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Prediction of bearing capacity of pile foundation using deep learning approaches

Frontiers of Structural and Civil Engineering 2024年第6期18卷 870-886页

作者： Manish KUMAR Divesh Ranjan KUMAR Jitendra KHATTi Pijush SAMUi kamaldeep Singh GROVER Department of Civil Engineering SRM Institute of Science and Technology Tiruchirappalli CampusTrichy 621105India Department of Civil Engineering National Institute of Technology PatnaBihar 800005India Department of Civil Engineering Faculty of EngineeringThammasat School of EngineeringThammasat UniversityBangkok10200Thailand Department of Civil Engineering Rajasthan Technical UniversityKota 324010India

The accurate prediction of bearing capacity is crucial in ensuring the structural integrity and safety of pile *** research compares the Deep Neural Networks(DNN),Convolutional Neural Networks(CNN),Recurrent Neural Networks(RNN),Long Short-Term Memory(LSTM),and Bidirectional LSTM(BiLSTM)algorithms utilizing a data set of 257 dynamic pile load tests for the first ***,this research illustrates the multicollinearity effect on DNN,CNN,RNN,LSTM,and BiLSTM models’performance and accuracy for the first time.A comprehensive comparative analysis is conducted,employing various statistical performance parameters,rank analysis,and error matrix to evaluate the performance of these *** performance is further validated using external validation,and visual interpretation is provided using the regression error characteristics(REC)curve and Taylor *** from the comparative analysis reveal that the DNN(Coefficient of determination(R^(2))_(training(TR))=0.97,root mean squared error(RMSE)_(TR)=0.0413;R^(2)_(testing(TS))=0.9,RMSE_(TS)=0.08)followed by BiLSTM(R^(2)_(TR)=0.91,RMSE_(TR)=0.782;R^(2)_(TS)=0.89,RMSE_(TS)=0.0862)model demonstrates the highest performance *** is noted that the BiLSTM model is better than LSTM because the BiLSTM model,which increases the amount of information for the network,is a sequence processing model made up of two LSTMs,one of which takes the input in a forward manner,and the other in a backward *** prediction of pile-bearing capacity is strongly influenced by ram weight(having a considerable multicollinearity level),and the effect of the considerable multicollinearity level has been determined for the model based on the recurrent neural network *** this study,the recurrent neural network model has the least performance and accuracy in predicting the pile-bearing capacity.

关键词： deep learning algorithms high-strain dynamic pile test bearing capacity of the pile

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Urban heat mitigation by green and blue infrastructure:Drivers,effectiveness,and future needs

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The innovation 2024年第2期5卷 145-166页

作者： Prashant Kumar Sisay E.Debele Soheila Khalili Christos H.Halios Jeetendra Sahani Nasrin Aghamohammadi Maria de Fatima Andrade Maria Athanassiadou kamaldeep bh.i Nerea Calvillo Shi-Jie Cao Frederic Coulon Jill L.Edmondson David Fletcher Edmilson Dias de Freitas Hai Guo Matthew C.Hort Madhusudan Katti Thomas Rodding Kjeldsen Steffen Lehmann Giuliano Maselli Locosselli Shelagh K.Malham Lidia Morawska Rajan Parajuli Christopher D.F.Rogers Runming Yao Fang Wang Jannis Wenk Laurence Jones Global Centre for Clean Air Research(GCARE) School of SustainabilityCivil and Environmental EngineeringFaculty of Engineering and Physical SciencesUniversity of SurreyGuildford GU27XHUK institute for Sustainability University of SurreyGuildford GU27XHSurreyUK School of Built Environment University of ReadingWhiteknightsReading RG66BUUK School Design and the Built Environment Curtin University Sustainability Policy InstituteKent StBentley 6102Western Australia Harry Butler institute Murdoch UniversityMurdoch 6150Western Australia Atmospheric Sciences Department Institute of AstronomyGeophysics and Atmospheric SciencesUniversity of Sao PauloSao Paulo 05508-090Brazil Met Office FitzRoy RoadExeter EX13PBUK Department of Psychiatry and Nuffield Department of Primary Care Health Sciences Wadham CollegeUniversity of OxfordOxfordUK Centre for interdisciplinary Methodologies University of WarwickWarwickUK School of Architecture Southeast University2 SipailouNanjing 210096China Cranfield University School of WaterEnvironment and EnergyCranfield MK430ALUK Plants PhotosynthesisSoil ClusterSchool of BiosciencesUniversity of SheffieldSheffield S102TNUK UK Centre for Ecology&Hydrology Environment Centre WalesDeiniol RoadBangor LL572UWUK Air Quality Studies Department of Civil and Environmental EngineeringThe Hong Kong Polytechnic UniversityHong KongChina Department of Forestry and Environmental Resources Faculty Excellence Program for Leadership in Public ScienceNorth Carolina State UniversityChancellorRaleighNC 27695USA Departments of Architecture&Civil Engineering and Chemical EngineeringUniversity of BathBath BA27AYUK School of Architecture University of NevadaLas VegasNV 89154USA Department of Tropical Ecosystems Functioning Center of Nuclear Energy in AgricultureUniversity of São PauloPiracicaba 13416-000Sao PauloBrazil School of Ocean Sciences Bangor UniversityMenai BridgeAnglesey LL595 ABUK international Laboratory for Air Quality and Health Science and Engine

