A multiple back propagation neural network fusion algorithm for ceiling temperature prediction in tunnel fires

Sun, Bin; Xu, Zhao‐Dong; Zhou, Haoming

doi:10.1016/j.engstruct.2023.115601

Cited by 25 publications

(7 citation statements)

References 40 publications

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“…Its powerful algorithms make it an effective tool for solving complex problems, processing large datasets, enhancing efficiency, and improving user experiences [42]. In predicting shield tunneling parameters, various algorithms, such as Artificial Neural Network (ANN) [43,44], Deep Neural Network (DNN) [45], Backpropagation Neural Network (BPNN) [46,47], Support Vector Machine (SVM) [48,49], Multiple Linear Regression (MNR) [50], and Random Forest (RF) [51][52][53], are commonly employed, and these algorithms are briefly outlined in Table 11.…”

Section: Machine-learning Algorithms In Tunnelingmentioning

confidence: 99%

Analysis of Shield Tunneling Parameters and Research on Prediction Model of Tunneling Excavation Speed in Volcanic Ash Strata of Jakarta–Bandung High-Speed Railway Project

Fan,

Tan,

Zhang

et al. 2024

Applied Sciences

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Insufficient investigations have been conducted on the analysis of shield tunneling parameters and the prediction of the tunneling excavation speed in formations composed of volcanic ash strata. To address this issue, we employ a comprehensive approach utilizing literature research, mathematical statistics, and other methodologies, centered on the analysis of the No. 1 Tunnel of the Jakarta–Bandung High-Speed Railway. Our focus is on examining the evolution patterns and inter-relationships of shield tunneling parameters within volcanic ash strata. Subsequently, we propose an optimized strategy for these tunneling parameters. By employing six machine-learning algorithms to construct prediction models, we compare and analyze their performance in predicting the tunneling excavation speed. The results indicate a positive correlation between slurry pressure and tunnel depth in volcanic ash strata, suggesting that the grouting pressure should exceed the slurry pressure by approximately 0.22 MPa. In the composite stratum of “volcanic ash debris + round gravel”, the cutter torque exhibits a strong negative correlation with the total thrust (−0.77). Due to tool wear and ground resistance, the excavation speed and cutter speed are weakly negatively correlated. Compared to other strata, shield tunneling in volcanic ash strata exhibits larger grouting pressure fluctuations, slower tunneling excavation speed, greater total thrust, higher cutter torque, and lower cutter speed. Regarding shield tunneling excavation speed prediction, the ranking of the algorithm performance is RF > DNN > ANN > BPNN > MNR > SVM, with RF achieving a decision coefficient of 0.829. The RF model is well-suited for predicting the shield structure tunneling excavation speed.

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Section: Machine-learning Algorithms In Tunnelingmentioning

confidence: 99%

Analysis of Shield Tunneling Parameters and Research on Prediction Model of Tunneling Excavation Speed in Volcanic Ash Strata of Jakarta–Bandung High-Speed Railway Project

Fan,

Tan,

Zhang

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

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“…The architecture diagram of basic backpropagation NN (BPNN) [37,38] is a good algorithm that simulates the nerves of organisms; it is composed of an input layer, a hidden layer, and an output layer. A connection in each layer represents a weight; when the weight reaches the threshold through the activation function output, the neuron is activated, and the data is passed to the next layer.…”

Section: Nn Theory and Its Applicationmentioning

confidence: 99%

Identification of the Yield Rate by a Hybrid Fuzzy Control PID-Based Four-Stage Model: A Case Study of Optical Filter Industry

