An Agent-Based Simulation of Deep Foundation Pit Emergency Evacuation Modeling in the Presence of Collapse Disaster
With the gradual expansion of high buildings and underground spaces, deep foundation pits have been widely used in these engineering projects, but if they are not well-designed, safety problems occur. Proper deep foundation pit design requires proper exit distribution. However, calculating an adequa...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2018-11-01
|
Series: | Symmetry |
Subjects: | |
Online Access: | https://www.mdpi.com/2073-8994/10/11/581 |
id |
doaj-27e83a943a95409cab7ab12102d5f1d4 |
---|---|
record_format |
Article |
spelling |
doaj-27e83a943a95409cab7ab12102d5f1d42020-11-24T22:40:08ZengMDPI AGSymmetry2073-89942018-11-01101158110.3390/sym10110581sym10110581An Agent-Based Simulation of Deep Foundation Pit Emergency Evacuation Modeling in the Presence of Collapse DisasterWeilong Yang0Yue Hu1Cong Hu2Mei Yang3College of System Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of System Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of System Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of System Engineering, National University of Defense Technology, Changsha 410073, ChinaWith the gradual expansion of high buildings and underground spaces, deep foundation pits have been widely used in these engineering projects, but if they are not well-designed, safety problems occur. Proper deep foundation pit design requires proper exit distribution. However, calculating an adequate number of exit distributions for evaluation is difficult due to the numerous influential factors existing in the deep foundation pit environment. To this end, this paper presents a prototype of a decision-making system that uses agent-based modeling to simulate deep foundation pit evacuation in the presence of collapse disaster. By modeling the collapse occurrence process and agent escape process, an agent-based evacuation model is built, and a modified simulation-based particle swarm optimization algorithm is used to solve the optimization problem of exit distribution. Extensive experiments are conducted to verify the system, and the results show that the system provides a feasible framework for deep foundation pit evacuation.https://www.mdpi.com/2073-8994/10/11/581emergency evacuationdeep foundation pitagent-based simulationcollapse disasterparticle swarm optimizationweighted Voronoi diagram |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Weilong Yang Yue Hu Cong Hu Mei Yang |
spellingShingle |
Weilong Yang Yue Hu Cong Hu Mei Yang An Agent-Based Simulation of Deep Foundation Pit Emergency Evacuation Modeling in the Presence of Collapse Disaster Symmetry emergency evacuation deep foundation pit agent-based simulation collapse disaster particle swarm optimization weighted Voronoi diagram |
author_facet |
Weilong Yang Yue Hu Cong Hu Mei Yang |
author_sort |
Weilong Yang |
title |
An Agent-Based Simulation of Deep Foundation Pit Emergency Evacuation Modeling in the Presence of Collapse Disaster |
title_short |
An Agent-Based Simulation of Deep Foundation Pit Emergency Evacuation Modeling in the Presence of Collapse Disaster |
title_full |
An Agent-Based Simulation of Deep Foundation Pit Emergency Evacuation Modeling in the Presence of Collapse Disaster |
title_fullStr |
An Agent-Based Simulation of Deep Foundation Pit Emergency Evacuation Modeling in the Presence of Collapse Disaster |
title_full_unstemmed |
An Agent-Based Simulation of Deep Foundation Pit Emergency Evacuation Modeling in the Presence of Collapse Disaster |
title_sort |
agent-based simulation of deep foundation pit emergency evacuation modeling in the presence of collapse disaster |
publisher |
MDPI AG |
series |
Symmetry |
issn |
2073-8994 |
publishDate |
2018-11-01 |
description |
With the gradual expansion of high buildings and underground spaces, deep foundation pits have been widely used in these engineering projects, but if they are not well-designed, safety problems occur. Proper deep foundation pit design requires proper exit distribution. However, calculating an adequate number of exit distributions for evaluation is difficult due to the numerous influential factors existing in the deep foundation pit environment. To this end, this paper presents a prototype of a decision-making system that uses agent-based modeling to simulate deep foundation pit evacuation in the presence of collapse disaster. By modeling the collapse occurrence process and agent escape process, an agent-based evacuation model is built, and a modified simulation-based particle swarm optimization algorithm is used to solve the optimization problem of exit distribution. Extensive experiments are conducted to verify the system, and the results show that the system provides a feasible framework for deep foundation pit evacuation. |
topic |
emergency evacuation deep foundation pit agent-based simulation collapse disaster particle swarm optimization weighted Voronoi diagram |
url |
https://www.mdpi.com/2073-8994/10/11/581 |
work_keys_str_mv |
AT weilongyang anagentbasedsimulationofdeepfoundationpitemergencyevacuationmodelinginthepresenceofcollapsedisaster AT yuehu anagentbasedsimulationofdeepfoundationpitemergencyevacuationmodelinginthepresenceofcollapsedisaster AT conghu anagentbasedsimulationofdeepfoundationpitemergencyevacuationmodelinginthepresenceofcollapsedisaster AT meiyang anagentbasedsimulationofdeepfoundationpitemergencyevacuationmodelinginthepresenceofcollapsedisaster AT weilongyang agentbasedsimulationofdeepfoundationpitemergencyevacuationmodelinginthepresenceofcollapsedisaster AT yuehu agentbasedsimulationofdeepfoundationpitemergencyevacuationmodelinginthepresenceofcollapsedisaster AT conghu agentbasedsimulationofdeepfoundationpitemergencyevacuationmodelinginthepresenceofcollapsedisaster AT meiyang agentbasedsimulationofdeepfoundationpitemergencyevacuationmodelinginthepresenceofcollapsedisaster |
_version_ |
1725705724120530944 |