scholarly journals Critical distance between two irregular adjacent buildings in order to prevent collision during earthquake

2021 ◽  
Author(s):  
Yaghub Ebrahimi ◽  
◽  
Ali Hemmati ◽  
Ali Reza Mortezaei ◽  
Mahmud Nikkhah Shahmirzadi ◽  
...  
Keyword(s):  
Neural Network ◽  
Lateral Displacement ◽  
Critical Distance ◽  
Gap Size ◽  
Story Model ◽  
Adjacent Buildings ◽  
Mathematical Equations ◽  
Lateral Displacements ◽  
The Impact ◽  
Tabas Earthquake

The aim of this study is to evaluate the value of suitable distance due to prevent the impact between two irregular adjacent buildings when earthquake is caused to occur large lateral displacement and damage the elements of buildings. For this purpose, by using a mathematical program based on neural network, the number of stories, the period and height of investigated models, PGD, PGV and PGA of earthquake records are defined and the nonlinear lateral displacements of different structures are determined in order to use in the program. Thus, the results of displacements based on all inputs are listed and the minimum critical distance is approximately estimated based on especial regression. For instance, a 3-4 story model is numerically investigated by Tabas earthquake record, which is suggested to provide required gap size about 70 cm. In fact, each model has to observe a 35 cm gap. A newly developed program based on mathematical equations are applied for determining the lateral displacements of each story. A new mathematical formula is proposed by neural network, which shows the least distance between irregular adjacent buildings. For investigating the accuracy of formula, two different ways are performed and the results of analyses confirm suggested equation. For this challenge, a 2-4 story model is considered and three different critical distances are calculated to be 59, 62 and 75 cm which show the last gap size is able to provide safety gap size, determined by suggested formula.

scholarly journals Predicting the peak structural displacement preventing pounding of buildings during earthquakes

2021 ◽  
Vol 2070 (1) ◽  
pp. 012010
Author(s):  
S M Khatami ◽  
H Naderpour ◽  
A Mortezaei ◽  
S T. Tafreshi ◽  
A Jakubczyk-Gałczyńska ◽  
...  
Keyword(s):  
Structural Model ◽  
Lateral Displacement ◽  
Critical Distance ◽  
Lumped Mass ◽  
Seismic Excitations ◽  
Ground Acceleration ◽  
Vibration Period ◽  
Adjacent Buildings ◽  
Artificial Neural Network Ann ◽  
Story Building

Abstract The aim of the present paper is to verify the effectiveness of the artificial neural network (ANN) in predicting the peak lateral displacement of multi-story building during earthquakes, based on the peak ground acceleration (PGA) and building parameters. For the purpose of the study, the lumped-mass multi-degree-of-freedom structural model and different earthquake records have been considered. Firstly, values of stories mass and stories stiffness have been selected and building vibration period has been automatically calculated. The ANN algorithm has been used to determine the limitation of the peak lateral displacement of the multi-story building with different properties (height of stories, number of stories, mass of stories, stiffness of stories and building vibration period) exposed to earthquakes with various PGA. Then, the investigation has been focused on critical distance between two adjacent buildings so as to prevent their pounding during earthquakes. The proposed ANN has logically predicted the limitation of the peak lateral displacement for the five-story building with different properties. The results of the study clearly indicate that the algorithm is also capable to properly predict the peak lateral dis-placements for two buildings so as to prevent their pounding under different earthquakes. Subsequently, calculation of critical distance can also be optimized to save the land and provide the safety space between two adjacent buildings prone to seismic excitations.