The combination of urbanization and global warming leads to urban overheating and compounds the frequency and intensity of extreme heat events due to climate ***,the risk of urban overheating can be mitigated by urban green-blue-grey infrastructure(GBGi),such as parks,wetlands,and engineered greening,which have the potential to effectively reduce summer air *** many reviews,the evidence bases on quantified GBGi cooling benefits remains partial and the practical recommendations for implementation are *** systematic literature review synthesizes the evidence base for heat mitigation and related co-benefits,identifies knowledge gaps,and proposes recommendations for their implementation to maximize their *** screening 27,486 papers,202 were reviewed,based on 51 GBGi types categorized under 10 main *** GBGi(green walls,parks,street trees)have been well researched for their urban cooling ***,several other GBGi have received negligible(zoological garden,golf course,estuary)or minimal(private garden,allotment)*** most efficient air cooling was observed in botanical gardens(5.0±3.5℃),wetlands(4.9±3.2℃),green walls(4.1±4.2℃),street trees(3.8±3.1℃),and vegetated balconies(3.8±2.7℃).Under changing climate conditions(2070–2100)with consideration of RCP8.5,there is a shift in climate subtypes,either within the same climate zone(e.g.,Dfa to Dfb and Cfb to Cfa)or across other climate zones(e.g.,Dfb[continental warm-summer humid]to BSk[dry,cold semi-arid]and Cwa[temperate]to Am[tropical]).These shifts may result in lower efficiency for the current GBGi in the *** the importance of multiple services,it is crucial to balance their functionality,cooling performance,and other related co-benefits when planning for the future *** global GBGi heat mitigation inventory can assist policymakers and urban planners in prioritizing effective interventions to reduce the risk of urban overheating,fi

关键词： estuary walls extreme

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Prediction of compaction parameters for fine-grained soil: Critical comparison of the deep learning and standalone models

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Journal of Rock Mechanics and Geotechnical Engineering 2023年第11期15卷 3010-3038页

作者： Jitendra Khatti kamaldeep Singh Grover Department of Civil Engineering Rajasthan Technical UniversityKota324010RajasthanIndia

A comparison between deep learning and standalone models in predicting the compaction parameters of soil is presented in this *** hundred and ninety and fifty-three soil samples were randomly picked up from two hundred and forty-three soil samples to create training and validation datasets,*** performance and accuracy of the models were measured by root mean square error(RMSE),coefficient of determination(R2),Pearson product-moment correlation coefficient(r),mean absolute error(MAE),variance accounted for(VAF),mean absolute percentage error(MAPE),weighted mean absolute percentage error(WMAPE),a20-index,index of scatter(iOS),and index of agreement(iOA).Comparisons between standalone models demonstrate that the model MD 29 in Gaussian process regression(GPR)and model MD 101 in support vector machine(SVM)can achieve over 96%of accuracy in predicting the optimum moisture content(OMC)and maximum dry density(MDD)of soil,and outperformed other standalone *** comparison between deep learning models shows that the models MD 46 and MD 146 in long short-term memory(LSTM)predict OMC and MDD with higher accuracy than ANN ***,the LSTM models outperformed the GPR models in predicting the compaction *** sensitivity analysis illustrates that fine content(FC),specific gravity(SG),and liquid limit(LL)highly influence the prediction of compaction parameters.

