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Compressive strength prediction and optimization design of sustainable concrete based on squirrel search algorithm-extreme gradient boosting technique

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Frontiers of Structural and Civil Engineering 2023年第9期17卷 1310-1325页

作者： Enming Li Ning ZHANG Bin XI Jian ZHOU Xiaofeng GAO ETSI Minas y Energía Universidad Politécnica de MadridMadrid 28003Spain Leibniz Institute of Ecological Urban and Regional Development(IOER) Dresden 01217Germany Department of Civil and Environmental Engineering Politecnico Di MilanoMilano 20133Italy School of Resources and Safety Engineering Central South UniversityChangsha 410083China Key Laboratory of the Three Gorges Reservoir Region’s Eco-Environment(Ministry of Education) College of Environment and EcologyChongqing UniversityChongqing 400045China

Concrete is the most commonly used construction material.However,its production leads to high carbon dioxide(CO_(2))emissions and energy consumption.Therefore,developing waste-substitutable concrete components is necessary.Improving the sustainability and greenness of concrete is the focus of this research.In this regard,899 data points were collected from existing studies where cement,slag,fly ash,superplasticizer,coarse aggregate,and fine aggregate were considered potential influential factors.The complex relationship between influential factors and concrete compressive strength makes the prediction and estimation of compressive strength difficult.Instead of the traditional compressive strength test,this study combines five novel metaheuristic algorithms with extreme gradient boosting(XGB)to predict the compressive strength of green concrete based on fly ash and blast furnace slag.The intelligent prediction models were assessed using the root mean square error(RMSE),coefficient of determination(R^(2)),mean absolute error(MAE),and variance accounted for(VAF).The results indicated that the squirrel search algorithm-extreme gradient boosting(SSA-XGB)yielded the best overall prediction performance with R^(2) values of 0.9930 and 0.9576,VAF values of 99.30 and 95.79,MAE values of 0.52 and 2.50,RMSE of 1.34 and 3.31 for the training and testing sets,respectively.The remaining five prediction methods yield promising results.Therefore,the developed hybrid XGB model can be introduced as an accurate and fast technique for the performance prediction of green concrete.Finally,the developed SSA-XGB considered the effects of all the input factors on the compressive strength.The ability of the model to predict the performance of concrete with unknown proportions can play a significant role in accelerating the development and application of sustainable concrete and furthering a sustainable economy.

关键词： sustainable concrete fly ash slay extreme gradient boosting technique squirrel search algorithm parametric analysis

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A comprehensive comparison of different regression techniques and nature-inspired optimization algorithms to predict carbonation depth of recycled aggregate concrete

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Frontiers of Structural and Civil Engineering 2024年第1期18卷 30-50页

作者： Bin XI Ning ZHANG Enming Li Jiabin li Jian ZHOU Pablo SEGARRA Department of Civil and Environmental Engineering Politecnico Di MilanoMilano 20133Italy Leibniz Institute of Ecological Urban and Regional Development(IOER) Dresden 01217Germany Universidad Politécnica de Madrid-ETSI Minas y Energía Madrid 28003Spain Department of Civil Engineering KU Leuven CampusBrugge 8200Belgium School of Resources and Safety Engineering Central South UniversityChangsha 410083China

The utilization of recycled aggregates(RA)for concrete production has the potential to offer substantial environmental and economic advantages.However,RA concrete is plagued with considerable durability concerns,particularly carbonation.To advance the application of RA concrete,the establishment of a reliable model for predicting the carbonation is needed.On the one hand,concrete carbonation is a long and slow process and thus consumes a lot of time and energy to monitor.On the other hand,carbonation is influenced by many factors and is hard to predict.Regarding this,this paper proposes the use of machine learning techniques to establish accurate prediction models for the carbonation depth(CD)of RA concrete.Three types of regression techniques and meta-heuristic algorithms were employed to provide more alternative predictive tools.It was found that the best prediction performance was obtained from extreme gradient boosting-multi-universe optimizer(XGB-MVO)with R^(2) value of 0.9949 and 0.9398 for training and testing sets,respectively.XGB-MVO was used for evaluating physical laws of carbonation and it was found that the developed XGB-MVO model could provide reasonable predictions when new data were investigated.It also showed better generalization capabilities when compared with different models in the literature.Overall,this paper emphasizes the need for sustainable solutions in the construction industry to reduce its environmental impact and contribute to sustainable and low-carbon economies.

