检索结果-南通市图书馆

Effects of data smoothing and recurrent neural network(RNN)algorithms for real-time forecasting of tunnel boring machine(TBM)performance

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Journal of Rock Mechanics and Geotechnical Engineering 2024年第5期16卷 1538-1551页

作者： Feng Shan Xuzhen He danial Jahed Armaghani Daichao Sheng School of Civil and Environmental Engineering University of Technology SydneyNSW2007Australia

Tunnel boring machines(TBMs)have been widely utilised in tunnel construction due to their high efficiency and *** predicting TBM performance can improve project time management,cost control,and risk *** study aims to use deep learning to develop real-time models for predicting the penetration rate(PR).The models are built using data from the Changsha metro project,and their performances are evaluated using unseen data from the Zhengzhou Metro *** one-step forecast,the predicted penetration rate follows the trend of the measured penetration rate in both training and *** autoregressive integrated moving average(ARIMA)model is compared with the recurrent neural network(RNN)*** results show that univariate models,which only consider historical penetration rate itself,perform better than multivariate models that take into account multiple geological and operational parameters(GEO and OP).Next,an RNN variant combining time series of penetration rate with the last-step geological and operational parameters is developed,and it performs better than other models.A sensitivity analysis shows that the penetration rate is the most important parameter,while other parameters have a smaller impact on time series *** is also found that smoothed data are easier to predict with high ***,over-simplified data can lose real characteristics in time *** conclusion,the RNN variant can accurately predict the next-step penetration rate,and data smoothing is crucial in time series *** study provides practical guidance for TBM performance forecasting in practical engineering.

关键词： Tunnel boring machine(TBM) Penetration rate(PR) Time series forecasting Recurrent neural network(RNN)

Performance comparison of machine learning algorithms for maximum displacement prediction in soldier pile wall excavation

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Underground Space 2024年第3期16卷 301-313页

作者： danial Sheini Dashtgoli Mohammad Hossein Dehnad Seyed Ahmad Mobinipour Michela Giustiniani Department of Mathematics Informaticsand GeosciencesUniversity of TriesteTrieste 34127Italy National Institute of Oceanography and Applied Geophysics-OGS Borgo Grotta Gigante 42/CTriesteSgonico 34010Italy Department of Civil Engineering University of QomQom ***Iran

One of the common excavation methods in the construction of urban infrastructures as well as water and wastewater facilities is the excavation through soldier pile *** maximum lateral displacement of pile wall is one of the important variables in controlling the stability of the excavation and its adjacent ***,the application of machine learning methods is widely used in engineering sciences due to its low cost and high speed of *** paper utilized three intelligent machine learning algorithms based on the excavation method through soldier pile walls,namely eXtreme gradient boosting(XGBoost),least square support vector regressor(LS-SVR),and random forest(RF),to predict maximum lateral displacement of pile *** results showed that the implemented XGBoost model performed excellently and could make predictions for maximum lateral displacement of pile walls with the mean absolute error of 0.1669,the highest coefficient of determination 0.9991,and the lowest root mean square error *** the LS-SVR,and RF models were less accurate than the XGBoost model,they provided good prediction results of maximum lateral displacement of pile walls for numerical ***,a sensitivity analysis was performed to determine the most effective parameters in the XGBoost *** analysis showed that soil elastic modulus and excavation height had a strong influence on of maximum lateral displacement of pile wall prediction.

关键词： Soldier pile wall Lateral displacements XGBoost Machine learning Artificial intelligence

Prediction of vertical displacement for a buried pipeline subjected to normal fault using a hybrid FEM-ANN approach

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Frontiers of Structural and Civil Engineering 2024年第3期18卷 428-443页

作者： Hedye JALALI Reza YEGANEH KHAKSAR danial MOHAMMADZADEH S. Nader KARBALLAEEZADEH Amir H.GANDOMI Department of Civil Engineering Architecture and UrbanismSadjad University of TechnologyPO Box ***MashhadIran Department of Civil Engineering Faculty of EngineeringEqbal Lahoori Institute of Higher EducationPO Box ***MashhadIran Faculty of Civil Engineering Architectureand ArtScience and Research Branch of Islamic Azad UniversityPO Box ***TehranIran Faculty of Engineering&Information Technology University of Technology SydneyUltimoNSW 2007Australia University Research and Innovation Center(EKIK) Óbuda UniversityBudapest 1034Hungary

