Wyniki wyszukiwania - Biblioteka Nauki

1

Vibration control of a building with ATMD under eartquake excitation

100%

Yagiz N.

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2003

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tom Vol. 8, no 1

117-123

EN

In this paper, the application of an active tuned mass damper (ATMD) is studied for controlling the earthquake induced vibration of buildings using a sliding mode control (SMC). Since the structural system may have uncertainties and/or parameter changes, the sliding mode control has been preferred because of its robust character. Additionally, this control method can easily be applied to non-linear systems. The time history of the base and top floor displacements, control force input and frequency responses of both the uncontrolled and sliding mode of a realistic 9-storey building model controlled by the ATMD are presented at the end of the study. The earthquake ground motion used is obtained using the seismic data of the Marmara Earthquake (Mw = 7.4) in Turkey in August 17th, 1999.

2

Optimal positioning of vibration monitoring instruments and their impact on blast-induced seismic influence results

84%

Stanković S. , Dobrilović M. , Škrlec V.

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tom Vol. 64, no. 3

591--607

EN

The major downside of blasting works is blast vibrations. Extensive research has been done on the subject and many predictors, estimating Peak Particle Velocity (PPV), were published till date. However, they are either site specific or global (unified model regardless of geology) and can give more of a guideline than exact data to use. Moreover, the model itself among other factors highly depends on positioning of vibration monitoring instruments. When fitting of experimental data with best fit curve and 95% confidence line, the equation is valid only for the scaled distance (SD) range used for fitting. Extrapolation outside of this range gives erroneous results. Therefore, using the specific prediction model, to predetermine optimal positioning of vibration monitoring instruments has been verified to be crucial. The results show that vibration monitoring instruments positioned at a predetermined distance from the source of the blast give more reliable data for further calculations than those positioned outside of a calculated range. This paper gives recommendation for vibration monitoring instruments positioning during test blast on any new site, to optimize charge weight per delay for future blasting works without increasing possibility of damaging surrounding structures.

PL

Jedną z głównych niedogodności związanych z pracami strzałowymi są spowodowane przez te prace wibracje. Problem ten był dogłębnie badany, opracowano także wskaźniki pozwalające na oszacowanie maksymalnej prędkości ruchu cząstek (Peak Particle Velocity). Jednakże w większości wskaźniki te są albo globalne (wspólny model niezależny od geologii terenu) lub odnoszące się do specyfiki terenu; dlatego też traktować je należy bardziej jako wytyczne do obliczeń niż dokładne dane. Ponadto, wyniki modelowania uzależnione są, między innymi, od lokalizacji i rozmieszczenia instrumentów do pomiarów i monitorowania drgań oraz wibracji. Przy dopasowaniu danych eksperymentalnych krzywą najlepszego dopasowania i linią obrazującą stopień zaufania na poziomie 95%, okazuje się, że równanie modelu zastosowanie ma jedynie dla skalowanych odległości wykorzystanych w dopasowaniu. Ekstrapolowanie poza ten zakres daje wyniki błędne. Dlatego też przed opracowaniem właściwego modelu prognozowania kwestią kluczową jest zastosowanie wstępnego modelu do określenia optymalnej lokalizacji i rozmieszczenia instrumentów pomiarowych. Wyniki wskazują, że rozmieszczenie aparatury pomiarowej we wcześniej wyznaczonej odległości od źródła wybuchu daje bardziej wiarygodne wyniki będące podstawą do dalszych obliczeń niż w przypadku instrumentów umieszczonych poza wyliczonym zakresem. W pracy tej podkreśla się konieczność właściwego umiejscowienia aparatury pomiarowej w trakcie prac strzałowych w nowym miejscu przed przystąpieniem do właściwych obliczeń optymalnej wagi ładunku wybuchowego oraz czasu zwłoki pomiędzy kolejnym strzałami, tak by nie zwiększać ryzyka uszkodzenia sąsiadujących struktur.

3

Prediction of the blast induced ground vibration in tunnel blasting using ANN, moth fame optimized ANN, and gene expression programming

67%

Lawal A. I. , Kwon S. , Kim G. Y.

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tom Vol. 69, no. 1

161--174

EN

The blast-induced ground vibration (BIGV) is a severe environmental impact of blasting as it can afect the integrity of the structures and cause civil unrest. In this study, the BIGV of Daejeon tunnel was predicted taking into consideration parameters such as hole length, the charge per delay, number of holes, total charge, distance from the measuring station to the blasting point and the rock mass rating as the input parameters, while the peak particle velocity (PPV) was the targeted output parameter. An artifcial neural network (ANN) model was frst simulated. The optimum ANN structure obtained was optimized using a novel moth-fame optimization algorithm (MFO). The gene expression program (GEP) was also used to develop another new model. The proposed models were compared with the multilinear regression (MLR) model and the selected empirical models for the PPV predictions. The performance of the proposed model was evaluated using statistical indices such as adjusted coefcient of determination (adj R2 ), mean square error (MSE), mean absolute error (MAE), and the variance accounted for (VAF). The proposed MFO-ANN outperformed other models with the adj R2 of 0.9702 and 0.9577, VAF of 97.0472 and 95.9832, MSE of 0.0009 and 0.0008, and MAE of 0.0233 and 0.0216 for the respective training and testing phases. The sensitivity analysis was conducted using the weight partitioning method (WPM), and the charge per delay has the highest infuence on the predicted PPV. This study indicates the suitability of the proposed models for the prediction of PPV.

4

A comparison of advanced computational models and experimental techniques in predicting blast induced ground vibration in open pit coal mine

67%

Nguyen H. , Bui X. N. , Moayedi H.

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tom Vol. 67, no. 4

1025--1037

EN

Green mining is an essential requirement for the development of the mining industry. Of the operations in mining technology, blasting is one of the operations that signifcantly affect the environment, especially ground vibration. In this paper, four artificial intelligence (AI) models including artificial neural network (ANN), k-nearest neighbor (KNN), support vector machine (SVM), and classification and regression tree (CART) were developed as the advanced computational models for estimating blast-induced ground vibration in a case study of Vietnam. Some empirical techniques were applied and developed to predict ground vibration and compared with the four AI models as well. For this research, 68 events of blasting were collected; 80% of the whole datasets were used to build the mentioned models, and the rest 20% were used for testing/checking the models’ performances. Mean absolute error (MAE), determination coefficient (R2 ), and root-mean-square error (RMSE) were used as the standards to evaluate the quality of the models in this study. The results indicated that the advanced computational models were much better than empirical techniques in estimating blast-induced ground vibration in the present study. The ANN model (2-6-8-6-1) was introduced as the most superior model for predicting ground vibration with an RMSE of 0.508, R2 of 0.981 and MAE of 0.405 on the testing dataset. The SVM, CART, and KNN models provided poorer performance with an RMSE of 1.192, 2.820, 1.878; R2 of 0.886, 0.618, 0.737; and MAE of 0.659, 1.631, 0.762, respectively.