Prediction of blast-induced ground vibration using artificial neural networks

Blasting is still being considered to be one the most important applicable alternatives for conventional tunneling. Ground vibration generated due to blasting is an undesirable phenomenon which is harmful for the nearby habitants and dwellings and should be prevented. In this paper, an attempt has been made to predict blast-induced ground vibration using artificial neural network (ANN) in the Siahbisheh project, Iran. To construct the model maximum charge per delay, distance from blasting face to the monitoring point, stemming and hole depth are taken as input parameters, whereas, peak particle velocity (PPV) is considered as an output parameter. A database consisting of 182 datasets was collected at different strategic and vulnerable locations in and around the project. From the prepared database, 162 datasets were used for the training and testing of the network, whereas 20 randomly selected datasets were used for the validation of the ANN model. A four layer feed-forward back-propagation neural network with topology 4-10-5-1 was found to be optimum. To compare performance of the ANN model with empirical predictors as well as regression analysis, the same database was applied. Superiority of the proposed ANN model over empirical predictors and statistical model was examined by calculating coefficient of determination for predicted and measured PPV. Sensitivity analysis was also performed to get the influence of each parameter on PPV. It was found that distance from blasting face is the most effective and stemming is the least effective parameter on the PPV.     

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.     

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.     

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.     

Flyrock danger zone demarcation in opencast mines: a risk based approach

Flyrock, a rock fragment thrown to an excessive distance, is a random event and an ongoing problem in opencast bench blasting. Existing criteria for a ‘Flyrock Danger Zone’ are rigid, such that blasting may not be permitted where there are structures within about 100 m. A statistical approach to the problem is proposed and new concepts of Factor of Safety, Threat Levels and Flyrock Risk have been introduced in order to elucidate risk classes for different geo-mining conditions. The new criteria allow the mining engineer to work out the confidence level of the blasting practice in terms probabilities and risk. The approach is unique, with the emphasis on the categories of blasting and degree of risk that a blasting engineer can afford without sacrificing production and at the same time controlling the travel distance of the flyrock. The proposed dynamic danger zone gives the engineer flexibility to adjust blasting operations to take account of safety requirements and production demands.     

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%.     

Flyrock in bench blasting: a comprehensive review

Flyrock is unwanted throw of rock fragments during bench blasting in mines and civil constructions. Perfunctory attempts by researchers to predict the flyrock range using mathematical, empirical and ANN based models do not address the issue in totality. Thus, flyrock continues to haunt the blaster. The research on the subject is, thus, still in its infancy. This paper identifies the lacunae, through a comprehensive review of the existing models, and suggests measures for better prediction and understanding of the problem on a holistic plane. One of the main reasons for improper predictions is the lack of data on flyrock in comparison to blast vibrations owing to statutory restrictions, avoidance of reporting and consequent constraints on experimentation. While fragmentation and throw of rock accompanied by subsequent vibration and air overpressure are essential constituents of the blasting, flyrock is not. This probably is one of the main errors in predictive domains. In addition, rock mass properties play a major role in heaving of rock fragments during blasting. Barring density of the rock, other rock mass properties have practically been ignored in all the models. At the end of this paper, for future investigations, a methodology for prediction of flyrock is also given.     

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。     

Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization-based multi-layer perceptron neural network

In mining or construction projects, for exploitation of hard rock with high strength properties, blasting is frequently applied to breaking or moving them using high explosive energy. However, use of explosives may lead to the flyrock phenomenon. Flyrock can damage structures or nearby equipment in the surrounding areas and inflict harm to humans, especially workers in the working sites. Thus, prediction of flyrock is of high importance. In this investigation, examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried out. One hundred and fifty-two blasting events in three open-pit granite mines in Johor, Malaysia, were monitored to collect field data. The collected data include blasting parameters and rock mass properties. Site-specific weathering index (WI), geological strength index (GSI) and rock quality designation (RQD) are rock mass properties. Multi-layer perceptron (MLP), random forest (RF), support vector machine (SVM), and hybrid models including Harris Hawks optimization-based MLP (known as HHO-MLP) and whale optimization algorithm-based MLP (known as WOA-MLP) were developed. The performance of various models was assessed through various performance indices, including a10-index, coefficient of determination (R²), root mean squared error (RMSE), mean absolute percentage error (MAPE), variance accounted for (VAF), and root squared error (RSE). The a10-index values for MLP, RF, SVM, HHO-MLP and WOA-MLP are 0.953, 0.933, 0.937, 0.991 and 0.972, respectively. R² of HHO-MLP is 0.998, which achieved the best performance among all five machine learning (ML) models.     

