隧道后续洞上台阶采用楔形掏槽爆破产生的振动效应,分析了掏槽孔与掌子面之间的布置角度对振动速度场的影响。结果表明:先行洞迎爆侧边墙上的最大振动速度出现在后续洞爆破掌子面的侧后方;掏槽孔的布置角度越大则振动强度的极值越大,极值出现的位置也越靠近爆破掌子面。可以通过减小掏槽孔的布置角度来实现降低隧道掘进爆破振动强度的作用,此外在施工现场布置振动监测点时应考虑掏槽孔布置角度的影响,掏槽孔的布置角度越大则振动监测重点区域与爆破掌子面间的距离越小。     

基于爆破振动监测的岩石边坡开挖损伤区预测

 通过对白鹤滩水电站左岸834.0～770.0 m高程坝肩槽边坡爆破开挖振动监测和爆破损伤的声波检测,分析第一～六梯段的振动衰减规律,并通过回归分析建立不同爆心距处质点峰值振动速度与损伤深度的关系,并利用此关系和爆破振动监测结果对第七梯段的损伤深度进行预测。结果表明,当边坡岩性较为均一,且坡体上无较大结构面发育时,在一定距离处边坡预裂爆破振动峰值与保留岩体的损伤深度之间的相关性良好;采用预裂爆破振动衰减规律与保留岩体损伤深度之间的关系预测下一梯段损伤范围的方法简便可行,可大大降低大面积边坡损伤声波检测的工程量。考虑到地质条件、开挖造成的地貌改变等因素,为进一步提高预测精度,需要增加其它因素作为回归要素,或者增加部分关键部位的声波检测,对预测结果进行修正补充。     

Artificial neural networks approach for estimating the groutability of granular soils with cement-based grouts

A reliable estimation of the groutability of the target geomaterial is an essential part of any grouting project. An artificial neural network (ANN) model has been developed for the estimation of groutability of granular soils by cement-based grouts, using a database of 87 laboratory results. The proposed model used the water:cement ratio of the grout, relative density of the soil, grouting pressure, and diameter of the sieves through which 15% of the soil particles and 85% of the grout pass. A very good correlation was obtained between the ANN predictions and the laboratory experiments. Comparison of these results with those obtained using traditional methods for groutability prediction confirmed the viability of using ANN to estimate groutability.     

露天采矿爆破振动对砌体结构破坏效应预测的Fisher判别模型及应用

 应用Fisher判别分析理论并结合工程实际特点,从爆破振动特征参量和砌体结构自身特性这2个方面出发,选取峰值质点振动速度(PPV)、爆破振动主频率、主频率持续时间、灰缝强度、圈梁构造柱、房屋高度、屋盖形式和砖墙面积率8个影响因素作为判别因子,建立爆破振动对砌体结构破坏效应预测的Fisher判别分析模型。将该方法应用到湖北一露天采场爆破振动对砌体结构破坏效应预测问题中,利用现场实测的108组数据进行训练和检验,回判估计的误判率为0.083,通过求解判别函数,认为峰值质点振动速度为最重要的判别指标,其后依次为圈梁构造柱、屋盖形式、砖墙面积率、房屋高度、爆破振动主频率、主频率持续时间和灰缝强度,可以为同类工程在选取爆破振动对砌体结构破坏效应的判别指标方面提供参考。利用其他12组现场数据作为预测样本进行测试,预测结果与实际情况吻合较好。研究表明,该方法回判估计的误判率低,判别性能良好,是爆破振动对砌体结构破坏效应预测的一种有效新方法,可以在实际工程中进行推广应用。     

悬索桥锚碇隧道爆破开挖的围岩累积振动效应研究

 结合矮寨悬索桥锚碇隧道的爆破施工,通过围岩的声波测试得到围岩损伤度和松动圈范围在不同等级的爆破冲击荷载作用下的发育规律,探讨围岩累积损伤程度和振动速度之间的对应关系,以及引起岩体损伤度累计效应的阈值,较为完整地描述了爆破冲击荷载作用下围岩损伤度和松动圈的发展过程。在此基础上得出合理的爆破振动速度的控制指标。结果表明：锚碇隧道的爆破施工不但要控制单次爆破对围岩的扰动,更重要的是,应考虑围岩在频繁的近距离爆破作用下产生的累计振动效应并加以控制。爆破振动速度控制在3～6 cm/s时,最大围岩松动圈厚度约为2.3 m,围岩平均损伤度约为0.15。当测点处围岩的振动速度小于2 cm/s时,围岩的损伤度积累不明显,可视为爆破振动累积损伤阈值。     

Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm

In blasting works, the aim is to provide proper rock fragmentation and to avoid undesirable environmental impacts such as back-break. Therefore, predicting fragmentation and back-break is a significant step in achieving a technically and economically successful outcome. In this paper, considering the robustness of artificial intelligence methods utilized in engineering problems, an artificial neural network (ANN) was applied to predict rock fragmentation and back-break; an artificial bee colony (ABC) algorithm was also utilized to optimize the blasting pattern parameters. In this regard, blasting parameters, including burden, spacing, stemming length, hole length and powder factor, as well as back-break and fragmentation were collected at the Anguran mine in Iran. Root mean square error (RMSE) values equal to 2.76 and 0.53 for rock fragmentation and back-break, respectively, reveal the high reliability of the ANN model. In addition, ABC algorithm results suggest values of 29 cm and 3.25 m for fragmentation and back-break, respectively. For comparison purposes, an empirical model (Kuz-Ram) was performed to predict the mean fragment size in the Anguran mine. A mean fragment size of 33.5 cm shows the ABC algorithm can optimize rock fragmentation with a high degree of accuracy.     

Statistical and neural network assessment of the compression index of clay-bearing soils

The compression index is used to estimate the consolidation settlement of clay-bearing soils. As the determination of compression index from oedometer tests is relatively time-consuming, empirical equations based on index properties can be useful. In this study the performance of widely used single and multi-variable empirical equations was evaluated using a database consisting of 135 test data. New empirical equations were developed utilizing least square regression analysis. In addition, an artificial neural network (ANN) with eight input variables was also developed to estimate the compression index. It was concluded that ANN provides the best results.      

A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility mapping by artificial neural networks

A case study for the use of an artificial neural network (ANN) model for landslide susceptibility mapping in Koyulhisar (Sivas-Turkey) is presented. Digital elevation model (DEM) was first constructed using ArcGIS software. Relevant parameter maps were created, including geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index, stream power index, normalized difference vegetation index and distance from roads. Finally, a landslide susceptibility map was constructed using the neural networks. The drawbacks of the method are discussed but as the validation procedures used confirmed the quality of the map produced, it is recommended the use of ANN may be helpful for planners and engineers in the initial assessment of landslide susceptibility.