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

《结构与土木工程前沿（英文）》   页码 1310-1325 doi: 10.1007/s11709-023-0997-3

摘要： Concrete is the most commonly used construction material. However, its production leads to high carbon dioxide (CO2) 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 (R2), 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 R2 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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The influence of social media on stock volatility

Xianjiao WU, Xiaolin WANG, Shudong MA, Qiang YE

《工程管理前沿（英文）》 2017年 第4卷 第2期   页码 201-211 doi: 10.15302/J-FEM-2017018

摘要： This study explores the influence of social media on stock volatility and builds a feature model with an intelligence algorithm using social media data from Xueqiu.com in China, Sina Finance and Economics, Sina Microblog, and Oriental Fortune. We find that the effect of social factors, such as increased attention to a stock’s volatility, is more significant than public sentiment. A prediction model is introduced based on social factors and public sentiment to predict stock volatility. Our findings indicate that the influence of social media data on the next day’s volatility is more significant but declines over time.

关键词： stock volatility     social data     sentiment analysis     boosting algorithm    

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Predicting shear strength of slender beams without reinforcement using hybrid gradient boosting trees

Thuy-Anh NGUYEN; Hai-Bang LY; Van Quan TRAN

《结构与土木工程前沿（英文）》 2022年 第16卷 第10期   页码 1267-1286 doi: 10.1007/s11709-022-0842-0

摘要： Shear failure of slender reinforced concrete beams without stirrups has surely been a complicated occurrence that has proven challenging to adequately understand. The primary purpose of this work is to develop machine learning models capable of reliably predicting the shear strength of non-shear-reinforced slender beams (SB). A database encompassing 1118 experimental findings from the relevant literature was compiled, containing eight distinct factors. Gradient Boosting (GB) technique was developed and evaluated in combination with three different optimization algorithms, namely Particle Swarm Optimization (PSO), Random Annealing Optimization (RA), and Simulated Annealing Optimization (SA). The findings suggested that GB-SA could deliver strong prediction results and effectively generalizes the connection between the input and output variables. Shap values and two-dimensional PDP analysis were then carried out. Engineers may use the findings in this work to define beam's geometrical components and material used to achieve the desired shear strength of SB without reinforcement.

关键词： slender beam     shear strength     gradient boosting     optimization algorithms    

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Photoreduction adjusted surface oxygen vacancy of BiMoO for boosting photocatalytic redox performance

《化学科学与工程前沿（英文）》 2023年 第17卷 第12期   页码 1937-1948 doi: 10.1007/s11705-023-2353-5

摘要： In this study, Bi2MoO6 with adjustable rich oxygen vacancies was prepared by a novel and simple solvothermal-photoreduction method which might be suitable for a large-scale production. The experiment results show that Bi2MoO6 with rich oxygen vacancies is an excellent photocatalyst. The photocatalytic ability of BMO-10 is 0.3 and 3.5 times higher than that of the pristine Bi2MoO6 for Rhodamine B degradation and Cr(VI) reduction, respectively. The results display that the band energy of the samples with oxygen vacancies was narrowed and the light absorption was broadened. Meanwhile, the efficiency of photogenerated electron-holes was increased and the separation and transfer speed of photogenerated carriers were improved. Therefore, this work provides a convenient and efficient method to prepare potential adjustable oxygen vacancy based photocatalysts to eliminate the pollution of dyes and Cr(VI) in water.

关键词： Bi2MoO6     oxygen vacancies     photoreduction     Cr(VI)     RhB    

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Floret-like Fe–N nanoparticle-embedded porous carbon superstructures from a Fe-covalent triazine polymer boosting

《化学科学与工程前沿（英文）》 2023年 第17卷 第5期   页码 525-535 doi: 10.1007/s11705-022-2232-5

摘要： Fe–Nx nanoparticles-embedded porous carbons with a desirable superstructure have attracted immense attention as promising catalysts for electrochemical oxygen reduction reaction. Herein, we employed Fe-coordinated covalent triazine polymer for the fabrication of Fe–Nx nanoparticle-embedded porous carbon nanoflorets (Fe/N@CNFs) employing a hypersaline-confinement-conversion strategy. Presence of tailored N types within the covalent triazine polymer interwork in high proportions contributes to the generation of Fe/N coordination and subsequent Fe–Nx nanoparticles. Owing to the utilization of NaCl crystals, the resultant Fe/N@CNF-800 which was generated by pyrolysis at 800 °C showed nanoflower structure and large specific surface area, which remarkably suppressed the agglomeration of high catalytic active sites. As expect, the Fe/N@CNF-800 exhibited unexpected oxygen reduction reaction catalytic performance with an ultrahigh half-wave potential (0.89 V vs. reversible hydrogen electrode), a dominant 4e– transfer approach and great cycle stability (> 92% after 100000 s). As a demonstration, the Fe/N-PCNF-800-assembled zinc–air battery delivered a high open circuit voltage of 1.51 V, a maximum peak power density of 164 mW·cm–2, as well as eminent rate performance, surpassing those of commercial Pt/C. This contribution offers a valuable avenue to exploit efficient metal nanoparticles-based carbon catalysts towards energy-related electrocatalytic reactions and beyond.