Predicting the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives using artificial neural network

In this study, an artificial neural networks study was carried out to predict the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives. This study is based on the determination of the variation of core compressive strength, water absorption and unit weight in curtain wall elements. One conventional concrete (vibrated concrete) and six different self-compacting concrete (SCC) mixtures with mineral additives were prepared. SCC mixtures were produced as control concrete (without mineral additives), moreover fly ash and limestone powder were used with two different replacement ratios (15% and 30%) of cement and marble powder was used with 15% replacement ratio of cement. SCC mixtures were compared to conventional concrete according to the variation of compressive strength, water absorption and unit weight. It can be seen from this study, self-compacting concretes consolidated by its own weight homogeneously in the narrow reinforcement construction elements. Experimental results were also obtained by building models according to artificial neural network (ANN) to predict the core compressive strength. ANN model is constructed, trained and tested using these data. The results showed that ANN can be an alternative approach for the predicting the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives.     

Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN

An artificial neural network (ANN) is presented to predict a 28-day compressive strength of a normal and high strength self compacting concrete (SCC) and high performance concrete (HPC) with high volume fly ash. The ANN is trained by the data available in literature on normal volume fly ash because data on SCC with high volume fly ash is not available in sufficient quantity. Further, while predicting the strength of HPC the same data meant for SCC has been used to train in order to economise on computational effort. The compressive strengths of SCC and HPC as well as slump flow of SCC estimated by the proposed neural network are validated by experimental results.     

A comparative study on the application of various artificial neural networks to simultaneous prediction of rock fragmentation and backbreak

In blasting operation, the aim is to achieve proper fragmentation and to avoid undesirable events such as backbreak. Therefore, predicting rock fragmentation and backbreak is very important to arrive at a technically and economically successful outcome. Since many parameters affect the blasting results in a complicated mechanism, employment of robust methods such as artificial neural network may be very useful. In this regard, this paper attends to simultaneous prediction of rock fragmentation and backbreak in the blasting operation of Tehran Cement Company limestone mines in Iran. Back propagation neural network (BPNN) and radial basis function neural network (RBFNN) are adopted for the simulation. Also, regression analysis is performed between independent and dependent variables. For the BPNN modeling, a network with architecture 6-10-2 is found to be optimum whereas for the RBFNN, architecture 6-36-2 with spread factor of 0.79 provides maximum prediction aptitude. Performance comparison of the developed models is fulfilled using value account for (VAF), root mean square error (RMSE), determination coefficient (R²) and maximum relative error (MRE). As such, it is observed that the BPNN model is the most preferable model providing maximum accuracy and minimum error. Also, sensitivity analysis shows that inputs burden and stemming are the most effective parameters on the outputs fragmentation and backbreak, respectively. On the other hand, for both of the outputs, specific charge is the least effective parameter.     

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.     

Boundary shear stress analysis in smooth rectangular channels and ducts using neural networks

An artificial neural network (ANN) model was developed to predict boundary shear force distributions in smooth rectangular channels and ducts. Currently the designers often obtain these values with the help of semi-empirical methods. In this paper, as an alternative to these methods, a neural network model is presented. The model was trained and tested using 94 experimental data obtained from the works of best known researchers in this field. The proposed ANN model was found to be superior to existing methods for most of the data sets studied.     

Probabilistic Neural Network for Reliability Assessment of Oil and Gas Pipelines

A fuzzy artificial neural network (ANN)–based approach is proposed for reliability assessment of oil and gas pipelines. The proposed ANN model is trained with field observation data collected using magnetic flux leakage (MFL) tools to characterize the actual condition of aging pipelines vulnerable to metal loss corrosion. The objective of this paper is to develop a simulation-based probabilistic neural network model to estimate the probability of failure of aging pipelines vulnerable to corrosion. The approach is to transform a simulation-based probabilistic analysis framework to estimate the pipeline reliability into an adaptable connectionist representation, using supervised training to initialize the weights so that the adaptable neural network predicts the probability of failure for oil and gas pipelines. This ANN model uses eight pipe parameters as input variables. The output variable is the probability of failure. The proposed method is generic, and it can be applied to several decision problems related with the maintenance of aging engineering systems.     

Using artificial neural networks for calculation of temperatures in timber under fire loading

Artificial neural networks have been used in recent years as a tool to model properties and behavior of materials in many areas of civil engineering applications. Because of their ability to learn and adapt they can be used to find complex relations between different properties. In the present paper artificial neural networks are used for predicting the temperatures in timber under fire loading. The artificial neural network model has been trained and tested using available numerical results obtained using design methods of Eurocode 5 for the calculation of temperatures in timber under fire loading. A multilayer feed forward network has been used with input data arranged in a format of three input parameters that cover the density of timber, the time of fire exposure and the distance from exposed side and the output parameter being the temperature in timber. The training and testing results in the neural network model have shown that neural networks can accurately calculate the temperature in timber members subjected to fire.     

