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

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

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Deciphering the controlling factors for phase transitions in zeolitic imidazolate frameworks

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National Science Review 2024年第4期11卷 151-163页

作者： Tao Du Shanwu Li Sudheer Ganisetti Mathieu Bauchy Yuanzheng Yue Morten M.Smedskjaer department of Chemistry and Bioscience Aalborg University department of Mechanical engineering-engineering Mechanics Michigan Technological University Physics of Amo Rphous and Inorganic Solids Laboratory(PARISlab) Department of Civil and Environmental EngineeringUniversity of CaliforniaLos Angeles

Zeolitic imidazolate frameworks（ZIFs） feature complex phase transitions, including polymorphism,melting, vitrification, and polyamorphism. Experimentally probing their structural evolution during transitions involving amorphous phases is a significant challenge, especially at the medium-range length scale. To overcome this challenge, here we first train a deep learning-based force field to identify the structural characteristics of both crystalline and non-crystalline ZIF phases. This allows us to reproduce the structural evolution trend during the melting of crystals and formation of ZIF glasses at various length scales with an accuracy comparable to that of ab initio molecular dynamics, yet at a much lower computational cost. Based on this approach, we propose a new structural descriptor, namely, the ring orientation index, to capture the propensity for crystallization of ZIF-4(Zn（Im）2, Im = C3H3N2-) glasses, as well as for the formation of ZIF-zni(Zn（Im）2) out of the high-density amorphous phase. This crystal formation process is a result of the reorientation of imidazole rings by sacrificing the order of the structure around the zinc-centered tetrahedra. The outcomes of this work are useful for studying phase transitions in other metal-organic frameworks（MOFs） and may thus guide the development of MOF glasses.

关键词： metal-organic frameworks phase transitions melting glass formation transferable deep learning force field ring orientation

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A novel low-cost visual ear tag based identification system for precision beef cattle livestock farming

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Information Processing in Agriculture 2024年第1期11卷 117-126页

作者： Andrea Pretto Gianpaolo Savio Flaviana Gottardo Francesca Uccheddu Gianmaria Concheri department of civil Environmental and Architectural EngineeringUniversity of PaduaItaly department of Animal Medicine Production and HealthUniversity of PadovaItaly department of Industrial engineering University of PadovaItaly

The precision livestock farming(PLF)has the objective to maximize each animal's performance while reducing the environmental impact and maintaining the quality and safety of meat *** the PLF techniques,the personalised management of each individual animal based on sensors systems,represents a viable *** is worth noting that the implementation of an effective PLF approach can be still expensive,especially for small and medium-sized farms;for this reason,to guarantee the sustainability of a customized livestock management system and encourage its use,plug and play and cost-effective systems are *** this context,we present a novel low-cost method for identifying beef cattle and recognizing their basic activities by a single surveillance *** leveraging the current state-of-the-art methods for real-time object detection,(i.e.,YOLOv3)cattle's face areas,we propose a novel mechanism able to detect the ear tag as well as the water ingestion state when the cattle is close to the *** cow IDs are read by an Optical Character Recognition(OCR)algorithm for which,an ad hoc error correction algorithm is here presented to avoid numbers misreading and correctly match the IDs to only actually present *** to the detection of the tag position,the OCR algorithm can be applied only to a specific region of interest reducing the computational cost and the time *** times for the areas are outputted as cattle activity recognition *** results demonstrate the effectiveness of our proposed method,showing a mAP@0.50 of 89%.

