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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 ***,RA concrete is plagued with considerable durability concerns,particularly *** advance the application of RA concrete,the establishment of a reliable model for predicting the carbonation is *** the one hand,concrete carbonation is a long and slow process and thus consumes a lot of time and energy to *** the other hand,carbonation is influenced by many factors and is hard to *** this,this paper proposes the use of machine learning techniques to establish accurate prediction models for the carbonation depth(CD)of RA *** types of regression techniques and meta-heuristic algorithms were employed to provide more alternative predictive *** 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,***-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 *** also showed better generalization capabilities when compared with different models in the ***,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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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 ***,its production leads to high carbon dioxide(CO_(2))emissions and energy ***,developing waste-substitutable concrete components is *** the sustainability and greenness of concrete is the focus of this *** 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 *** complex relationship between influential factors and concrete compressive strength makes the prediction and estimation of compressive strength *** 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 *** 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,*** remaining five prediction methods yield promising ***,the developed hybrid XGB model can be introduced as an accurate and fast technique for the performance prediction of green ***,the developed SSA-XGB considered the effects of all the input factors on the compressive *** 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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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 ***,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 *** the failure modes were determined by macro and micro *** with different cement-to-tailings ratios,solid mass concentrations,and curing ages was prepared to conduct the SHPB *** results showed that increasing the cement content,tailings content,and curing age can improve the dynamic compressive strength and elastic *** an impact load,a higher strain rate can lead to larger increasing times of the dynamic compressive strength when compared with static *** the dynamic compressive strength of CPB has an exponential correlation with the strain *** macroscopic failure modes indicated that CPB is more seriously damaged under dynamic *** local damage was enhanced,and fine cracks were formed in the interior of the *** 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 *** 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 ***,accurate prediction of rock fragmentation is crucial in blasting *** fragment size(MFS) is a crucial index that measures the goodness of blasting *** the past decades,various models have been proposed to evaluate and predict blasting *** these models,artificial intelligence(AI)-based models are becoming more popular due to their outstanding prediction results for multiinfluential *** this study,support vector regression(SVR) techniques are adopted as the basic prediction tools,and five types of optimization algorithms,*** 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 *** prediction model involves 19 influential factors that constitute a comprehensive blasting MFS evaluation system based on AI *** all the models,the GWO-v-SVR-based model shows the best comprehensive performance in predicting MFS in blasting *** types of mathematical indices,*** square error(MSE),coefficient of determination(R^(2)) and variance accounted for(VAF),are utilized for evaluating the performance of different prediction *** 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 ***,sensitivity analysis is performed to understand the influence of input parameters on *** 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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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 *** 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 *** 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 *** prediction ability of the models is quantitatively assessed as the rate of recognition of discontinuities observed in borehole *** between the parameters involved in the models for each site,and differences in their weights,highlight the site-dependence of the resulting *** 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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Microacoustic Imaging Technique for Bulk Microstructure Characterization of Woven CFRC Materials

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航空制造技术 2007年第Z1期50卷 20-25页

作者： liu Songping Guo enming liu Feifei li Legang V.M.Levin Yu S Petronyuk

In short wavelength case ultrasound propagation through woven carbon fiber-rein-forced composite (CFRC) materials essentially depends on their *** bundle and weaving ply can be considered as basic structural elements of woven CFRC *** structural units provide effective reflectors when ultra-short impulse high frequency ultrasound impinges on woven *** reflection coefficient R0 achieves level of 3%-15% at ply-ply interface,0.2-0.4 in resin pocket *** magnitude of reflection coefficient offers reasonable acoustic contrast to see packing,lay-up and resin pockets in woven laminate body by acoustic imaging *** imaging technique is based on time-resolved echoes from reflections in woven CFRC laminates.A focal system with low aperture angle is used to generate 20-40 probe ultrasonic pulse *** spatial resolution is up to 60mm at 50MHz in woven CFRC *** results presented in this paper demonstrates the possibility and potentiality of characterization of microstrueture of woven CFRC ***,lay-up,weaving nature,fiber bundle,resin pocket as well as their topology are exhibited clearly in the acoustic images by using microaeoustic technique.

关键词： Ultrasonics Woven composites Microstructure Acoustic imaging

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利用电石渣制内墙涂料的研究

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无机盐工业 2011年第2期43卷 55-56页

作者：陈文娟李恩明湖南化工职业技术学院湖南株洲412004 中盐集团湖南株洲化工有限责任公司

对中国内墙涂料市场进行了分析和预测,指出以电石水解后产生的电石渣代替灰钙粉作内墙涂料的填料可行,并对其市场前景进行展望。分析了电石渣的组成和性质,通过小试确定了电石渣的处理工艺,并进行了生产成本估算和对比以及经济效益评价... 详细信息

对中国内墙涂料市场进行了分析和预测,指出以电石水解后产生的电石渣代替灰钙粉作内墙涂料的填料可行,并对其市场前景进行展望。分析了电石渣的组成和性质,通过小试确定了电石渣的处理工艺,并进行了生产成本估算和对比以及经济效益评价。假设在PVC厂配套建一套10万t/a内墙涂料的生产装置,预计可处理电石渣近10万t/a,可实现经济效益5 270万元/a。该工艺使电石渣变废为宝,减轻了环保压力,具有十分良好的环保效益和经济效益。