岩体单孔及群孔齐发爆破爆炸荷载数值分析

 采用广东岭澳核电站现场岩体的力学特性和基岩爆破参数，利用高能炸药的状态方程模拟岩石乳化炸药的爆炸过程，并假定岩石在爆炸产生的高应变率和高压环境下符合率相关的弹塑性本构关系，由此利用显式动力分析方法模拟单孔柱状装药和群孔齐发爆破柱状装药情况下的岩体爆炸应力波的传播过程，分析岩体爆炸粉碎区边界峰值应力的变化情况和衰减特征，并与相关理论公式进行比较，得到单孔和群孔齐发爆破情况下岩体爆炸荷载随装药量的变化规律。研究结果表明，爆源近区岩体爆炸冲击波压力急剧衰减；岩体爆炸峰值应力随装药量增加而增加；拉应力随药量的增长幅度远比压应力小；单孔药量的变化对岩体爆炸荷载的影响大于最大段药量的影响。工程实践中采用多爆孔小药量的爆破方式能有效减小岩体动态响应。     

Optimizing overbreak prediction based on geological parameters comparing multiple regression analysis and artificial neural network

Underground mining becomes more efficient due to the technological advancements of drilling and blasting methods and the developing of highly productive mining methods that facilitate easier access to ore. In the perspective of maximizing productivity in underground mining by drilling and blasting methods, overbreak control is an essential component. The causing factors of overbreak can simply divided as blasting and geological parameters and all of the factors are nonlinearly correlated. In this paper, the blasting design of the tunnel was fixed as the standard blasting pattern and the research focus on effects of geological parameters to the overbreak phenomenon. 49 sets of rock mass rating (RMR) and overbreak data were applied to linear and nonlinear multiple regression analysis (LMRA and NMRA) and artificial neural network (ANN) to predict overbreak as input and output parameters, respectively. The performance of LMRA, NMRA, and optimized ANN models was evaluated by comparing coefficient correlations (R²) and their values are 0.694, 0.704 and 0.945, respectively, which means that the relatively high level of accuracy of the optimized ANN in comparison with LMRA and NMRA. The developed optimum overbreak predicting ANN model is suitable for establishing an overbreak warning and preventing system and it will utilize as a foundation reference for a practical drift blasting reconciliation at mines for operation improvements.     

Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling

Numerous empirical and analytical relations exist between shield tunnel characteristics and surface and subsurface deformation. Also, 2-D and 3-D numerical analyses have been applied to such tunneling problems. Similar but substantially fewer approaches have been developed for earth pressure balance (EPB) tunneling. In the Bangkok MRTA project, data on ground deformation and shield operation were collected. The tunnel sizes are practically identical and the subsurface conditions over long distances are comparable, which allow one to establish relationships between ground characteristics and EPB – operation on the one hand, and surface deformations on the other hand. After using the information to identify which ground- and EPB-characteristic have the greatest influence on ground movements, an approach based on artificial neural networks (ANN) was used to develop predictive relations. Since the method has the ability to map input to output patterns, ANN enable one to map all influencing parameters to surface settlements. Combining the extensive computerized database and the knowledge of what influences the surface settlements, ANN can become a useful predictive method. This paper attempts to evaluate the potential as well as the limitations of ANN for predicting surface settlements caused by EPB shield tunneling and to develop optimal neural network models for this objective.     

Artificial neural network-based response surface methods for reliability analysis of pre-stressed concrete bridges

Two artificial neural network (ANN)-based response surface methods for reliability analysis of pre-stressed concrete bridges are presented. The first method is the traditional ANN-based response surface method, originally introduced by Papadrakakis et al. in 1996 (Papadrakakis, M., Papadopoulos, V. and Lagaro, N., 1996. Structural reliability analysis of elastic-plastic structures using neural network and Monte carlo simulation. Computer Methods in Applied Mechanics and Engineering, 136, 145–163), which is applied here to the reliability analysis of pre-stressed concrete bridges. The second method is an improved ANN-based response surface method developed recently by the authors for the reliability analysis of truss structures, in which the key idea is that the uniform design method (UDM) is adopted to select training data for establishing an ANN model, thereby greatly improving the quality of training datasets for establishing an ANN model and dramatically reducing the required number of training datasets. There are two main objectives of the present work. Firstly, an attempt is made to extensively examine the performance of the traditional ANN-based response surface method since no detailed study has been carried out to investigate the effectiveness of this ANN-based response surface method on the basis of the reliability analysis of complicated structures such as pre-stressed concrete bridges. Secondly, the recently developed ANN-based response surface method is extended to the reliability analysis of pre-stressed concrete bridges. A detailed numerical investigation is carried out to compare the performance of the two methods.