Sensitivity indices of a reinforced concrete beam exposed to explosions

Nariman, Nazim Abdul

doi:10.1504/ijhm.2022.122457

Cited by 5 publications

(2 citation statements)

References 18 publications

(22 reference statements)

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“…Therefore, coal ash accidents in mines are the great enemy of coal mine production and such accidents must be eliminated. Shi et al proposed that one of the important means to prevent dust explosion and reduce the risk of explosion is to master the explosive thermodynamic parameters and explosion mechanism model of such dust through theoretical analysis or experimental studies of explosion strength, maximum explosion pressure, and explosion [16]. Li et al used fuzzy mathematical comprehensive evaluation technique to analyze and improve the coal ash blast criterion, and then the coal ash blast was analyzed and improved [17].…”

Section: Related Workmentioning

confidence: 99%

A Support Vector Machine Based Prediction on Sensitivity to Coal Ash Blast for Different Degrees of Deterioration

et al. 2022

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Coal ash blast is a potential hazard that causes serious disasters in coal mines. In explosion control, research work on coal ash sensitivity prediction is of practical importance to improve accuracy, reduce blindness of explosion protection measures, and strengthen targets. The potential and destructive characteristics of coal ash blast vary greatly from coal to coal, especially in coal mines with complex and changing environments, where the characteristics of coal ash blast show great variability under the influence of various factors. In addition, due to the lack of systematic and comprehensive understanding of the occurrence mechanism of coal ash blast, it is necessary to conduct systematic research on the occurrence mechanism of coal ash blast. Current coal ash blast sensitivity summarizes and concludes prediction methods to create reliable predictions for coal ash blast. A new general learning method, support vector machine (SVM), has been developed, which provides a unified framework for solving limited sample training problems and can better solve small sample training problems. With the purpose of determining the coal mine problem and coal ash sensitivity prediction sensitivity indicators and thresholds, the SVM method is used to set the sensitivity function of each prediction indicator, and the sensitivity of each prediction indicator for the proposed study mine is expressed quantitatively. The experimental results show that the prediction accuracy of SVM for positive and negative categories is 15.6% higher than that of BP neural network and 35.1% higher than that of Apriori algorithm. Therefore, the prediction effectiveness of the SVM algorithm is proved. Therefore, it is practical to adopt SVM method for prediction on sensitivity to coal ash blast and apply the latest statistical learning theory SVM to predict the risk of coal ash.

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Section: Related Workmentioning

confidence: 99%

A Support Vector Machine Based Prediction on Sensitivity to Coal Ash Blast for Different Degrees of Deterioration

et al. 2022

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“…Truck-mounted concrete pumps play a crucial role in construction work, aiming to complete concrete placing tasks efficiently, quickly, and accurately [1]. To achieve this, the hydraulic-controlled boom system must effectively deploy and arrange the conveying pipe.…”

Section: Introductionmentioning

confidence: 99%

Research on Dynamic Performance of Independent Metering Valves Controlling Concrete-Placing Booms Based on Fuzzy-LADRC Controller

Bao

Zhang

et al. 2023

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Motion control of truck-mounted concrete pump booms is an important part of concrete construction work. The quality of the boom movement will affect the efficiency and safety of construction operations. To make the boom move quickly and steadily, its hydraulic control system needs to be improved. In this article, we firstly combine independent metering control (IMC) and active disturbance rejection control (ADRC). The hydraulic system that controls the boom is meliorated by replacing the traditional proportional directional spool valve with four cartridge flow control valves to realize IMC. Meanwhile, a fuzzy linear ADRC algorithm is proposed for a new IMC hydraulic system where ADRC is linearized and combined with fuzzy logic to achieve better motion control performance of boom sections. According to the results of experiments conducted using all six boom sections, compared with the PID control algorithm, the fuzzy linear active disturbance rejection control (FLADRC) proposed in this article can improve the stability and rapidity of valves controlled by hydraulic cylinders by 10.0% to 66.7% in different working conditions.

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Crevice corrosion of reinforcing steel in carbonated simulated concrete pore solutions contaminated by chloride

Wu,

Gong,

Zhang

et al. 2024

J. Iron Steel Res. Int.

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Sensitivity indices of a reinforced concrete beam exposed to explosions

Cited by 5 publications

References 18 publications

A Support Vector Machine Based Prediction on Sensitivity to Coal Ash Blast for Different Degrees of Deterioration

A Support Vector Machine Based Prediction on Sensitivity to Coal Ash Blast for Different Degrees of Deterioration

Research on Dynamic Performance of Independent Metering Valves Controlling Concrete-Placing Booms Based on Fuzzy-LADRC Controller

Crevice corrosion of reinforcing steel in carbonated simulated concrete pore solutions contaminated by chloride

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