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Smart prediction of liquefaction-induced lateral spreading

Journal of Rock Mechanics and Geotechnical Engineering 2024年第6期16卷 2310-2325页

作者： muhammad nouman amjad raja Tarek Abdoun Waleed El-Sekelly New York University(NYU)Abu Dhabi Abu Dhabi129188United Arab Emirates University of Management and Technology Lahore54372Pakistan Department of Civil and Environmental Eng. Rensselaer Polytechnic Institute(RPI)1108th StreetJEC 4049TroyNY12180USA Department of Structural Engineering Mansoura UniversityMansoura35516Egypt

The prediction of liquefaction-induced lateral spreading/displacement(Dh)is a challenging task for civil/geotechnical *** this study,a new approach is proposed to predict Dh using gene expression programming(GEP).Based on statistical reasoning,individual models were developed for two topographies:free-face and gently sloping *** with a comparison with conventional approaches for predicting the Dh,four additional regression-based soft computing models,*** process regression(GPR),relevance vector machine(RVM),sequential minimal optimization regression(SMOR),and M5-tree,were developed and compared with the GEP *** results indicate that the GEP models predict Dh with less bias,as evidenced by the root mean square error(RMSE)and mean absolute error(MAE)for training(i.e.1.092 and 0.815;and 0.643 and 0.526)and for testing(i.e.0.89 and 0.705;and 0.773 and 0.573)in free-face and gently sloping ground topographies,*** overall performance for the free-face topology was ranked as follows:GEP>RVM>M5-tree>GPR>SMOR,with a total score of 40,32,24,15,and 10,*** the gently sloping condition,the performance was ranked as follows:GEP>RVM>GPR>M5-tree>SMOR with a total score of 40,32,21,19,and 8,***,the results of the sensitivity analysis showed that for both free-face and gently sloping ground,the liquefiable layer thickness(T_(15))was the major parameter with percentage deterioration(%D)value of 99.15 and 90.72,respectively.

关键词： Lateral spreading Intelligent modeling Gene expression programming(GEP) Closed-form solution Feature importance

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Experimental study on repeatedly loaded foundation soil strengthened by wraparound geosynthetic reinforcement technique

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Journal of Rock Mechanics and Geotechnical Engineering 2021年第4期13卷 899-911页

作者： muhammad nouman amjad raja Sanjay Kumar Shukla Geotechnical and Geoenvironmental Engineering Research Group School of EngineeringEdith Cowan UniversityJoondalupPerthWA 6027Australia Department of Civil Engineering School of EngineeringUniversity of Management and TechnologyLahorePakistan Civil Engineering Department Delhi Technological UniversityDelhiIndia

In the recent past,the potential benefits of wraparound geosynthetic reinforcement technique for constructing the reinforced soil foundations have been *** paper presents the experimental study on the behaviour of model strip footing resting on sandy soil bed reinforced with geosynthetic in wraparound and planar forms under monotonic and repeated *** geosynthetic layers were laid according to the reinforcement ratio to minimise the scale *** is found that for the same amount of reinforcement material,the wraparound reinforced model resulted in less settlement in comparison to planar reinforced *** efficiency of wraparound reinforced model increased with the increase in load amplitude and the rate of total cumulative settlement substantially decreased with the increase in number of load *** wraparound reinforced model has shown about 45% lower average total settlement in comparison to unreinforced model,while the double-layer reinforced model has about 41% lower average total settlement at the cost of approximately twice the material and 1.5 times the occupied land width ***,wraparound models have shown much greater stability in comparison to their counterpart models when subjected to incremental repeated loading.

关键词： Geosynthetics Repeated loads Wraparound reinforcement technique Model tests Footing settlement Bearing capacity Reinforcement ratio Strip footing

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Predicting and validating the load-settlement behavior of large-scale geosynthetic-reinforced soil abutments using hybrid intelligent modeling

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Journal of Rock Mechanics and Geotechnical Engineering 2023年第3期15卷 773-788页

作者： muhammad nouman amjad raja Syed Taseer Abbas Jaffar Abidhan Bardhan Sanjay Kumar Shukla Department of Civil Engineering School of EngineeringUniversity of Management and TechnologyLahorePakistan Department of Civil Engineering National Institute of Technology(NIT)PatnaPatnaBihar800005India Geotechnical and Geoenvironmental Research Group School of EngineeringEdith Cowan UniversityJoondalupPerthAustralia Department of Civil Engineering Delhi Technological UniversityDelhiIndia

Settlement prediction of geosynthetic-reinforced soil(GRS)abutments under service loading conditions is an arduous and challenging task for practicing geotechnical/civil ***,in this paper,a novel hybrid artificial intelligence(AI)-based model was developed by the combination of artificial neural network(ANN)and Harris hawks’optimisation(HHO),that is,ANN-HHO,to predict the settlement of the GRS *** other robust intelligent models such as support vector regression(SVR),Gaussian process regression(GPR),relevance vector machine(RVM),sequential minimal optimisation regression(SMOR),and least-median square regression(LMSR)were constructed and compared to the ANN-HHO *** predictive strength,relalibility and robustness of the model were evaluated based on rigorous statistical testing,ranking criteria,multi-criteria approach,uncertainity analysis and sensitivity analysis(SA).Moreover,the predictive veracity of the model was also substantiated against several large-scale independent experimental studies on GRS abutments reported in the scientific *** acquired findings demonstrated that the ANN-HHO model predicted the settlement of GRS abutments with reasonable accuracy and yielded superior performance in comparison to counterpart ***,it becomes one of predictive tools employed by geotechnical/civil engineers in preliminary decision-making when investigating the in-service performance of GRS ***,the model has been converted into a simple mathematical formulation for easy hand calculations,and it is proved cost-effective and less time-consuming in comparison to experimental tests and numerical simulations.

关键词： Geosynthetic-reinforced soil(GRS) abutments Settlement estimation Predictive modeling Artificial intelligence(AI) Artificial neural network(ANN)-Harris hawks’optimisation(HHO)

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Intrusion Detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

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Computers, Materials & Continua 2023年第1期74卷 801-815页

作者： Yongjie Yang Shanshan Tu raja Hashim Ali Hisham Alasmary muhammad Waqas muhammad nouman amjad Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Information Security and Cybersecurity Unit King Khalid UniversityAbhaSaudi Arabia Computer Engineering Department College of Information TechnologyUniversity of Bahrain32038Bahrain School of Engineering Edith Cowan UniversityJoondalup PerthWA6027Australia School of Engineering University of Management and TechnologyLahorePakistan

With the recent developments in the Internet of Things(IoT),the amount of data collected has expanded tremendously,resulting in a higher demand for data storage,computational capacity,and real-time processing *** computing has traditionally played an important role in establishing ***,fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility,location awareness,heterogeneity,scalability,low latency,and geographic ***,IoT networks are vulnerable to unwanted assaults because of their open and shared *** a result,various fog computing-based security models that protect IoT networks have been developed.A distributed architecture based on an intrusion detection system(IDS)ensures that a dynamic,scalable IoT environment with the ability to disperse centralized tasks to local fog nodes and which successfully detects advanced malicious threats is *** this study,we examined the time-related aspects of network traffic *** presented an intrusion detection model based on a twolayered bidirectional long short-term memory(Bi-LSTM)with an attention mechanism for traffic data classification verified on the UNSW-NB15 benchmark *** showed that the suggested model outperformed numerous leading-edge Network IDS that used machine learning models in terms of accuracy,precision,recall and F1 score.

关键词： Fog computing intrusion detection bi-LSTM attention mechanism

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