scholarly journals Control method of mechanical smoke emission in high-rise building corridor

2021 ◽  
Vol 25 (6 Part A) ◽  
pp. 4099-4106
Author(s):  
Can Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Traditional Method ◽  
Control Method ◽  
Smoke Control ◽  
Fire Dynamics ◽  
Smoke Emission ◽  
High Rise ◽  
Fire Dynamics Simulator ◽  
High Rise Building

The traditional method has a large control error in the corridor mechanical smoke control method. Therefore, a multi-task convolutional neural network-based high-rise building corridor mechanical smoke control method is proposed. Through the mechanical smoke exhaust principle of high-rise building corridors, the threshold of mechanical smoke exhaust is set to predict the mechanical smoke exhaust volume of high-rise building corridors. The movement of mechanical smoke in high-rise building corridors is simulated according to fire dynamics simulator to determine the turbulence state of mechanical smoke in high-rise building corridors. Input the mechanical smoke exhaust data of high-rise building corridors into the multi-task convolutional neural network to complete the mechanical smoke exhaust control of high-rise building corridors. Experimental results show that the maximum accuracy of this method is about 98%, and the control time is always less than 1 second.

Study on the Applications of Neural Networks for Processing Deformation Monitoring Data

2014 ◽  
Vol 501-504 ◽  
pp. 2149-2153 ◽  
Author(s):  
Cai Yun Gao ◽  
Xi Min Cui ◽  
Xue Qian Hong
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Prediction Models ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Nonlinear Fitting ◽  
High Rise ◽  
High Rise Building ◽  
Good Ability ◽  
Neural Network Prediction

Accurately estimating the deformation of high-rise building is a very important work for surveyors, however it is very difficult to get an accurate and reliable predictor. In this paper, artificial neural network has been applied here because of its good ability of nonlinear fitting. On the basis of the high-rise building monitoring data, three prediction models including the BP, RBF and GRNN neural network prediction models were established, the comparative analysis for the prediction accuracy of the three models was obtained. The results show that neural network is capable for prediction, and GRNN possess higher capability in prediction and better adaptability in comparing with other two neural networks.

Simulation and Analyzing of Mechanical Smoke Exhaust in a High-Rise Building

2013 ◽  
Vol 726-731 ◽  
pp. 3596-3599
Author(s):  
Wei Shi ◽  
Fu Sheng Gao
Keyword(s):  
Fire Safety ◽  
Field Model ◽  
Unit Area ◽  
Zone Model ◽  
Smoke Control ◽  
Control Manner ◽  
High Rise ◽  
High Rise Building

The mechanical smoke exhaust is as acknowledged as an effective smoke control manner by making use of some necessary exhaust facilities. In this paper, a field model with a combination of a zone model were used to simulate the mechanical smoke exhaust in a loop corridor of the fire floor in a high-rise hotel, for the propose of evaluate fire safety of mechanical smoke exhaust. There were several factors are under discussion, as the arrangement of smoke vents, quantity of smoke vents, the volume of smoke exhaust, the position of the smoke vents and height of ceiling indoor, et al. The conclusions were obtained as followed. When two exhaust vents were set symmetrically in the loop corridor, one of which was located nearby the fire room, the smoke exhausted better. The volume of smoke exhaust per unit area with 60m3/h according to regulations, always could ensure safety of smoke exhaust.

Safe Evacuation from Subway Station under Platform Train Fire

2014 ◽  
Vol 638-640 ◽  
pp. 2023-2026 ◽  
Author(s):  
Sheng Zeng ◽  
Xiao Xiong Zha ◽  
Yi Yan Chen ◽  
Rui Juan Jiang
Keyword(s):  
Computer Simulation ◽  
Theoretical Analysis ◽  
Subway Station ◽  
Concentration Change ◽  
Smoke Control ◽  
Fire Dynamics ◽  
Fire Temperature ◽  
Fire Dynamics Simulator ◽  
Environmental Deterioration ◽  
Time Calculation

The environmental deterioration of the subway station and the safety of the personnel evacuation under platform train fire are researched. The critical fire danger condition is proposed and the time calculation method of evacuation is determined. A platform train fire in a subway station is simulated by the Fire Dynamics Simulator software. Then the available egress time can be got by analyzing the fire temperature and smoke concentration change with time. At the same time, the required egress time is studied through theoretical analysis and computer simulation by software Building Exodus. The results showed that smoke exhaust rate is very important to the smoke control under platform train fire. And the stair evacuation ability is the key to the whole evacuation.

Study on Concrete Construction Technology of High-Rise Building

2015 ◽  
Vol 730 ◽  
pp. 93-96
Author(s):  
Ya Guo
Keyword(s):  
Control Method ◽  
Construction Method ◽  
Construction Technology ◽  
Construction Site ◽  
Practical Application ◽  
Concrete Construction ◽  
High Rise ◽  
High Rise Building ◽  
Key Points ◽  
Technical Measures

This paper introduces the features and key points of construction of high-rise buildings. Combined with the practical application, the technology of concrete construction is put forward, as well as the control method of concrete pouring quality. According to the specific conditions of construction site, the construction method of foundation from shallow to deep is adapted. The different placement scheme and technical measures are made.

An improved linear quadratic regulator control method through convolutional neural network–based vibration identification

2020 ◽  
pp. 107754632093375
Author(s):  
Xinzheng Lu ◽  
Wenjie Liao ◽  
Wei Huang ◽  
Yongjia Xu ◽  
Xingyu Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Control Algorithm ◽  
Control Method ◽  
Wavelet Coefficient ◽  
Linear Quadratic Regulator ◽  
Optimal Parameters ◽  
Linear Quadratic ◽  
Negative Effects ◽  
Environmental Vibration

An efficient vibration control can reduce negative effects induced by environmental vibrations and thereby improve the performance of precision instruments and the qualities of manufacture. The performance of the widely used linear quadratic regulator control algorithm, a classical active control methodology, depends on the parameters of the control algorithm. Consequently, a set of fixed parameters cannot satisfy the demand for controlling various types of environmental vibrations. Therefore, this study proposes a vibration identification method based on a convolutional neural network. This method helps to optimize the linear quadratic regulator algorithm by selecting corresponding optimal parameters according to the identification results, thereby achieving the objective of optimal control subjected to various types of vibration inputs. Specifically, environmental vibration signals are collected, and the preliminary features of the vibrations (i.e. wavelet coefficient matrices or images) are adopted as input samples for the convolutional neural network. A genetic algorithm is used to optimize the parameters of the linear quadratic regulator algorithm for each type of vibration; subsequently, the trained convolutional neural network model with the best performance is used to identify the vibration and select the corresponding optimal parameters of the linear quadratic regulator algorithm under different types of vibration inputs. Case studies show that the performance of the improved linear quadratic regulator control method is significantly better than that of the conventional linear quadratic regulator algorithm with fixed parameters.

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