Remote sensing identification and hazard assessment methods for spoil sites

Spoil sites, as loose accumulations, are prone to instability after disturbances and are a serious threat to the safety of downstream communities. This research aims to rapidly and accurately assess the potential hazards of the numerous spoil sides. An integrated approach that combines spatial, aerial, and ground techniques was developed to rapidly identify and interpret the key factors influencing the stability of spoil sites. Subsequently, based on the results of numerical simulation of multiple combinations of unfavorable working conditions by the material point method, a hazard assessment methodology for spoil sites is proposed, which combines the limit equilibrium method with the improved hierarchical analysis. The proposed method is applied and validated on spoil sites along an expressway. The achieved results reveal that remote sensing combined with unmanned aerial vehicle (UAV) verification technology is capable of quickly and accurately identifying the critical elements of spoil sites. The hazard assessment of the spoil site is represented by the hazard coefficient (P), where 0 < P < 1 indicates safety, 1 ≤ P < 5 signifies instability, and P ≥ 5 represents extreme instability. The effectiveness of the proposed assessment approach is then verified through the on-site validation, which provides a vital reference for rapid identification and batch assessment of spoil sites.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2024-09-27
DOI
https://doi.org/10.1038/s41598-024-72942-7
Primary Topic
Landslides and related hazards
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Remote sensing identification and hazard assessment methods for spoil sites

Wei Xiao, Weiping Tian
Scientific Reports
Landslides and related hazards
article

Remote sensing identification and hazard assessment methods for spoil sites

Wei Xiao, Weiping Tian
article en

Abstract

Spoil sites, as loose accumulations, are prone to instability after disturbances and are a serious threat to the safety of downstream communities. This research aims to rapidly and accurately assess the potential hazards of the numerous spoil sides. An integrated approach that combines spatial, aerial, and ground techniques was developed to rapidly identify and interpret the key factors influencing the stability of spoil sites. Subsequently, based on the results of numerical simulation of multiple combinations of unfavorable working conditions by the material point method, a hazard assessment methodology for spoil sites is proposed, which combines the limit equilibrium method with the improved hierarchical analysis. The proposed method is applied and validated on spoil sites along an expressway. The achieved results reveal that remote sensing combined with unmanned aerial vehicle (UAV) verification technology is capable of quickly and accurately identifying the critical elements of spoil sites. The hazard assessment of the spoil site is represented by the hazard coefficient (P), where 0 < P < 1 indicates safety, 1 ≤ P < 5 signifies instability, and P ≥ 5 represents extreme instability. The effectiveness of the proposed assessment approach is then verified through the on-site validation, which provides a vital reference for rapid identification and batch assessment of spoil sites.

Scientific ReportsVol. 14(1)
Chang'an University (CN)
Natural Science Basic Research Program of Shaanxi Province
Climate action
Openalex Percentile: Top 76%
Landslides and related hazards
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.