关键词： Precision livestock farming Deep learning Cattlei dentification Low-cost sensors Computer vision

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Journal of Oceanology and Limnology

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Journal of Oceanology and Limnology

作者： Song Sun

出版社：Chinese Society for Oceanology and Limnology

Journal of Oceanology and Limnology (JOL) (formerly Chinese Journal of Oceanology and Limnology (CJOL)) was established in 1982 under the auspices of Chinese Society for Oceanology and Limnology and managed by Institute of Oceanology, Chinese Academy of Sciences. It is an international peer-reviewed bimonthly journal and co-published by Science Press, China, and Springer.Journal of Oceanology and Limnology (JOL) aims to disseminate original research articles in the forms of review, research paper, and note. JOL welcomes high-quality articles on aspects of oceanology (oceanography) and limnology from all over the world, including those in academic coverage: biology (bioinformatics, bioengineering), physics (mathematical and computational physics), geology (sedimentology, geophysics, paleontology, geochemistry), chemistry (geochemistry, biochemistry), hydrology, meteorology, and geography; and those in practical coverage: aquaculture, marine resource exploration, remote-sensing, environmental protection, marine engineering, pharmacology, and instrumentation. Manuscripts submitted are considered only on the condition that the contents are not plagiarized nor published elsewhere previously, and must be complied with ethical standards. Articles submitted shall state explicitly the objectives and novelty. The articles shall be written in good English and smooth logic, and well understood by the broad audience.

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Kinetic-Thermodynamic Promotion engineering toward High-Density Hierarchical and Zn-Doping Activity-Enhancing ZnNiO@CF for High-Capacity Desalination

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Nano-Micro Letters 2024年第8期16卷 35-50页

作者： Jie Ma Siyang Xing Yabo Wang Jinhu Yang Fei Yu College of Marine Ecology and Environment Shanghai Ocean University201306 ShanghaiPeople’s Republic of China School of civil engineering Kashi University844000 KashiPeople’s Republic of China Research Center for environmental Functional Materials College of Environmental Science and EngineeringTongji University1239 Siping Road200092 ShanghaiPeople’s Republic of China School of Chemical Science and engineering Tongji University1239 Siping Road200092 ShanghaiPeople’s Republic of China department of Energy Environmental&Chemical EngineeringWashington University in St.LouisSt.LouisMO 63130USA

Despite the promising potential of transition metal oxides(TMOs)as capacitive deionization(CDI)electrodes,the actual capacity of TMOs electrodes for sodium storage is significantly lower than the theoretical capacity,posing a major ***,we prepared the kinetically favorable Zn_(x)Ni_(1−x)O electrode in situ growth on carbon felt(Zn_(x)Ni_(1−x)O@CF)through constraining the rate of OH^(−)generation in the hydrothermal ***_(x)Ni_(1−x)O@CF exhibited a high-density hierarchical nanosheet structure with three-dimensional open pores,benefitting the ion transport/electron *** tuning the moderate amount of redox-inert Zn-doping can enhance surface electroactive sites,actual activity of redox-active Ni species,and lower adsorption energy,promoting the adsorption kinetic and thermodynamic of the Zn_(0.2)Ni_(0.8)O@*** from the kinetic-thermodynamic facilitation mechanism,Zn_(0.2)Ni_(0.8)O@CF achieved ultrahigh desalination capacity(128.9 mgNaCl g^(-1)),ultra-low energy consumption(0.164 kW h kgNaCl^(-1)),high salt removal rate(1.21 mgNaCl g^(-1) min^(-1)),and good *** thermodynamic facilitation and Na^(+)intercalation mechanism of Zn_(0.2)Ni_(0.8)O@CF are identified by the density functional theory calculations and electrochemical quartz crystal microbalance with dissipation monitoring,*** research provides new insights into controlling electrochemically favorable morphology and demonstrates that Zn-doping,which is redox-inert,is essential for enhancing the electrochemical performance of CDI electrodes.