关键词： Fe–Nx nanoparticles     hypersaline-confinement conversion     floret-like carbon     covalent triazine polymers     oxygen reduction reaction    

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余弦忆导忆阻系统周期变化初值位移调控行为 Regular Papers

Mo CHEN, Xue REN, Hua-gan WU, Quan XU, Bo-cheng BAO

《信息与电子工程前沿（英文）》 2019年 第20卷 第12期   页码 1706-1716 doi: 10.1631/FITEE.1900360

摘要： 利用一种新型余弦忆导理想忆阻，构造一个四维忆阻系统。由于忆导函数特殊的非线性，忆阻系统具有沿忆阻内部状态变量坐标轴分布的线平衡点集(0, 0, 0, δ)，且平衡点集稳定性随δ变化而周期性演化。数值仿真揭示了忆阻系统非线性、一维的初值位移调控行为，它不仅可由忆阻状态初值触发，也可由其他两个系统状态初值引发。特别地，在位移调控路线上，可以观测到多种具有不同位置和拓扑结构的共存吸引子。通过PSIM电路仿真对该特殊动力学特性进行了验证。

关键词： 初值位移调控；忆阻系统；忆导；线平衡点集    

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Machine learning enabled prediction and process optimization of VFA production from riboflavin-mediated sludge fermentation

《环境科学与工程前沿（英文）》 2023年 第17卷 第11期 doi: 10.1007/s11783-023-1735-8

摘要：

● Data-driven approach was used to simulate VFA production from WAS fermentation.

关键词： Machine learning     Volatile fatty acids     Riboflavin     Waste activated sludge     eXtreme Gradient Boosting    

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SPT based determination of undrained shear strength: Regression models and machine learning

Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU

《结构与土木工程前沿（英文）》 2020年 第14卷 第1期   页码 185-198 doi: 10.1007/s11709-019-0591-x

摘要： The purpose of this study is the accurate prediction of undrained shear strength using Standard Penetration Test results and soil consistency indices, such as water content and Atterberg limits. With this study, along with the conventional methods of simple and multiple linear regression models, three machine learning algorithms, random forest, gradient boosting and stacked models, are developed for prediction of undrained shear strength. These models are employed on a relatively large data set from different projects around Turkey covering 230 observations. As an improvement over the available studies in literature, this study utilizes correct statistical analyses techniques on a relatively large database, such as using a train/test split on the data set to avoid overfitting of the developed models. Furthermore, the validity and consistency of the prediction results are ensured with the correct use of statistical measures like -value and cross-validation which were missing in previous studies. To compare the performances of the models developed in this study with the prior ones existing in literature, all models were applied on the test data set and their performances are evaluated in terms of the resulting root mean squared error ( ) values and coefficient of determination ( ). Accordingly, the models developed in this study demonstrate superior prediction capabilities compared to all of the prior studies. Moreover, to facilitate the use of machine learning algorithms for prediction purposes, entire source code prepared for this study and the collected data set are provided as supplements of this study.

关键词： undrained shear strength     linear regression     random forest     gradient boosting     machine learning     standard penetration test    

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Boosting the direct conversion of NHHCO electrolyte to syngas on Ag/Zn zeolitic imidazolate framework

《化学科学与工程前沿（英文）》 2023年 第17卷 第9期   页码 1196-1207 doi: 10.1007/s11705-022-2289-1

摘要： The electrochemical reduction of NH4HCO3 to syngas can bypass the high energy consumption of high-purity CO2 release and compression after the ammonia-based CO2 capture process. This technology has broad prospects in industrial applications and carbon neutrality. A zeolitic imidazolate framework-8 precursor was introduced with different Ag contents via colloid chemical synthesis. This material was carbonized at 1000 °C to obtain AgZn zeolitic imidazolate framework derived nitrogen carbon catalysts, which were used for the first time for boosting the direct conversion of NH4HCO3 electrolyte to syngas. The AgZn zeolitic imidazolate framework derived nitrogen carbon catalyst with a Ag/Zn ratio of 0.5:1 achieved the highest CO Faradaic efficiency of 52.0% with a current density of 1.15 mA·cm–2 at –0.5 V, a H2/CO ratio of 1–2 (–0.5 to –0.7 V), and a stable catalytic activity of more than 6 h. Its activity is comparable to that of the CO2-saturated NH4HCO3 electrolyte. The highly discrete Ag-Nx and Zn-Nx nodes may have combined catalytic effects in the catalysts synthesized by appropriate Ag doping and sufficient carbonization. These nodes could increase active sites of catalysts, which is conducive to the transport and adsorption of reactant CO2 and the stability of *COOH intermediate, thus can improve the selectivity and catalytic activity of CO.