Fault movement during earthquakes is a geotechnical phenomenon threatening buried pipelines and with the potential to cause severe damage to critical ***,effective prediction of pipe displacement is crucial for preventive management *** study aims to develop a fast,hybrid model for predicting vertical displacement of pipe networks when they experience *** this study,the complex behavior of soil and a buried pipeline system subjected to a normal fault is analyzed by using an artificial neural network(ANN)to generate predictions the behavior of the soil when different parameters of it are *** this purpose,a finite element model is developed for a pipeline subjected to normal fault *** data bank used for training the ANN includes all the critical soil parameters(cohesion,internal friction angle,Young’s modulus,and faulting).Furthermore,a mathematical formula is presented,based on biases and weights of the ANN *** results show that the maximum error of the presented formula is 2.03%,which makes the proposed technique efficiently predict the vertical displacement of buried pipelines and hence,helps to optimize the upcoming pipeline projects.

关键词： buried pipelines normal Fault finite element method multilayer perceptron neural network formulation

Applications of Soft Computing Methods in Backbreak Assessment in Surface Mines: A Comprehensive Review

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Modeling in Engineering & Sciences 2024年第9期140卷 2207-2238页

作者： Mojtaba Yari Manoj Khandelwal Payam Abbasi Evangelos I.Koutras danial Jahed Armaghani Panagiotis G.Asteris Department of Mining Engineering Faculty of EngineeringMalayer UniversityMalayer***Iran Institute of Innovation Science and SustainabilityFederation University AustraliaBallaratVIC 3350Australia Marane Olia Ornamental Stone Mine Sanandaj***Iran Computational Mechanics Laboratory School of Pedagogical and Technological EducationAthens15122Greece School of Civil and Environmental Engineering University of Technology SydneySydneyNSW 2007Australia

Geo-engineering problems are known for their complexity and high uncertainty levels,requiring precise defini-tions,past experiences,logical reasoning,mathematical analysis,and practical insight to address them *** Computing(SC)methods have gained popularity in engineering disciplines such as mining and civil engineering due to computer hardware and machine learning *** traditional hard computing approaches,SC models use soft values and fuzzy sets to navigate uncertain *** study focuses on the application of SC methods to predict backbreak,a common issue in blasting operations within mining and civil ***,which refers to the unintended fracturing of rock beyond the desired blast perimeter,can significantly impact project timelines and *** study aims to explore how SC methods can be effectively employed to anticipate and mitigate the undesirable consequences of blasting operations,specifically focusing on backbreak *** research explores the complexities of backbreak prediction and highlights the potential benefits of utilizing SC methods to address this challenging issue in geo-engineering projects.

关键词： Backbreak blasting soft computing methods prediction theory-guided machine learning

Volatility and Dynamic Herding in Energy Sector of Developed Markets During COVID-19:A Markov Regime-Switching Approach

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Fudan Journal of the Humanities and Social Sciences 2024年第1期17卷 115-138页

作者： Zuee Javaira Najam Us Sahar Syed danial Hashmi Iram Naz Federal Urdu University of Arts Science and Technology FUUASTIslamabadPakistan AFIPGM National University of Medical SciencesRawalpindiPakistan Faculty of Management Sciences Riphah International UniversityIslamabadPakistan College of Signals National University of Science and TechnologyIslamabadPakistan

This study examines a novel relationship between volatility and dynamic herding behavior during COVID-19 by examining the relationship of oil market volatility,Global volatility and Infectious disease equity market volatility with time-varying herding behavior in energy stock of Developed *** country level data,this study observes that market switch between anti-herding to herding state during pandemic and all three volatility measures have significant impact on dynamic herding state under high dispersion ***,in low dispersion regime only global volatility has significant impact on time-varying herding *** study suggests that the level of speculation in energy sector affect investor behavior;therefore,policy makers should monitor and model possible signals related to health crisis that can be transformed in to financial market crisis.

