Automated intelligent hybrid computing schemes to predict blasting induced ground vibration

Shahri, Abbas Abbaszadeh; Pashamohammadi, Fardin; Asheghi, Reza; Shahri, Hossein

doi:10.1007/s00366-021-01444-1

Cited by 25 publications

(4 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AI optimisation strategies have been demonstrated to enhance the efficiency and cost-effectiveness of procedures for identifying an optimal solution from a range of repeatedly compared replies. The application of classical methodologies results in a system of simultaneous nonlinear equations that can present challenges in terms of solvability [ 26 ].…”

Section: Vissim Application Literature Reviewmentioning

confidence: 99%

“…Moreover, when implemented in public transportation, AVs can reduce labour expenses [ 56 ]. Microscopic traffic simulations, also known as traffic simulation, such as VISSIM, offer a proactive methodology for evaluating the consequences of automated traffic on different aspects, including transportation efficiency, safety, equity, and the environment [ 26 , 55 ] ( Table 3 ).…”

Section: Vissim Application Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Review of driving-behaviour simulation: VISSIM and artificial intelligence approach

Al-Msari,

Koting,

Ahmed

et al. 2024

Heliyon

View full text Add to dashboard Cite

Section: Vissim Application Literature Reviewmentioning

confidence: 99%

Section: Vissim Application Literature Reviewmentioning

confidence: 99%

Review of driving-behaviour simulation: VISSIM and artificial intelligence approach

Al-Msari,

Koting,

Ahmed

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…Blasting is widely used in geoengineering projects, but improper blasting-induced ground vibrations expressed as PPV can pose a threat to the environment and residents, and to reduce the dangerous effects of blasting, Abbaszadeh Shahri et al [128] used a generalized feedforward neural network (GFFN) structure combined with a novel automatic intelligent parameter setting method, using GFFN combined with firefly and imperialist competing metaheuristic algorithms (FMA and ICA) to develop two new optimized hybrid models, which have significantly improved the prediction accuracy for PPV in real detection events.…”

Section: Earthquake Detection and Locationmentioning

confidence: 99%

Recent Advances of Deep Learning in Geological Hazard Forecasting

Wang¹,

Sun²,

Chen³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

Geological hazard is an adverse geological condition that can cause loss of life and property. Accurate prediction and analysis of geological hazards is an important and challenging task. In the past decade, there has been a great expansion of geohazard detection data and advancement in data-driven simulation techniques. In particular, great efforts have been made in applying deep learning to predict geohazards. To understand the recent progress in this field, this paper provides an overview of the commonly used data sources and deep neural networks in the prediction of a variety of geological hazards. KEYWORDSGeological hazard; deep learning; neural networks; geohazard data sources; earthquake; volcanic 1 Introduction Geological hazards are caused by endogenous and exogenous geological processes or anthropogenic factors on earth [1,2], which encompass a broad range of abnormal stratigraphic activity or extreme changes in geological environments, such as landslides, avalanches, debris flows, earthquakes, etc. Geological hazards pose a serious threat to human life and property [3]. Only by accurately forecasting the occurrence of geohazards can emergency measures be devised to mitigate the damage caused by the impending disaster. Geological hazard forecasting refers to the use of logical reasoning, numerical simulation, and comprehensive analysis based on historical geological hazard activity patterns, formation conditions, occurrence mechanisms, and other factors to speculate and assess the development and changes of geological hazards and the possible degree of danger and damage in a certain period in the future. Geohazard forecasting has attracted tremendous attention nowadays, particularly considering natural resource scarcity, environmental degradation, population expansion, sustainable development, and world economic integration. Many efforts have been made in forecasting geological hazards in the past decades. The "3S technology", including Global Position System (GPS), Geographic Information System (GIS) [4], and Remote Sensing (RS), has been widely applied in the field of geological hazard forecasting [5]. The

show abstract

“…Kim et al [17,18] employed machine learning algorithms to effectively predict tunnel surface settlement, enhancing the prediction capabilities for surface settlement in urban tunnel construction sites under complex excavation conditions. In addition, the use of combination models with different machine learning methods and various optimization algorithms for predicting ground vibrations caused by blasting and tunnel excavation-induced surface settlement has found applications, effectively improving the prediction of ground settlement [19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Mine Surface Settlement Prediction Based on Optimized VMD and Multi-Model Combination

Shen,

2023

Processes

View full text Add to dashboard Cite

The accurate prediction of mining area surface deformation is essential to preventing large-scale coal mining-related surface collapse and ensure safety and daily life continuity. Monitoring subsidence in mining areas is challenged by environmental interference, causing data noise. This paper employs the Sparrow Search Algorithm, which integrates Sine Cosine and Cauchy mutation (SCSSA), to optimize variational mode decomposition (VMD) and combine multi-models for prediction. Firstly, SCSSA is employed to adaptively determine the parameters of VMD using envelope entropy as the fitness value. Subsequently, the VMD method optimized using SCSSA adaptively decomposes the original mining area subsidence data sequence into various sub-sequences. Then, SCSSA-VMD is applied to adaptively decompose the original mining subsidence data sequence into multiple sub-sequences. Meanwhile, using sample entropy, the sub-sequences are categorized into trend sequences and fluctuation sequences, and different models are employed to predict sub-sequences at different frequencies. Finally, the prediction results from different sub-sequences are integrated to obtain the final prediction of mining area subsidence. To validate the predictive performance of the established model, experiments are conducted using GNSS monitoring data from the 110801 working face of Banji Coal Mine in Bozhou. The results demonstrate the following: (1) The hybrid model enhanced the prediction accuracy and trends by decomposing the data and optimizing the parameters with VMD. It outperformed single models, reducing errors and improving predictive trends. (2) The hybrid model significantly improved the prediction accuracy for subsidence data at work surface monitoring stations. It is particularly effective at critical subsidence points, making it a valuable reference for safety in mining operations.

show abstract

Automated intelligent hybrid computing schemes to predict blasting induced ground vibration

Cited by 25 publications

References 45 publications

Review of driving-behaviour simulation: VISSIM and artificial intelligence approach

Review of driving-behaviour simulation: VISSIM and artificial intelligence approach

Recent Advances of Deep Learning in Geological Hazard Forecasting

Mine Surface Settlement Prediction Based on Optimized VMD and Multi-Model Combination

Contact Info

Product

Resources

About