关键词： Zinc-nickel metal oxide High-density hierarchical Capacitive deionization Zinc-doping

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Tuning Surface Molecular Design of Porous Carbon for Blue Energy Harvesting

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Research 2024年第1期2023卷 651-662页

作者： Jian Yu Zhong-Lin Wang Tianwei Ma department of civil and environmental engineering University of Hawaii at MānoaHonoluluHI 96822USA Beijing Institute of Nanoenergy and Nanosystems Chinese Academy of SciencesBeijing 101400China College of engineering Texas A&M University-Corpus ChristiCorpus ChristiTX 78412USA

Capacitive mixing is a promising blue energy technology due to its membrane-free electricity generation and long electrode life ***,because of limited performance,existing systems do not lend themselves to practical *** it is a crucial factor directly influencing electrode behavior,surface chemistry has largely been overlooked in capacitive ***,we show that manipulating surface functionalization alone can tune the responses of electrodes to produce a high voltage rise without altering the pore structure of the *** findings reveal that the spontaneous electrode potential of a surface-modified carbon electrode shifts negatively proportional to the surface charge due to the surface groups,which explains why and how manipulating the surface chemistry can improve the power generation capacity.

关键词： electrode Porous mixing

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Renewable biosynthesis of isoprene from wastewater through a synthetic biology approach:the role of individual organic compounds

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Frontiers of environmental Science & engineering 2024年第3期18卷 13-24页

作者： Min Yang Xianghui Li Weixiang Chao Xiang Gao Huan Wang Lu Lu State Key Laboratory of Urban Water Resource and Environment School of Civil and Environmental EngineeringHarbin Institute of TechnologyShenzhen 518055China Center for Materials Synthetic Biology CAS Key Laboratory of Quantitative Engineering Biology of CASShenzhen Institute of Synthetic BiologyShenzhen Institutes of Advanced TechnologyChinese Academic of ScienceShenzhen 518000China Institute of Urban Ecology and environmental Technology School of Materials and Environmental EngineeringShenzhen Polytechnic UniversityShenzhen 518055China

The biosynthesis of isoprene offers a more sustainable alternative to fossil fuel-based approaches,yet its success has been largely limited to pure organic compounds and the cost remains a *** study proposes a waste-to-wealth strategy for isoprene biosynthesis utilizing genetically engineered *** bacteria to convert organic waste from real food *** impact of organic compounds present in wastewater on *** growth and isoprene production was systematically *** results demonstrated that with filtration pretreatment of wastewater,isoprene yield,and production achieved 115 mg/g COD and 7.1 mg/(L·h),***,even without pretreatment,isoprene yield only decreased by~24%,indicating promising ***,maltose,glycerol,and lactate are effective substrates for isoprene biosynthesis,whereas starch,protein,and acetate do not support *** *** optimum C/N ratio for isoprene production was found to be 8:***,augmenting essential nutrients in wastewater elevated the isoprene yield increased to 159 mg/g *** wastewater biosynthesis significantly reduced the cost(44%–53%decrease,p-value<0.01)and CO_(2)emission(46%–55%decrease,p-value<0.01)compared with both sugar fermentation and fossil fuel–based *** study introduced a more sustainable and economically viable approach to isoprene synthesis,offering an avenue for resource recovery from wastewater.

关键词： Wastewater Resource recovery Genetic engineering Biosynthesis Isoprene

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Review of Recent Trends in the Hybridisation of Preprocessing-Based and Parameter Optimisation-Based Hybrid Models to Forecast Univariate Streamflow

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工程与科学中的计算机建模(英文) 2024年第1期138卷 1-41页

作者： Baydaa Abdul Kareem Salah L.Zubaidi Nadhir Al-Ansari Yousif Raad Muhsen department of civil engineering University of MaysanMaysan57000Iraq department of civil engineering Wasit UniversityWasit52001Iraq College of engineering University ofWarith Al-AnbiyaaKarbala56001Iraq department of civil environmental and Natural Resources engineering Lulea University of TechnologyLulea97187Sweden department of Computer Science Faculty of Computer Science and Information TechnologyUniversiti Putra MalaysiaSerdang43400Malaysia