关键词： Herding Energy sector COVID-19 Volatility Markov regime approach

An Optimized System of Random Forest Model by Global Harmony Search with Generalized Opposition-Based Learning for Forecasting TBM Advance Rate

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Modeling in Engineering & Sciences 2024年第3期138卷 2873-2897页

作者： Yingui Qiu Shuai Huang danial Jahed Armaghani Biswajeet Pradhan Annan Zhou Jian Zhou School of Resources and Safety Engineering Central South UniversityChangsha410083China School of Civil and Environmental Engineering University of Technology SydneyNew South WalesAustralia Centre for Advanced Modelling and Geospatial Information Systems School of Civil and Environmental EngineeringFaculty of Engineering and Information TechnologyUniversity of Technology SydneyNew SouthWalesAustralia Civil and Infrastructure Engineering Discipline School of EngineeringRoyal Melbourne Institute of Technology(RMIT)MelbourneVictoria3001Australia

As massive underground projects have become popular in dense urban cities,a problem has arisen:which model predicts the best for Tunnel Boring Machine(TBM)performance in these tunneling projects?However,performance level of TBMs in complex geological conditions is still a great challenge for practitioners and *** the other hand,a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction *** performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine *** previously-proposed intelligent techniques in this field are mostly based on a single or base model with a low level of ***,this study aims to introduce a hybrid randomforest(RF)technique optimized by global harmony search with generalized oppositionbased learning(GOGHS)for forecasting TBM advance rate(AR).Optimizing the RF hyper-parameters in terms of,e.g.,tree number and maximum tree depth is the main objective of using the GOGHS-RF *** the modelling of this study,a comprehensive databasewith themost influential parameters onTBMtogetherwithTBM AR were used as input and output variables,*** examine the capability and power of the GOGHSRF model,three more hybrid models of particle swarm optimization-RF,genetic algorithm-RF and artificial bee colony-RF were also constructed to forecast TBM *** of the developed models was performed by calculating several performance indices,including determination coefficient(R2),root-mean-square-error(RMSE),and mean-absolute-percentage-error(MAPE).The results showed that theGOGHS-RF is a more accurate technique for estimatingTBMAR compared to the other applied *** newly-developedGOGHS-RFmodel enjoyed R2=0.9937 and 0.9844,respectively,for train and test stages,which are higher than a pre-developed ***,the importance of the input parameters was interpreted through the SHapley Additive exPlanations(SHA

关键词： Tunnel boring machine random forest GOGHS optimization PSO optimization GA optimization ABC optimization SHAP

Introduction to the Special Issue on Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Modeling in Engineering & Sciences 2024年第3期138卷 2023-2027页

作者： danial Jahed Armaghani Ahmed SalihMohammed Ramesh Murlidhar Bhatawdekar Pouyan Fakharian Ashutosh Kainthola Wael Imad Mahmood School of Civil and Environmental Engineering University of Technology SydneyUltimoNSW 2007Australia College of Engineering University of SulaimaniKurdistan46001Iraq Engineering Department American University of IraqSulaimaniKurdistan46001Iraq Centre of Tropical Geoengineering(GEOTROPIK) Institute of Smart Infrastructure and Innovative Engineering(ISIIC)Faculty of Civil EngineeringUniversiti Teknologi MalaysiaJohor Bahru81310Malaysia Faculty of Civil Engineering Semnan UniversitySemnanIran Geo-Engineering and Computing Laboratory Banaras Hindu UniversityVaranasi221005India Scientific Research Centre Soran UniversitySoranErbilKurdistan-Region44008Iraq

Computational Intelligent(CI)systems represent a pivotal intersection of cutting-edge technologies and complex engineering challenges aimed at solving real-world *** comprehensive body of work delves into the realm of CI,which is designed to tackle intricate and multifaceted engineering problems through advanced computational *** history of CI systems is a fascinating journey that spans several decades and has its roots in the development of artificial intelligence and machine learning *** a wide array of practical examples and case studies,this special issue bridges the gap between theoretical concepts and practical implementation,shedding light on how CI systems can optimize processes,design solutions,and inform decisions in complex engineering *** compilation stands as an essential resource for both novice learners and seasoned practitioners,offering a holistic perspective on the potential of CI in reshaping the future of engineering problem-solving.

