EFFECT OF FLOOR PLAN SHAPE ON SEISMIC RESPONSES OF BASE-ISOLATED BUILDINGS

2021 ◽  
Vol 10 (1) ◽  
pp. 1
Author(s):  
MISHRA ABHISHEK ◽  
CHANDRA MAURYA MADAN ◽  
AHMAD KHAN FAHEEM ◽  
◽  
◽  
...  
Keyword(s):  
Floor Plan ◽  
Seismic Responses

Impact of Reservoir Pressure Changes on 4D Seismic Responses in Carbonates: Special Rock Physics Core Study

2007 ◽  
Author(s):  
William L. Soroka ◽  
Taha Al-Dayyani ◽  
Christian J. Strohmenger ◽  
Hafez H. Hafez ◽  
Mahfoud Salah Al-Jenaibi
Keyword(s):  
Rock Physics ◽  
Reservoir Pressure ◽  
Seismic Responses ◽  
Core Study ◽  
Pressure Changes ◽  
4D Seismic

Shaking-table tests of seismic responses of slender intake tower-hoist chamber systems

2021 ◽  
Vol 242 ◽  
pp. 112517
Author(s):  
Hanyun Zhang ◽  
Cai Jiang ◽  
Shuming Liu ◽  
Liaojun Zhang ◽  
Chen Wang ◽  
...  
Keyword(s):  
Shaking Table ◽  
Shaking Table Tests ◽  
Seismic Responses

scholarly journals Framework for Indoor Elements Classification via Inductive Learning on Floor Plan Graphs

2021 ◽  
Vol 10 (2) ◽  
pp. 97
Author(s):  
Jaeyoung Song ◽  
Kiyun Yu
Keyword(s):  
Neural Network ◽  
Inductive Learning ◽  
Network Data ◽  
Floor Plan ◽  
Vector Data ◽  
Adjacency Graph ◽  
Different Types ◽  
Image Pixels ◽  
Learning Frameworks ◽  
New Framework

This paper presents a new framework to classify floor plan elements and represent them in a vector format. Unlike existing approaches using image-based learning frameworks as the first step to segment the image pixels, we first convert the input floor plan image into vector data and utilize a graph neural network. Our framework consists of three steps. (1) image pre-processing and vectorization of the floor plan image; (2) region adjacency graph conversion; and (3) the graph neural network on converted floor plan graphs. Our approach is able to capture different types of indoor elements including basic elements, such as walls, doors, and symbols, as well as spatial elements, such as rooms and corridors. In addition, the proposed method can also detect element shapes. Experimental results show that our framework can classify indoor elements with an F1 score of 95%, with scale and rotation invariance. Furthermore, we propose a new graph neural network model that takes the distance between nodes into account, which is a valuable feature of spatial network data.

scholarly journals DeepLocBox: Reliable Fingerprinting-Based Indoor Area Localization

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2000
Author(s):  
Marius Laska ◽  
Jörg Blankenbach
Keyword(s):  
Wireless Local Area Network ◽  
Indoor Localization ◽  
Local Area Network ◽  
Success Probability ◽  
Local Area ◽  
Position Estimation ◽  
Location Based Services ◽  
Global Navigation Satellite Systems ◽  
Area Network ◽  
Floor Plan

Location-based services (LBS) have gained increasing importance in our everyday lives and serve as the foundation for many smartphone applications. Whereas Global Navigation Satellite Systems (GNSS) enable reliable position estimation outdoors, there does not exist any comparable gold standard for indoor localization yet. Wireless local area network (WLAN) fingerprinting is still a promising and widely adopted approach to indoor localization, since it does not rely on preinstalled hardware but uses the existing WLAN infrastructure typically present in buildings. The accuracy of the method is, however, limited due to unstable fingerprints, etc. Deep learning has recently gained attention in the field of indoor localization and is also utilized to increase the performance of fingerprinting-based approaches. Current solutions can be grouped into models that either estimate the exact position of the user (regression) or classify the area (pre-segmented floor plan) or a reference location. We propose a model, DeepLocBox (DLB), that offers reliable area localization in multi-building/multi-floor environments without the prerequisite of a pre-segmented floor plan. Instead, the model predicts a bounding box that contains the user’s position while minimizing the required prediction space (size of the box). We compare the performance of DLB with the standard approach of neural network-based position estimation and demonstrate that DLB achieves a gain in success probability by 9.48% on a self-collected dataset at RWTH Aachen University, Germany; by 5.48% for a dataset provided by Tampere University of Technology (TUT), Finland; and by 3.71% for the UJIIndoorLoc dataset collected at Jaume I University (UJI) campus, Spain.

A simplified iterative approach for testing the pulse derailment of light rail vehicles across a viaduct to near-fault earthquake scenarios

2021 ◽  
pp. 095440972098741
Author(s):  
Ling-Kun Chen ◽  
Peng Liu ◽  
Li-Ming Zhu ◽  
Jing-Bo Ding ◽  
Yu-Lin Feng ◽  
...  
Keyword(s):  
Ground Motions ◽  
Simulation Software ◽  
Light Rail ◽  
Rail Transit ◽  
Light Rail Transit ◽  
Seismic Responses ◽  
Near Fault ◽  
Rail Vehicle ◽  
Pulse Type ◽  
The Impact

Near-fault (NF) earthquakes cause severe bridge damage, particularly urban bridges subjected to light rail transit (LRT), which could affect the safety of the light rail transit vehicle (“light rail vehicle” or “LRV” for short). Now when a variety of studies on the fault fracture effect on the working protection of LRVs are available for the study of cars subjected to far-reaching soil motion (FFGMs), further examination is appropriate. For the first time, this paper introduced the LRV derailment mechanism caused by pulse-type near-fault ground motions (NFGMs), suggesting the concept of pulse derailment. The effects of near-fault ground motions (NFGMs) are included in an available numerical process developed for the LRV analysis of the VBI system. A simplified iterative algorithm is proposed to assess the stability and nonlinear seismic response of an LRV-reinforced concrete (RC) viaduct (LRVBRCV) system to a long-period NFGMs using the dynamic substructure method (DSM). Furthermore, a computer simulation software was developed to compute the nonlinear seismic responses of the VBI system to pulse-type NFGMs, non-pulse-type NFGMs, and FFGMs named Dynamic Interaction Analysis for Light-Rail-Vehicle Bridge System (DIALRVBS). The nonlinear bridge seismic reaction determines the impact of pulses on lateral peak earth acceleration (Ap) and lateral peak land (Vp) ratios. The analysis results quantify the effects of pulse-type NFGMs seismic responses on the LRV operations' safety. In contrast with the pulse-type non-pulse NFGMs and FFGMs, this article's research shows that pulse-type NFGM derail trains primarily via the transverse velocity pulse effect. Hence, this study's results and the proposed method can improve the LRT bridges' seismic designs.

Shaking table tests on the seismic responses of underground structures in coral sand

2021 ◽  
Vol 109 ◽  
pp. 103775
Author(s):  
Xuanming Ding ◽  
Yanling Zhang ◽  
Qi Wu ◽  
Zhixiong Chen ◽  
Chenglong Wang
Keyword(s):  
Shaking Table ◽  
Shaking Table Tests ◽  
Seismic Responses ◽  
Underground Structures ◽  
Coral Sand
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