Artificial Neural Network Optimized by Modified Particle Swarm Optimization for Predicting Peak Particle Velocity Induced by Blasting Operations in Open Pit Mines

Blasting is an indispensable part of the open pit mining operations. It plays a vital role inpreparing the rock mass for subsequent operations, such as loading/unloading, transporting, crushing, anddumping. However, adverse effects, especially blast-induced ground vibrations, are considered one of themost dangerous problems. In this study, artificial intelligence was supposed to predict the intensity ofblast-induced ground vibration, which is represented by the peak particle velocity (PPV). Accordingly, anartificial neural network was designed to predict PPV at the Coc Sau open pit coal mine with 137 blastingevents were collected. Aiming to optimize the ANN model, the modified version of the particle swarmoptimization (MPSO) algorithm was applied to optimize the ANN model for predicting PPV, called theMPSO-ANN model. For the comparison purposes, two forms of empirical equations, namely UnitedStates Bureau of Mining (USBM) and U Langefors - Kihlstrom, were also developed to predict PPV andcompared with the proposed MPSO-ANN model. The results showed that the proposed MPSO-ANNmodel provided a better performance with a mean absolute error (MAE) of 1.217, root-mean-squared error(RMSE) of 1.456, and coefficient of determination (R2) of 0.956. Meanwhile, the empirical models onlyprovided poorer performances with an MAE of 1.830 and 2.012, RMSE of 2.268 and 2.464, and R2 of0.874 and 0.852 for the USBM and U Langefors – Kihlstrom empirical models, respectively.

Empirical Model of Ground-Borne Vibration Induced by Freight Railway Traffic

Developing an empirical model that can predict ground-borne vibration is required in the modelling process using actual data of ground vibration velocity induced by train traffic collected from sites. In the preliminary and mitigation planning stages of the project, the empirical models developed are expected to predict the ground-borne vibration velocity due to rail traffic. The findings of this research are expected to provide a new perspective for railway planners and designers to improve the national design to improve the quality of life for the residents living close to the rail tracks. This research study firmly fills the information gap towards a fundamental understanding of ground-borne vibration in numerous areas of learning regarding the condition of train operation. This study has developed a prediction model of regression to forecast the peak particle velocity of ground-borne vibration from freight trains based on correlated and fixed parameters. The models developed have considered a few parameters obtained from sites using minimal or without tools altogether. Speed of trains and distance of receivers from the sources were the only significant parameters found in this study and used to simplify the empirical model. Type of soil, which is soft soil, and type of train, which is freight train, were the fixed parameters for this study. The data collected were measured along the ground rail tracks involving human-operated freight trains. Residents from the landed residential areas near the railway tracks were chosen as the receivers. Finally, the peak particle velocity models have been successfully developed, and validation analysis was conducted. The model can be used by authorities in the upcoming plan for the new rail routes based on similar fixed parameters with correlated parameters from the study.

Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels

This research examines the feasibility of hybridizing boosted Chi-Squared Automatic Interaction Detection (CHAID) with different kernels of support vector machine (SVM) techniques for the prediction of the peak particle velocity (PPV) induced by quarry blasting. To achieve this objective, a boosting-CHAID technique was applied to a big experimental database comprising six input variables. The technique identified four input parameters (distance from blast-face, stemming length, powder factor, and maximum charge per delay) as the most significant parameters affecting the prediction accuracy and utilized them to propose the SVM models with various kernels. The kernel types used in this study include radial basis function, polynomial, sigmoid, and linear. Several criteria, including mean absolute error (MAE), correlation coefficient (R), and gains, were calculated to evaluate the developed models’ accuracy and applicability. In addition, a simple ranking system was used to evaluate the models’ performance systematically. The performance of the R and MAE index of the radial basis function kernel of SVM in training and testing phases, respectively, confirm the high capability of this SVM kernel in predicting PPV values. This study successfully demonstrates that a combination of boosting-CHAID and SVM models can identify and predict with a high level of accuracy the most effective parameters affecting PPV values.