A survey of hysteresis energy distribution and lateral displacement in steel buildings with CCB brace at internal and external frames

2018 ◽  
Vol 9 (1) ◽  
pp. 38-49 ◽  
Author(s):  
Hadi Faghihmaleki ◽  
Gholamreza Abdollahzadeh ◽  
Hedieh Esmaili
Keyword(s):  
Energy Distribution ◽  
Design Methodology ◽  
Seismic Vulnerability ◽  
Lateral Displacement ◽  
Steel Buildings ◽  
Content Type ◽  
First Case ◽  
Lateral Displacements ◽  
Far Fault ◽  
The Impact

Purpose The purpose of this paper is to study the method of hysteresis energy distribution and maximum relative lateral displacement in buildings’ stories, under the influence of scaled records for near-fault and far-fault earthquakes. The bracings in the considered buildings’ plan are distributed in two different ways: in the first case, the braces are added in external frames of the building, and in the second case, in the internal ones. Design/methodology/approach This research first selects some steel buildings with concentric braces and studies the seismic vulnerability of buildings under different earthquakes in accordance with the concepts of input and Hysteresis energy. In order to study the impact of braces’ distribution in the building’s plan, the buildings were modeled in this study in two ways. In the first way the braces were added to the building’s external frames and in the second way in its internal ones. Findings Results show that the need for far-fault scaled records’ displacement is more than the near ones and that the resultant relative lateral displacements in buildings with external braces are more than those with internal ones. Originality/value After these studies on the way of hysteresis energy distribution, it was shown that in case of buildings with internal braces, as the building’s height increases, the share of higher stories of the hysteresis energy rises. Also, it was illustrated that hysteresis energy distribution in buildings with internal braces is more uniform than those with external ones.

scholarly journals Prediction of Critical Distance Between Two MDOF Systems Subjected to Seismic Excitation in Terms of Artificial Neural Networks

2017 ◽  
Author(s):  
Hosein Naderpour ◽  
Seyed Mohammad Khatami ◽  
Rui Carneiro Barros
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Separation Distance ◽  
Critical Distance ◽  
Building Codes ◽  
Seismic Excitation ◽  
Gap Size ◽  
Mdof Systems ◽  
Lateral Displacements ◽  
New Equation

This study focuses on preventing collisions between structuresduring seismic excitation based on gap size. Several approximatedequations in order to estimate separation distancebetween buildings are collected and evaluated to measure gapsize in order to avoid impact between them when large lateraldisplacements occurred due to earthquake. Artificial neuralnetworks are utilized to estimate the required distance betweenstructures. The majority of building codes suggest separationdistances based on maximum lateral displacements of eachbuilding or height of buildings in order to provide safety gapsize between them. Subsequently, researchers have proposedseveral equations to predict the critical distance. In currentstudy, some MDOF models are equivalently modelled and optimumgap size between buildings is approximately estimatedand finally a new equation for separation distance is suggestedand the accuracy of formula is numerically investigated.

scholarly journals Collapse-Pounding Dynamic Responses of Adjacent Frame Structures under Earthquake Action

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Wei Jing ◽  
Shangshang Xing ◽  
Yu Song
Keyword(s):  
Impact Force ◽  
Contact Problems ◽  
Dynamic Responses ◽  
Seismic Action ◽  
Angle Distribution ◽  
Gap Size ◽  
Adjacent Buildings ◽  
Adjacent Structures ◽  
Displacement Angle ◽  
The Impact

There are a large number of adjacent buildings in practical engineering application. The structure will collapse and impact the adjacent structures once the weak column is destroyed under seismic action, and, finally, the earthquake damage is aggravated. Material nonlinearity, initial imperfections, and contact problems in the process of collapse-pounding are considered, and the three-dimensional calculation model with unequal 8-story and 6-story height adjacent frames is established. The dynamic response of adjacent structures caused by collapse-pounding is investigated when there is a weak column at different positions in the 8-story frame, and the influence of gap size on the dynamic responses of adjacent structures is discussed. The results show that the impact force is larger when the weak column of the 8-story frame is close to the top of the 6-story frame; pounding increases the interlayer displacement angle of the 8-story frame and decreases the interlayer displacement angle of the 6-story frame in general; the impact force decreases first, then increases, and after that decreases with the increase of gap size; and the interlayer displacement angle distribution of the 6-story frame is significantly affected after the collapse-pounding of the 8-story frame with a weak column. The collapse-pounding problem of adjacent buildings under seismic action is very complex, which should be paid enough attention in engineering design and application.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

European Economies Stability Faced With Potential Outburst of Sovereign Debt Crisis. An Empirical Study Using Neural Network