Forecasting river flow is crucial for optimal planning,management,and sustainability using freshwater *** machine learning(ML)approaches have been enhanced to improve streamflow *** techniques have been viewed as a viable method for enhancing the accuracy of univariate streamflow estimation when compared to standalone *** researchers have also emphasised using hybrid models to improve forecast ***,this paper conducts an updated literature review of applications of hybrid models in estimating streamflow over the last five years,summarising data preprocessing,univariate machine learning modelling strategy,advantages and disadvantages of standalone ML techniques,hybrid models,and performance *** study focuses on two types of hybrid models:parameter optimisation-based hybrid models(OBH)and hybridisation of parameter optimisation-based and preprocessing-based hybridmodels(HOPH).Overall,this research supports the idea thatmeta-heuristic approaches precisely improveML ***’s also one of the first efforts to comprehensively examine the efficiency of various meta-heuristic approaches(classified into four primary classes)hybridised with ML *** study revealed that previous research applied swarm,evolutionary,physics,and hybrid metaheuristics with 77%,61%,12%,and 12%,***,there is still room for improving OBH and HOPH models by examining different data pre-processing techniques and metaheuristic algorithms.

关键词： Univariate streamflow machine learning hybrid model data pre-processing performance metrics

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Exploring the Applications of Digital Twin Technology in Enhancing Sustainability in civil engineering:A Review

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Structural Durability & Health Monitoring 2024年第5期18卷 577-598页

作者： Jiamin Huang Ping Wu Wangxin Li Jun Zhang Yidong Xu School of civil engineering Chongqing Jiaotong UniversityChongqing400074China School of civil engineering and Architecture NingboTech UniversityNingbo315100China School of civil engineering Lanzhou University of TechnologyLanzhou730050China

With the advent of the big data era and the rise of Industrial Revolution 4.0,digital twins(DTs)have gained sig-niﬁcant attention in various *** offer the opportunity to combine the physical and digital worlds and aid the digital transformation of the civil engineering *** this paper,605 documents obtained from the search wereﬁrst analysed using CiteSpace for literature visualisation,and an author co-occurrence network,a keyword co-occurrence network,and a keyword clustering set were ***,through a literature review of 86 papers,this paper summarises the current status of DT application in civil engineering based on a review of the origins,concepts,and implementation techniques of DTs,and it introduces the application of DTs in the full project *** study shows that DTs have great potential to address many of the challenges faced by civil *** this regard,the paper also presents some thoughts on the future directions of DT research.

关键词： Digital twins civil engineering full lifecycle literature visualisation analysis

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Bayesian Optimized LightGBM model for predicting the fundamental vibrational period of masonry infilled RC frames

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Frontiers of Structural and civil engineering 2024年第7期18卷 1084-1102页

作者： Taimur RAHMAN Pengfei ZHENG Shamima SULTANA department of civil engineering World University of BangladeshDhaka 1230Bangladesh School of civil engineering Zhengzhou UniversityZhengzhou 450001China department of Computer Science&engineering University of Asia PacificDhaka 1205Bangladesh

The precise prediction of the fundamental vibrational period for reinforced concrete(RC)buildings with infilled walls is essential for structural design,especially earthquake-resistant *** learning models from previous studies,while boasting commendable accuracy in predicting the fundamental period,exhibit vulnerabilities due to lengthy training times and inherent dependence on pre-trained models,especially when engaging with continually evolving data *** predicament emphasizes the necessity for a model that adeptly balances predictive accuracy with robust adaptability and swift data *** latter should include consistent re-training ability as demanded by realtime,continuously updated data *** research implements an optimized Light Gradient Boosting Machine(LightGBM)model,highlighting its augmented predictive capabilities,realized through the astute use of Bayesian Optimization for hyperparameter tuning on the FP4026 research data set,and illuminating its adaptability and efficiency in predictive *** results show that the R^(2) score of LightGBM model is 0.9995 and RMSE is 0.0178,while training speed is 23.2 times faster than that offered by XGBoost and 45.5 times faster than for Gradient ***,this study introduces a practical application through a streamlit-powered,web-based dashboard,enabling engineers to effortlessly utilize and augment the model,contributing data and ensuring precise fundamental period predictions,effectively bridging scholarly research and practical applications.

关键词： masonry-infilled RC frame fundamental period LightGBM FP4026 research dataset machine learning data-driven approach Bayesian Optimization

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