Prediction of environmental impacts of quarry blasting operation using fuzzy logic

Fişne, Abdullah; Kuzu, C.; Hudaverdi, Turker

doi:10.1007/s10661-010-1470-z

Cited by 99 publications

(33 citation statements)

References 18 publications

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“…Iphar et al (2008) and Jahed developed the adaptive neuro-fuzzy inference system (ANFIS) for estimating PPV induced by blasting. A fuzzy inference system (FIS) model was proposed by Fisne et al (2011) for evaluation and prediction of 33 PPV values obtained from the Akdaglar quarry, Turkey. Another fuzzy model was employed and suggested for indirect determination of PPV using 6 different controllable input parameters in the study carried out by Ghasemi et al (2013).…”

Section: Introductionmentioning

confidence: 99%

Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction

Faradonbeh

Armaghani

Majid

et al. 2016

Int. J. Environ. Sci. Technol.

149

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Blasting is a widely used technique for rock fragmentation in opencast mines and tunneling projects. Ground vibration is one of the most environmental effects produced by blasting operation. Therefore, the proper prediction of blast-induced ground vibrations is essential to identify safety area of blasting. This paper presents a predictive model based on gene expression programming (GEP) for estimating ground vibration produced by blasting operations conducted in a granite quarry, Malaysia. To achieve this aim, a total number of 102 blasting operations were investigated and relevant blasting parameters were measured. Furthermore, the most influential parameters on ground vibration, i.e., burden-to-spacing ratio, hole depth, stemming, powder factor, maximum charge per delay, and the distance from the blast face were considered and utilized to construct the GEP model. In order to show the capability of GEP model in estimating ground vibration, nonlinear multiple regression (NLMR) technique was also performed using the same datasets. The results demonstrated that the proposed model is able to predict blast-induced ground vibration more accurately than other developed technique. Coefficient of determination values of 0.914 and 0.874 for training and testing datasets of GEP model, respectively show superiority of this model in predicting ground vibration, while these values were obtained as 0.829 and 0.790 for NLMR model.

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Section: Introductionmentioning

confidence: 99%

Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction

Faradonbeh

Armaghani

Majid

et al. 2016

Int. J. Environ. Sci. Technol.

149

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“…The model in [7] predicts the peak particle velocity of ground vibration levels. Such model adopts a FL-based inference scheme and utilizes the parameters of distance from blast face to the vibration monitoring point.…”

Section: Related Workmentioning

confidence: 99%

Predictive intelligence to the edge through approximate collaborative context reasoning

Anagnostopoulos

Kolomvatsos

2017

Appl Intell

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We focus on Internet of Things (IoT) environments where a network of sensing and computing devices are responsible to locally process contextual data, reason and collaboratively infer the appearance of a specific phenomenon (event). Pushing processing and knowledge inference to the edge of the IoT network allows the complexity of the event reasoning process to be distributed into many manageable pieces and to be physically located at the source of the contextual information. This enables a huge amount of rich data streams to be processed in real time that would be prohibitively complex and costly to deliver on a traditional centralized Cloud system. We propose a lightweight, energy-efficient, distributed, adaptive, multiple-context perspective event reasoning model under uncertainty on each IoT device (sensor/actuator). Each device senses and processes context data and infers events based on different local context perspectives: (i) expert knowledge on event representation, (ii) outliers inference, and (iii) deviation from locally predicted context. Such novel approximate reasoning paradigm is achieved through a contextualized, collaborative belief-driven clustering process, where clusters of devices are formed according to their belief on the presence of events. Our distributed and federated intelligence model efficiently identifies any localized abnormality on the contextual data in light of event reasoning through aggregating local degrees of belief, updates, and adjusts its knowledge to contextual data outliers and novelty detection. We provide comprehensive experimental and comparison assessment of our model over real contextual data with other localized and centralized event detection models and show the benefits stemmed from its adoption by achieving up to three orders of magnitude less energy consumption and high quality of inference.Keywords Collaborative event inference · Federated reasoning · Edge predictive intelligence · Optimal stopping theory · Adaptive vector quantization · Type-2 fuzzy logic inference

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“…The proposed FL systems handle the uncertainty related to the distribution of sensors reports and help in the decision making, i.e., simple alerts or more complex reasoning tasks. In [17], the authors report on a model that tries to predict the peak particle velocity of ground vibration levels. The model adopts an FL scheme and utilizes the parameters of distance from blast face to vibration monitoring point.…”

Section: Fuzzy Logic-based Monitoring Systemsmentioning

confidence: 99%

Data Fusion and Type-2 Fuzzy Inference in Contextual Data Stream Monitoring

Kolomvatsos

Anagnostopoulos

Hadjiefthymiades

2017

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-Data stream monitoring provides the basis for building intelligent context-aware applications over contextual data streams. A number of wireless sensors could be spread in a specific area and monitor contextual parameters for identifying phenomena e.g., fire or flood. A back-end system receives measurements and derives decisions for possible abnormalities related to negative effects. We propose a mechanism, which based on multivariate sensors data streams, provides real-time identification of phenomena. The proposed framework performs contextual information fusion over consensus theory for the efficient measurements aggregation while time-series prediction is adopted to result future insights on the aggregated values. The unanimous fused and predicted pieces of context are fed into a Type-2 fuzzy inference system to derive highly accurate identification of events. The Type-2 inference process offers reasoning capabilities under the uncertainty of the phenomena identification. We provide comprehensive experimental evaluation over real contextual data and report on the advantages and disadvantages of the proposed mechanism. Our mechanism is further compared with Type-1 fuzzy inference and other mechanisms to demonstrate its false alarms minimization capability.

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Prediction of environmental impacts of quarry blasting operation using fuzzy logic

Cited by 99 publications

References 18 publications

Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction

Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction

Predictive intelligence to the edge through approximate collaborative context reasoning

Data Fusion and Type-2 Fuzzy Inference in Contextual Data Stream Monitoring

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