关键词： recycled aggregate concrete carbonation depth nature-inspired optimization algorithms extreme gradient boosting technique parametric analysis

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Rock mass structural recognition from drill monitoring technology in underground mining using discontinuity index and machine learning techniques

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International Journal of Mining Science and Technology 2023年第5期33卷 555-571页

作者： Alberto Fernández JoséA.Sanchidrián Pablo Segarra Santiago Gómez Enming Li Rafael Navarro Universidad Politécnica de Madrid–ETSI Minas y Energía Spain Universidad de Salamanca–GIR Charrock Spain

A procedure to recognize individual discontinuities in rock mass from measurement while drilling(MWD)technology is developed,using the binary pattern of structural rock characteristics obtained from in-hole images for calibration.Data from two underground operations with different drilling technology and different rock mass characteristics are considered,which generalizes the application of the methodology to different sites and ensures the full operational integration of MWD data analysis.Two approaches are followed for site-specific structural model building:a discontinuity index(DI)built from variations in MWD parameters,and a machine learning(ML)classifier as function of the drilling parameters and their variability.The prediction ability of the models is quantitatively assessed as the rate of recognition of discontinuities observed in borehole logs.Differences between the parameters involved in the models for each site,and differences in their weights,highlight the site-dependence of the resulting models.The ML approach offers better performance than the classical DI,with recognition rates in the range 89%to 96%.However,the simpler DI still yields fairly accurate results,with recognition rates 70%to 90%.These results validate the adaptive MWD-based methodology as an engineering solution to predict rock structural condition in underground mining operations.

关键词： Drill monitoring technology Rock mass characterization Underground mining Similarity metrics of binary vectors Structural rock factor Machine learning

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High strain rate compressive strength behavior of cemented paste backfill using split Hopkinson pressure bar

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International Journal of Mining Science and Technology 2021年第3期31卷 387-399页

作者： Xin Chen Xiuzhi Shi Jian Zhou Enming Li Peiyong Qiu Yonggang Gou School of Resources and Safety Engineering Central South UniversityChangsha 410083China Research Institute on Mines and Environment Department of CivilGeological and Mining EngineeringÉcole Polytechnique de MontréalMontréal H3C 3A7Canada

The stability of cemented paste backfill(CPB)is threatened by dynamic disturbance,but the conventional low strain rate laboratory pressure test has difficulty achieving this research purpose.Therefore,a split Hopkinson pressure bar(SHPB)was utilized to investigate the high strain rate compressive behavior of CPB with dynamic loads of 0.4,0.8,and 1.2 MPa.And the failure modes were determined by macro and micro analysis.CPB with different cement-to-tailings ratios,solid mass concentrations,and curing ages was prepared to conduct the SHPB test.The results showed that increasing the cement content,tailings content,and curing age can improve the dynamic compressive strength and elastic modulus.Under an impact load,a higher strain rate can lead to larger increasing times of the dynamic compressive strength when compared with static loading.And the dynamic compressive strength of CPB has an exponential correlation with the strain rate.The macroscopic failure modes indicated that CPB is more seriously damaged under dynamic loading.The local damage was enhanced,and fine cracks were formed in the interior of the CPB.This is because the CPB cannot dissipate the energy of the high strain rate stress wave in a short loading period.

关键词： High strain rate Compressive strength behavior Cemented paste backfill Split Hopkinson pressure bar Tailings

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Prediction of blasting mean fragment size using support vector regression combined with five optimization algorithms

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Journal of Rock Mechanics and Geotechnical Engineering 2021年第6期13卷 1380-1397页

作者： Enming Li Fenghao Yang Meiheng Ren Xiliang Zhang Jian Zhou Manoj Khandelwal School of Resources and Safety Engineering Central South UniversityChangsha410083China Universidad Politecnica de Madrid-ETSI Minas y Energia Rios Rosas 21Madrid28003Spain College of Locomotive and Rolling Stock Engineering Dalian Jiaotong UniversityDalian116028China Department of Mathematics University of California Santa BarbaraSanta BarbaraCA93106USA State Key Laboratory of Safety and Health for Metal Mines Maanshan243000China School of Engineering Information Technology and Physical SciencesFederation University AustraliaBallaratVIC3350Australia

The main purpose of blasting operation is to produce desired and optimum mean size rock fragments.Smaller or fine fragments cause the loss of ore during loading and transportation,whereas large or coarser fragments need to be further processed,which enhances production cost.Therefore,accurate prediction of rock fragmentation is crucial in blasting operations.Mean fragment size(MFS) is a crucial index that measures the goodness of blasting designs.Over the past decades,various models have been proposed to evaluate and predict blasting fragmentation.Among these models,artificial intelligence(AI)-based models are becoming more popular due to their outstanding prediction results for multiinfluential factors.In this study,support vector regression(SVR) techniques are adopted as the basic prediction tools,and five types of optimization algorithms,i.***.grid search(GS),grey wolf optimization(GWO),particle swarm optimization(PSO),genetic algorithm(GA) and salp swarm algorithm(SSA),are implemented to improve the prediction performance and optimize the hyper-parameters.The prediction model involves 19 influential factors that constitute a comprehensive blasting MFS evaluation system based on AI techniques.Among all the models,the GWO-v-SVR-based model shows the best comprehensive performance in predicting MFS in blasting operation.Three types of mathematical indices,i.***.mean square error(MSE),coefficient of determination(R^(2)) and variance accounted for(VAF),are utilized for evaluating the performance of different prediction models.The R^(2),MSE and VAF values for the training set are 0.8355,0.00138 and 80.98,respectively,whereas 0.8353,0.00348 and 82.41,respectively for the testing set.Finally,sensitivity analysis is performed to understand the influence of input parameters on MFS.It shows that the most sensitive factor in blasting MFS is the uniaxial compressive strength.