ANALISIS GROUND VIBRATION DENGAN METODE PEAK PARTICLE VELOCITY (PPV)

<p><em>Measurement of Peak Particle Velocity (PPV) mm / sec in the Sabo dam construction project was carried out using seismic accelerometers. This study is to determine the value of PPV produced by construction equipment and then compared with the BS 6472-2: 2008 standard. The measurement method is carried out based on the applicable rules. PPV measurement results produced by each machine are different. In heavy equipment dump trucks, excavators, and front end loaders show PPV values at distances of 50 m, 100 m, 150 m and 200 m under safe conditions referring to the standard which is still in the range of 0.2 - 0.4 mm / sec. while for the pile driving device, demolition, vibrator pile driver at a distance of 50 meters are in unsafe conditions, because more than the range of 0.2 - 0.4 mm / sec, but at a distance of 100, 150, and 200 m PPV values are at safe condition</em></p><p><em><br /></em></p><p>Key words<strong>: </strong><em>PPV, Ground Vibration, Dam sabo</em></p><p><strong> </strong></p><p><em><br /></em></p>

Indirect Determination Approach of Blast-Induced Ground Vibration Based on a Hybrid SSA-Optimized GP-Based Technique

The accurate determination of blast-induced ground vibration has an important significance in protecting human activities and the surrounding environment. For evaluating the peak particle velocity resulting from the quarry blast, a robust artificial intelligence system combined with the salp swarm algorithm (SSA) and Gaussian process (GP) was proposed, and the SSA was used to find the optimal hyperparameters of the GP here. In this regard, 88 datasets with 9 variables including the ratio of bench height to burden (H/B) and the ratio of spacing to burden (S/B) were selected as the input variables, while peak particle velocity (PPV) was measured. Then, an ANN model, an SVR model, a GP model, an SSA-GP model, and three empirical models were established, and the predictive performance was evaluated by using the root-mean-square error (RMSE), determination coefficient (R2), value account for (VAF), Akaike Information Criterion (AIC), Schwarz Bayesian Criterion (SBC), and the run time. After comparing, it is found that the proposed SSA-GP yielded a superior performance and the ratio of bench height to burden (H/B) was the most sensitive variable.

KAJIAN AMBANG BATAS GETARAN PELEDAKAN DI AREAL DESA SEKITAR TAMBANG BATUBARA DENGAN METODE SCALE DISTANCE ANALYSIS

Getaran tanah merupakan bagian dari output operasi peledakan pada lingkungan. Ketika getaran tanah berada pada level yang tinggi, dapat menyebabkan gangguan pada manusia, ketidaknyamanan dan bahkan menyebabkan pada struktur bangunan di sekitarnya. Mengingat dekatnya jarak dari lokasi peledakan ke daerah pemukiman warga (zona crissis) yang berjarak sekitar ±1000 m. Berdasarkan kondisi lapangan yang terjadi pada bulan Agustus 2019 - Desember 2019 dimana tercatat ground vibration terbesar 3,06 mm/s. Kepmen LH No. 49 Tahun 1996 dan SNI 7571:2010 tentang Baku Tingkat Getaran Kejut menyebutkan batasan kecepatan getaran terhadap lingkungan sekitar yang berpengaruh terhadap keutuhan bangunan. Rekomendasi tersebut sebagai acuan penelitian untuk mengevaluasi nilai getaran tanah yang dihasilkan kegiatan peledakan tambang terbuka. Penulis melakukan evaluasi dari data hasil pengukuran ground vibration aktual berdasarkan pendekatan Teori Peak Particle Velocity yang dihubungkan dengan regresi power untuk memperoleh rumusan prediksi ground vibration, yang kedepannya bisa dijadikan acuan untuk menetukan jumlah isian bahan peledak agar ground vibration yang terjadi tidak melebihi batas aman. Hasil prediksi rumusan ground vibration pada jarak 900 m sampai 1500 m yang diperoleh nilai Peak Particle Velocity ≤ 1,5 mm/s menurut U.S Bureau Of Mines dengan isian bahan peledak maksimum 244,14 kg dimana nilai k = 698.54 dan β = -1.47, menurut Ambraseys-Hendorn dimana nilai k = 5787.19 dan β = -1.609 denagn isian bahan peledak maskimum 207,17 kg , sedangkan menurut Langefors Kihlstrom nilai k dan β yang dieproleh 101.46 dan 1.75 dengan isian bahan peledak maksimum 221.28 kg. Rumusan prediksi ini cukup baik dan dapat digunakan sebagai acuan prediksi getaran tanah agar dampak dari kegiatan peledakan terhadap lingkungan sekitar aman. Kata Kunci: Peledakan, Getaran Tanah, Peak Particle Velocity, Regresi Power