Chen,

Hung,

Lee

et al. 2024

Axioms

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With the vigorous development of emerging technology and the advent of the Internet generation, high-speed Internet and fast transmission 5G wireless networks contribute to interpersonal communication. Now, the Internet has become popular and widely available, and human life is inseparable from various experiences on the Internet. Many base stations and data centers have been established to convert and switch from electrical transmission to optical transmission; thus, it is entering the new era of optical fiber networks and optical communication technologies. For optical communication, the manufacturing of components for the purpose of high-speed networks is a key process, and the requirement for the stability of its production conditions is very strict. In particular, product yields are always low due to the restriction of high-precision specifications associated with the limitations of too many factors. Given these reasons, this study proposes a hybrid fuzzy control-based model for industry data applications to organize advanced techniques of box-and-whisker plot method, association rule, and decision trees to find out the determinants that affect the yield rate of products and then use the fuzzy control Proportional-Integral-Derivative (PID) method to manage the determinants. Since it is unrealistic to test the real machine online operation at the manufacturing stage, the simulation software supersedes this for improved results, and a mathematical neural network is used to verify the given data to confirm whether its result is similar to that of the simulation. The study suggests that excessive temperature differentials between substrate and cavity can lead to low yields. It suggests using fuzzy control technology for temperature management, which could increase yield, reduce labor costs, and accelerate the transition to high-speed networks by mass-producing high-precision optical filters.

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“…Therefore, an emerging requirement and challenge to cover the traditional methods are focused on an appropriated mathematical algorithm that can eliminate noise and accurately predict the data results of industry applications through some useful values, like generalized tricking eigenvalues. Hereafter, a variety of neural network (NN) structures, such as convolution neural network (CNN) [21,22] or back propagation neural network (BPNN) [23], that can bridge the research gap, because it is like a black box system and can be used for different applications in different industry fields, particularly in those that can be trained only with input and output data without having a well-defined mathematical model framework. Thus, this study uses a good alternative of a NN model as the fourth method to effectively address the PID issues to provide a prevention purpose against the PID problem in advance.…”

Section: Shortcomings and Contributions Of Previous Studies And Resea...mentioning

confidence: 99%

Prevention of PID Phenomenon for Solar Panel Based on Mathematical Data Analysis Models

Chen,

Hung,

Lin

et al. 2023

Mathematics

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In recent years, the problem of potential-induced degradation (PID) phenomenon has been deeply associated with solar power issues because it causes serious power attenuation of solar panels and results in lowering its power generation efficiency. Thus, effectively identifying the PID problem from insights of industry data analysis to reduce production costs and increase the performance of power generation is an interesting and important subject for the solar power industry. Moreover, by the traditional standard rule (IEC62804) and the condition of a 96 h testing time, the costs of testing time and assembling materials against PID are very high and must be improved. Given the above reasons, this study proposes a hybrid procedure to organizes four mathematical methods: the mini-module testing, solar cell testing, a settling time, and a neural network, which are named as Method-1–Method-4, respectively, to efficiently solve the PID problem. Consequently, there are four key outcomes from the empirical results for solar power application: (1) In Method-1 with a 96 h testing time, it was found that the large module with higher costs and the mini module with lower costs have a positive correlation; thus, we can replace the large-module testing by the effective mini module for lower cost on module materials. (2) In Method-2 with a 24 h testing time, it was also found that the mini module and the solar cell are positively correlated; this result provides evidence that we can conduct the PID test by the easier solar cell to lower the costs. (3) In Method-3, the settling time achieves an average accuracy of 94% for PID prediction with a 14 h testing time. (4) In Method-4, the experimental result provides an accuracy of 80% when identifying the PID problem with the mathematical neural network model and are obtained within a 2 h testing time. From the above results, these methods succeed in reducing cost of materials and testing time during the manufacturing process; thus, this study has an industrial application value. Concurrently, Method-3 and Method-4 are rarely seen in the limited literature review for identifying PID problem; therefore, this study also offers a novel contribution for technical application innovation.

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A multiple back propagation neural network fusion algorithm for ceiling temperature prediction in tunnel fires

Cited by 25 publications

References 40 publications

Analysis of Shield Tunneling Parameters and Research on Prediction Model of Tunneling Excavation Speed in Volcanic Ash Strata of Jakarta–Bandung High-Speed Railway Project

Analysis of Shield Tunneling Parameters and Research on Prediction Model of Tunneling Excavation Speed in Volcanic Ash Strata of Jakarta–Bandung High-Speed Railway Project

Identification of the Yield Rate by a Hybrid Fuzzy Control PID-Based Four-Stage Model: A Case Study of Optical Filter Industry

Prevention of PID Phenomenon for Solar Panel Based on Mathematical Data Analysis Models

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