关键词： Blasting mean fragment size e-support vector regression(e-SVR) V-support vector regression(v-SVR) Meta-heuristic algorithms Intelligent prediction

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Therapeutic effect of electroacupuncture,massage,and blocking therapy on external humeral epicondylitis

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Journal of Traditional Chinese Medicine 2014年第3期34卷 261-266页

作者： Xinjian li Kun Zhou enming Zhang Zhen xiQi Weiqing Sun liangfu Xu Jianfeng Xu Youzhi Cai Ronghui Wang Orthopedic Institute Fujian University of Traditional Chinese Medicine Department of Orthopedics and Traumatology Beijing Sport University Hospital Orthopedic Institute Fujian University of Traditional Chinese Medicine Department of Internal Medicine Zhaoxian People's Hospital Department of Administration Sports Bureau of Fujian Province

OBJECTIVE:To compare two therapeutic methods:electroacupuncture + massage + blocking therapy,and blocking therapy alone in the treatment of external humeral epicondylitis.METHODS:Eighty-six patients were randomized into two groups with 43 in each. The treatment group received electroacupuncture + massage +blocking therapy, while the control group received blocking therapy only. A course of electroacupuncture treatment included therapy once a day for 10days. There were 10 treatments in a massage course and massage was given once a day, with a1-week interval given before the next course. A course of blocking treatment included therapy once a week, for twototaltreatments,andgenerallyno more than three times. The therapeutic effects were evaluated with the visual analog scale(VAS),grip strength index(GSI) score, and Mayo elbow performance score(MEPS) before treatment and at0, 6, 12, and 24 months after treatment to observe thetotaleffectiverate.RESULTS: In the treatment and control groups before treatment and at 0, 6, 12, and 24 months after treatment, the VAS scores were: 6.5±1.9 and 6.4±1.6; 4.6±1.3 and 4.6±1.7; 4.8±1.3 and 4.8±1.2; 4.6±1.2 and 6.6±1.6; and 6.5±1.6 and 6.5±1.3, respectively. The GSI scores were 63±8 and 63±8; 84±6and82±7;82±7and82±6;84±6and62±8;and64±6 and 64±7, respectively.The MEPS of both groups were65±7and66±8;85±6and84±7;84±5and84±7;80±7and66±6;and65±6and65±7,respectively.The total effective rates of the treatment and control groups at 0, 6, 12, and 24 months after treatment were 87.5% and 85.0%; 85.0% and 82.5%;80.0% and 12.5%; and 2.5% and 5.0%, respectively.Compared with the treatment group, the control group had greater joint function, better the rapeutic effect, and lower pain intensity(P0.05)betweenthetwogroups.CONCLUSION: We found that both methods were effective for external humeral epicondylitis. After 6months of treatment,t

关键词：治疗效果封闭疗法肱骨电针对照组 VAS GSI 复发率

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Lubrication Performance of Connecting-Rod and Main Bearing in Di erent Engine Operating Conditions

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Chinese Journal of Mechanical Engineering 2019年第2期32卷 81-92页

作者： Jun Sun Biao li Shaoyu Zhu enming Miao Hu Wang Xiaoyong Zhao Qin Teng School of Automotive and Transportation Engineering Hefei University of Technology School of Mechanical Engineering Hefei University of Technology School of Instrument Science and Opto-electronics Engineering Hefei University of Technology

Only the lubrication performance at rated engine operating condition was generally analyzed in current design and research of engine connecting-rod and main bearing. However, the actual engine(especially vehicle engine) does not always operate in rated operating condition and its operating condition changes constantly. In this paper, a fourstroke four-cylinder engine is taken as the studying object, the load and lubrication of connecting-rod and main bearing in di erent operating conditions are analyzed. The load of connecting-rod bearing is calculated by the dynamic calculation method, the loads of all main bearings are calculated by the whole crankshaft beam-element finite element method, and the lubrication performance of connecting-rod and main bearings are analyzed by the dynamic method. The results show that there are major di erences in the changes and numerical value at corresponding moment of the loads and lubrication performance of connecting-rod and main bearings in an engine operating cycle in di erent engine operating conditions; the most unfavorable case of the lubrication performance of connectingrod and main bearings may not take place in the rated engine operating condition. There are also major di erences between the lubrication performance of connecting-rod bearing and that of main bearing and between the lubrication performances of main bearings one another. Therefore, it will not be reasonable that the lubrication performance of a certain connecting-rod bearing or main bearing is analyzed in the design of the engine bearing. It is necessary to analyze simultaneously the lubrication performances of all bearings in di erent engine operating conditions.