关键词： Artificial intelligence(Ai) Anderson-darling(AD)test Compaction parameters Fine-grained soil Soft computing Score analysis

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intrapericardial Paraganglioma, an Exceedingly Rare Finding: A Case Report

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Case Reports in Clinical Medicine 2023年第7期12卷 251-255页

作者： Alvaro Altamirano Roshanak Habibi kamaldeep Singh Govinda Adhikari Department of internal Medicine Banner University Medical Center Tucson USA

Paragangliomas are rare tumors that arise from the sympathetic or the parasympathetic ganglia. Parasympathetic paragangliomas are usually nonfunctional. They are often found in the pre aortic and paravertebral sympathetic plexus or at the base of the skull. 80% of these are Glomus jugulare and carotid body paragangliomas. intrapericardial paragangliomas are exceedingly rare. Less than 2% of paragangliomas are found in the chest and most of them are situated in the posterior mediastinum. As such, there are no clear guidelines on how to treat intrapericardial paragangliomas. We present here the case of a patient with an intrapericardial paraganglioma who was evaluated by cardiology, cardiac surgery, and endocrinology. in this case cardiac surgery opted for a conservative approach and did not offer surgery to the patient. There is some literature supporting a surgical approach but no specific guidelines about this have been written. The patient is currently being monitored since she did not have metastatic disease and her paraganglioma was nonfunctioning. With this case we want to add to the body of evidence that this type of cases does not need an urgent surgical approach.

关键词： Paraganglioma intrapericardial Nonfunctioning

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基于NFC的嵌入式系统自检

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单片机与嵌入式系统应用 2019年第11期19卷 90-91页

作者： kamaldeep Bansal Alok Mittal 意法半导体

引言今天,电子产品堪称无处不在,不管是汽车、白色家电,还是娱乐设备、可穿戴设备,都已融入我们生活的方方方面。电子系统的快速普及应用,归功于大规模集成电子器件的出现,例如,非常复杂的计算密集型微控制器和SoC(系统芯片)。随着白色... 详细信息

引言今天,电子产品堪称无处不在,不管是汽车、白色家电,还是娱乐设备、可穿戴设备,都已融入我们生活的方方方面。电子系统的快速普及应用,归功于大规模集成电子器件的出现,例如,非常复杂的计算密集型微控制器和SoC(系统芯片)。随着白色家电和电子产品设计日益复杂,设计师不得不开始关注产品的易用性和排障的便利性。复杂设计急需内部调试信息,需要了解计算单元内部发生的情况,如果出现系统错误或失败,可以在产品生命周期的各个阶段检索和检查错误,如图1所示。

关键词：系统自检嵌入式 NFC 电子产品集成电子器件产品生命周期白色家电娱乐设备

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MSH1 is a Plant Organellar DNA Binding and Thylakoid Protein under Precise Spatial Regulation to Alter Development

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Molecular Plant 2016年第2期9卷 245-260页

作者： kamaldeep S. Virdi Yashitola Wamboldt Hardik Kundariya John D. Laurie ido Keren K.R. Sunil Kumar Anna Block Gilles Basset Steve Luebker Christian Elowsky Philip M. Day Johnna L. Roose Terry M. Bricker Thomas Elthon Sally A. Mackenzie School of Biological Sciences Department of Agronomy and Horticulture University of Nebraska Lincoln NE 68588 USA Center for Biotechnology University of Nebraska Lincoln NE 68588 USA Department of Biological Sciences Louisiana State University Baton Rouge LA 70803 USA These authors contributed equally to this article

As metabolic centers, plant organelles participate in maintenance, defense, and signaling. MSH1 is a plant- specific protein involved in organeUar genome stability in mitochondria and plastids. Plastid depletion of MSH1 causes heritable, non-genetic changes in development and DNA methylation. We investigated the rash i phenotype using hemi-complementation mutants and transgene-null segregants from RNAi suppres- sion lines to sub-compartmentalize MSH1 effects. We show that MSH1 expression is spatially regulated, specifically localizing to plastids within the epidermis and vascular parenchyma. The protein binds DNA and localizes to plastid and mitochondrial nucleoids, but fractionation and protein-protein interactions data indicate that MSH1 also associates with the thylakoid membrane. Plastid MSH1 depletion results in variegation, abiotic stress tolerance, variable growth rate, and delayed maturity. Depletion from mitochon- dria results in 7%-10% of plants altered in leaf morphology, heat tolerance, and mitochondrlal genome sta- bility. MSH1 does not localize within the nucleus directly, but plastid depletion produces non-genetic changes in flowering time, maturation, and growth rate that are heritable independent of MSH 1. MSH1 deple- tion alters non-photoactive redox behavior in plastids and a sub-set of mitochondrially altered lines. Ectopic expression produces deleterious effects, underlining its strict expression control. Unraveling the complexity of the MSH1 effect offers insight into triggers of plant-specific, transgenerational adaptation behaviors.

关键词： Thylakoid protein Organellar DNA binding MSH1 Epigenetic variation

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