2013 ◽  
Vol 12 (2) ◽  
pp. 3255-3260
Author(s):  
Stelian Stancu ◽  
Alexandra Maria Constantin
Keyword(s):  
Neural Network ◽  
Sovereign Debt ◽  
Debt Crisis ◽  
Banking System ◽  
Sovereign Debt Crisis ◽  
European Level ◽  
Continuous Growth ◽  
Economic Context ◽  
European Economies ◽  
The Impact

Instilment, on a European level, of a state incompatible with the state of stability on a macroeconomic level and in the financial-banking system lead to continuous growth of vulnerability of European economies, situated at the verge of an outburst of sovereign debt crises. In this context, the current papers main objective is to produce a study regarding the vulnerability of European economies faced with potential outburst of sovereign debt crisis, which implies quantitative analysis of the impact of sovereign debt on the sensitivity of the European Unions economies. The paper also entails the following specific objectives: completing an introduction in the current European economic context, conceptualization of the notion of “sovereign debt crisis, presenting the methodology and obtained empirical results, as well as exposition of the conclusions.

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

scholarly journals Graphene-based 3D XNOR-VRRAM with ternary precision for neuromorphic computing

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Batyrbek Alimkhanuly ◽  
Joon Sohn ◽  
Ik-Joon Chang ◽  
Seunghyun Lee
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Recognition Accuracy ◽  
Material Selection ◽  
Weighted Sum ◽  
Device Design ◽  
Key Factors ◽  
Neuromorphic Computing ◽  
Device Scaling ◽  
The Impact

AbstractRecent studies on neural network quantization have demonstrated a beneficial compromise between accuracy, computation rate, and architecture size. Implementing a 3D Vertical RRAM (VRRAM) array accompanied by device scaling may further improve such networks’ density and energy consumption. Individual device design, optimized interconnects, and careful material selection are key factors determining the overall computation performance. In this work, the impact of replacing conventional devices with microfabricated, graphene-based VRRAM is investigated for circuit and algorithmic levels. By exploiting a sub-nm thin 2D material, the VRRAM array demonstrates an improved read/write margins and read inaccuracy level for the weighted-sum procedure. Moreover, energy consumption is significantly reduced in array programming operations. Finally, an XNOR logic-inspired architecture designed to integrate 1-bit ternary precision synaptic weights into graphene-based VRRAM is introduced. Simulations on VRRAM with metal and graphene word-planes demonstrate 83.5 and 94.1% recognition accuracy, respectively, denoting the importance of material innovation in neuromorphic computing.

Lateral obstacle avoidance control based on driving behavior recognition of the preceding vehicles in adjacent lanes

2020 ◽  
Vol 68 (10) ◽  
pp. 880-892
Author(s):  
Youguo He ◽  
Xing Gong ◽  
Chaochun Yuan ◽  
Jie Shen ◽  
Yingkui Du
Keyword(s):  
Obstacle Avoidance ◽  
Lateral Displacement ◽  
Longitudinal Velocity ◽  
Driving Behavior ◽  
Lateral Acceleration ◽  
Lateral Motion ◽  
Behavior Recognition ◽  
Dynamic Surface ◽  
Lane Changing ◽  
The Impact

AbstractThis paper proposes a lateral lane change obstacle avoidance constraint control simulation algorithm based on the driving behavior recognition of the preceding vehicles in adjacent lanes. Firstly, the driving behavior of the preceding vehicles is recognized based on the Hidden Markov Model, this research uses longitudinal velocity, lateral displacement and lateral velocity as the optimal observation signals to recognize the driving behaviors including lane-keeping, left-lane-changing or right-lane-changing; Secondly, through the simulation of the dangerous cutting-in behavior of the preceding vehicles in adjacent lanes, this paper calculates the ideal front wheel steering angle according to the designed lateral acceleration in the process of obstacle avoidance, designs the vehicle lateral motion controller by combining the backstepping and Dynamic Surface Control, and the safety boundary of the lateral motion is constrained based on the Barrier Lyapunov Function; Finally, simulation model is built, and the simulation results show that the designed controller has good performance. This active safety technology effectively reduces the impact on the autonomous vehicle safety when the preceding vehicle suddenly cuts into the lane.

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