关键词： Lubrication Connecting-rod bearing Main bearing Engine Operating condition

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基于引导信号修正的时域干扰阻塞方法

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兵工学报 2019年第3期40卷 561-569页

作者：郑恩明陈新华李嶷中国科学院声学研究所北京100190

针对时域干扰阻塞方法导致的波束畸变和探测盲区问题,提出一种基于引导信号修正的时域干扰阻塞方法。该方法根据处理数据频带构造引导信号;在干扰角度处,利用时域干扰阻塞方法对引导信号进行处理,得到相应波束;通过Kalman滤波器对该波... 详细信息

针对时域干扰阻塞方法导致的波束畸变和探测盲区问题,提出一种基于引导信号修正的时域干扰阻塞方法。该方法根据处理数据频带构造引导信号;在干扰角度处,利用时域干扰阻塞方法对引导信号进行处理,得到相应波束;通过Kalman滤波器对该波束进行光滑处理,得到干扰阻塞波束衰减曲线;采用波束衰减曲线对干扰阻塞输出波束进行非线性化处理,以实现波束优化,减小波束畸变和探测盲区,达到预期的干扰阻塞效果和探测效果。数值仿真和数据处理结果表明,该方法通过构造引导信号优化时域干扰阻塞方法,衰减区间缩小到5°;降低时域干扰阻塞方法形成的"宽凹"或"凸"字型波束对目标探测结果的影响,实现了波束校正;减小探测盲区,对原衰减区间内的弱目标实现了有效探测。

关键词：时域干扰阻塞波束畸变探测盲区引导信号衰减曲线波束校正

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Mechanical Design of the Full-length Magnet Mirror Structure for a Nbg_(3)Sn Sextupole Coil of FECR

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IMP & HIRFL Annual Report 2019年第1期 268-269页

作者： Zhu li Chen Yuquan Mei enming Wu Wei Sun liangting 不详

The fourth-generation 45 GHz ECR source(FECR)is under development in the Institute of Modern Physics.One of the critical components of FECR is the Nb_(3)Sn sextupole coil with a length of 937.4 mm.

关键词： Nb3Sn ECR critical

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Understanding the phase separation characteristics of nucleocapsid protein provides a new therapeutic opportunity against SARS-CoV-2

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Protein & Cell 2021年第9期12卷 734-740页

作者： Dan Zhao Weifan Xu Xiaofan Zhang Xiaoting Wang Yiyue Ge enming Yuan Yuanpeng Xiong Shenyang Wu Shuya li Nian Wu Tingzhong Tian Xiaolong Feng Hantao Shu Peng Lang Jingxin li Fengcai Zhu Xiaokun Shen Haitao li Pilong li Jianyang Zeng Institute for Interdisciplinary Information Sciences Tsinghua UniversityBeijing 100084China Beijing Advanced Innovation Center for Structural Biology Frontier Research Center for Biological Structure School of Life SciencesTsinghua UniversityBeijing 100084China Winghua-Peking Center for life Sciences Tsinghua UniversityBeijing 100084China Silexon Al Technology Co. LtdNanjing 210033China NHC Key Laboratory of Enteric Pathogenic Microbiology Jiangsu Provincial Center for Diseases Control and PreventionNanjing 210009China Bioinformatics Division BNRIST/Department of Computer Science and TechnologyTsinghua UniversityBeijing 100084China Protein Preparation and Identification Facility Technology Center for Protein ScienceTsinghua UniversityBeijing 100084China Institute of Pathology Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430074China Center for Global Health Nanjing Medical UniversityNanjing 210009China Convalife(Shanghai)Co. LtdShanghai 201203China Ministry of Education Key Laboratory of Protein Sciences Beijing Advaneed Innovation Center for Structural BiologyBeijing Frontier Research Center for Biological StructureDepartment of Basic Medical SciencesSchool of MedicineTsinghua UniversityBeijing 100084China

Dear Editor To date,tens of millions of people have been infected with severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),causing the outbreak of the respiratory disease named the coronavirus disease 2019(COVID-19).As a newly emerged member of the coronavirus family,SARS-CoV-2 is an enveloped positive-strand RNA virus,which has probably the largest genome(approximately 30 kb)among all RNA viruses.The nucleocapsid(N)protein of SARS-CoV-2 is mainly responsible for recognizing and wrapping viral RNA into helically symmetric structures(Malik,2020).

关键词： respiratory acute therapeutic

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