关键词：电石渣内墙涂料资源化利用环保

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基于温度相似性评价的机床热误差重构模型

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机械工程学报 2024年第5期60卷 288-295页

作者：徐凯李喆裕李国龙苗恩铭庹军波重庆理工大学机械工程学院重庆400054 重庆大学机械传动国家重点实验室重庆400044 重庆工商大学机械工程学院重庆400067

数据驱动方法广泛用于热误差建模,然而缺乏机理支撑的开环黑箱运行模式难以保证模型在新工况中的鲁棒性,模型极易失效。为进一步提升机床热误差模型的精度和稳定性,提出了一种基于温度相似性评价的机床热误差重构模型。通过对预测组温... 详细信息

数据驱动方法广泛用于热误差建模,然而缺乏机理支撑的开环黑箱运行模式难以保证模型在新工况中的鲁棒性,模型极易失效。为进一步提升机床热误差模型的精度和稳定性,提出了一种基于温度相似性评价的机床热误差重构模型。通过对预测组温度均值向量与建模组温度均值向量的相似性评价,从原始建模批次中筛选出与预测组温度相似的特定批次,并基于偏最小二乘算法进行热误差模型重构。为验证重构模型的有效性,进行了累计31批次的热误差预测,结果表明重构模型在保证稳定性的前提下,可将预测结果的均方根误差均值进一步降低至2.2μm,相比常规的模糊聚类结合多元线性回归模型与偏最小二乘模型,预测精度可分别提高15%、35%,效果显著。方法对于评价数据驱动热误差模型的适用性,提高模型预测精度具有参考价值。

关键词：相似性评价重构模型偏最小二乘算法稳定性

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基于动物标本的机器人自主椎体穿刺压力与路径准确性的相关性研究

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中华骨科杂志 2024年第15期44卷 1018-1024页

作者：邢通黄俊燊李明李玉希梁育玮车圳陈恩铭黄霖中山大学孙逸仙纪念医院骨外科广州510120 上海交通大学医学院附属第九人民医院骨科(上海市骨科内植物重点实验室) 上海200100 中山大学附属第八医院骨外科深圳518000

目的探讨骨科机器人在自主穿刺推进中不同骨质的压力峰值对穿刺路径偏移及骨水泥渗漏的影响。方法自行设计一种用于自主椎体穿刺和骨水泥推注的脊柱手术机器人系统,并对6具离体猪脊柱标本进行单节段或双节段模拟经皮椎体成形术。测量穿... 详细信息

目的探讨骨科机器人在自主穿刺推进中不同骨质的压力峰值对穿刺路径偏移及骨水泥渗漏的影响。方法自行设计一种用于自主椎体穿刺和骨水泥推注的脊柱手术机器人系统,并对6具离体猪脊柱标本进行单节段或双节段模拟经皮椎体成形术。测量穿刺路径准确性(Gertzbein-Robbins分级)、骨水泥渗漏分型及骨钻峰值压力,评估不同密度的椎体皮质骨与松质骨对骨科机器人执行自主椎体穿刺准确性及骨水泥渗漏情况。结果模拟穿刺64个离体猪椎体,其中Gertzbein-Robbins分级A级53椎,B级8椎,C级3椎。A级椎体的皮质骨压力(6.663±0.319)N,低于B级和C级的(8.348±0.418)和(11.500±0.600)N,差异有统计学意义(F=341.000,P<0.001);A、B、C级椎体的松质骨压力分别为(3.660±0.317)、(3.594±0.608)、(4.117±0.257)N,差异无统计学意义(F=2.496,P=0.091)。骨水泥无渗漏40椎、Ⅰ型(注入椎体内渗漏至椎体周围)20椎、Ⅱ型(注入椎体内渗漏至椎管内)3椎,总体渗漏发生率为36%(23/64)。无渗漏、Ⅰ型与Ⅱ型渗漏的皮质骨峰值压力分别为(6.638±0.301)、(6.792±0.404)、(6.753±0.473)N,松质骨压力峰值分别为(3.634±0.279)、(3.783±0.423)、(3.920±0.255)N,差异均无统计学意义(F=1.521,P=0.227;F=2.106,P=0.131)。结论新型骨科机器人在自主穿刺推进过程中穿过皮质骨穿刺压力较高时自主穿刺路径准确性降低,且侵犯椎弓根的概率增加;皮质骨与松质骨的穿刺压力对骨水泥渗漏的发生率无明显影响。

关键词：机器人手术最小侵入性外科手术椎体成形术穿刺术骨水泥成形术

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论继续医学教育与科研工作的关系

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继续医学教育 2001年第1期15卷 15-16页

作者：李恩民许丽艳卢仁仁陈爱云汕头大学医学院广东省汕头市新陵路22号515031

从继续医学教育项目负责人的角度 ,给合申办继续医学教育项目的经验体会 ,阐述了继续医学教育与科研工作的关系 :继续医学教育与科研工作有着广泛而密切的联系 ,是一种相辅相成的关系。

关键词：继续医学教育科研工作学科建设

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