关键词： offering bridges landscape

A machine learning-guided design and manufacturing of wearable nanofibrous acoustic energy harvesters

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Nano Research 2024年第10期17卷 9181-9192页

作者： Negar Hosseinzadeh Kouchehbaghi Maryam Yousefzadeh Aliakbar Gharehaghaji Safoora Khosravi danial Khorsandi Reihaneh Haghniaz Ke Cao Mehmet R.Dokmeci Mohammad Rostami Ali Khademhosseini Yangzhi Zhu Terasaki Institute for Biomedical Innovation Los AngelesCA 91367USA Department of Textile Engineering Amirkabir University of Technology(Tehran Polytechnic)Hafez Avenue*** TehranIran Department of Electrical and Computer Engineering University of British ColumbiaVancouverBC V6T1Z4Canada Chemical Sciences Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Department of Computer Science University of Southern CaliforniaLos AngelesCA 90007USA

Nanofibrous acoustic energy harvesters(NAEHs)have emerged as promising wearable platforms for efficient noise-to-electricity conversion in distributed power energy systems and wearable sound amplifiers for assistive listening ***,their reallife efficacy is hampered by low power output,particularly in the low-frequency range(<1 kHz).This study introduces a novel approach to enhance the performance of NAEHs by applying machine learning(ML)techniques to guide the synthesis of electrospun polyvinylidene fluoride(PVDF)/polyurethane(PU)nanofibers,optimizing their application in wearable *** use a feed-forward neural network along with solving an optimization problem to find the optimal input values of the electrospinning(applied voltage,nozzle-collector distance,electrospinning time,and drum rotation speed)to generate maximum output performance(acoustic-to-electricity conversion efficiency).We first prepared a dataset to train the network to predict the output power given the input variables with high *** introducing the neural network,we fix the network and then solve an optimization problem using a genetic algorithm to search for the input values that lead to the maximum energy harvesting *** ML-guided wearable PVDF/PU NAEH platform can deliver a maximal acoustoelectric power density output of 829μW/cm^(3) within the surrounding noise *** addition,our system can function stably in a broad frequency(0.1-2 kHz)with a high energy conversion efficiency of 66%.Sound recognition analysis reveals a robust correlation exceeding 0.85 among lexically akin terms with varying sound intensities,contrasting with a diminished correlation below 0.27 for words with disparate semantic ***,this work provides a previously unexplored route to utilize ML in advancing wearable NAEHs with excellent practicability.

关键词： wearable electronics acoustic energy harvester machine learning piezoelectric nanogenerator electrospun nanofiber

Enhancing rock fragmentation prediction in mining operations:A hybrid GWO-RF model with SHAP interpretability

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Central South University 2024年第8期31卷 2916-2929页

作者： ZHANG Yu-lin QIU Yin-gui ARMAGHANI danial Jahed MONJEZI Masoud ZHOU Jian School of Resources and Safety Engineering Central South UniversityChangsha 410083China School of Civil and Environmental Engineering University of Technology SydneyNew South Wales 2007Australia Department of Mining Faculty of EngineeringTarbiat Modares UniversityTehran 14115-143Iran State Key Laboratory of Precision Blasting Jianghan UniversityWuhan 430056China Hubei Key Laboratory of Blasting Engineering Jianghan UniversityWuhan 430056China

In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting *** this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hybrid predictive model named *** model combines the grey wolf optimization(GWO)algorithm with the random forest(RF)technique to predict the D_(80)value,a critical parameter in evaluating rock fragmentation *** study is conducted using a dataset from Sarcheshmeh Copper Mine,employing six different swarm sizes for the GWO-RF hybrid model *** GWO-RF model’s hyperparameters are systematically optimized within established bounds,and its performance is rigorously evaluated using multiple evaluation *** results show that the GWO-RF hybrid model has higher predictive skills,exceeding traditional models in terms of ***,the interpretability of the GWO-RF model is enhanced through the utilization of SHapley Additive exPlanations(SHAP)*** insights gained from this research contribute to optimizing blasting operations and rock fragmentation outcomes in the mining industry.

关键词： blasting rock fragmentation random forest grey wolf optimization hybrid tree-based technique