关键词： Ag catalyst     zeolitic imidazolate framework     CO2 electroreduction     ammonium bicarbonate electrolyte     syngas    

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Beyond bag of latent topics: spatial pyramid matching for scene category recognition

Fu-xiang LU,Jun HUANG

《信息与电子工程前沿（英文）》 2015年 第16卷 第10期   页码 817-828 doi: 10.1631/FITEE.1500070

摘要： We propose a heterogeneous, mid-level feature based method for recognizing natural scene categories. The proposed feature introduces spatial information among the latent topics by means of spatial pyramid, while the latent topics are obtained by using probabilistic latent semantic analysis (pLSA) based on the bag-of-words representation. The proposed feature always performs better than standard pLSA because the performance of pLSA is adversely affected in many cases due to the loss of spatial information. By combining various interest point detectors and local region descriptors used in the bag-of-words model, the proposed feature can make further improvement for diverse scene category recognition tasks. We also propose a two-stage framework for multi-class classification. In the first stage, for each of possible detector/descriptor pairs, adaptive boosting classifiers are employed to select the most discriminative topics and further compute posterior probabilities of an unknown image from those selected topics. The second stage uses the prod-max rule to combine information coming from multiple sources and assigns the unknown image to the scene category with the highest ‘final’ posterior probability. Experimental results on three benchmark scene datasets show that the proposed method exceeds most state-of-the-art methods.

关键词： Scene category recognition     Probabilistic latent semantic analysis     Bag-of-words     Adaptive boosting    

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退火-遗传算法寻优及其实现

王英

《中国工程科学》 2008年 第10卷 第7期   页码 57-59

摘要：

分析了遗传算法及退火算法的优缺点，提出用退火算法改进遗传算法局部的最优值搜索效率低问题。退火算法与遗传算法融合后，使算法在寻优结果上更加迅速精确。通过水泥的配比工程实例，与单纯的遗传算法的结果进行对比，说明该方法是有效的。

关键词： 遗传算法     退火算法     遗传算法改进    

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Application of machine learning technique for predicting and evaluating chloride ingress in concrete

Van Quan TRAN; Van Loi GIAP; Dinh Phien VU; Riya Catherine GEORGE; Lanh Si HO

《结构与土木工程前沿（英文）》 2022年 第16卷 第9期   页码 1153-1169 doi: 10.1007/s11709-022-0830-4

摘要： The degradation of concrete structure in the marine environment is often related to chloride-induced corrosion of reinforcement steel. Therefore, the chloride concentration in concrete is a vital parameter for estimating the corrosion level of reinforcement steel. This research aims at predicting the chloride content in concrete using three hybrid models of gradient boosting (GB), artificial neural network (ANN), and random forest (RF) in combination with particle swarm optimization (PSO). The input variables for modeling include exposure condition, water/binder ratio (W/B), cement content, silica fume, time exposure, and depth of measurement. The results indicate that three models performed well with high accuracy of prediction (R2 ≥ 0.90). Among three hybrid models, the model using GB_PSO achieved the highest prediction accuracy (R2 = 0.9551, RMSE = 0.0327, and MAE = 0.0181). Based on the results of sensitivity analysis using SHapley Additive exPlanation (SHAP) and partial dependence plots 1D (PDP-1D), it was found that the exposure condition and depth of measurement were the two most vital variables affecting the prediction of chloride content. When the number of different exposure conditions is larger than two, the exposure significantly impacted the chloride content of concrete because the chloride ion ingress is affected by both chemical and physical processes. This study provides an insight into the evaluation and prediction of the chloride content of concrete in the marine environment.

关键词： gradient boosting     random forest     chloride content     concrete     sensitivity analysis.    

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Multiobjective image recognition algorithm in the fully automatic die bonder

JIANG Kai, CHEN Hai-xia, YUAN Sen-miao

《机械工程前沿（英文）》 2006年 第1卷 第3期   页码 313-316 doi: 10.1007/s11465-006-0026-y

摘要： It is a very important task to automatically fix the number of die in the image recognition system of a fully automatic die bonder. A multiobjective image recognition algorithm based on clustering Genetic Algorithm (GA), is proposed in this paper. In the evolutionary process of GA, a clustering method is provided that utilizes information from the template and the fitness landscape of the current population. The whole population is grouped into different niches by the clustering method. Experimental results demonstrated that the number of target images could be determined by the algorithm automatically, and multiple targets could be recognized at a time. As a result, time consumed by one image recognition is shortened, the performance of the image recognition system is improved, and the atomization of the system is fulfilled.

关键词： clustering     different     recognition algorithm     Algorithm     multiobjective    

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Comparative seismic design optimization of spatial steel dome structures through three recent metaheuristic algorithms

《结构与土木工程前沿（英文）》 2022年 第16卷 第1期   页码 57-74 doi: 10.1007/s11709-021-0784-y

摘要： Steel dome structures, with their striking structural forms, take a place among the impressive and aesthetic load bearing systems featuring large internal spaces without internal columns. In this paper, the seismic design optimization of spatial steel dome structures is achieved through three recent metaheuristic algorithms that are water strider (WS), grey wolf (GW), and brain storm optimization (BSO). The structural elements of the domes are treated as design variables collected in member groups. The structural stress and stability limitations are enforced by ASD-AISC provisions. Also, the displacement restrictions are considered in design procedure. The metaheuristic algorithms are encoded in MATLAB interacting with SAP2000 for gathering structural reactions through open application programming interface (OAPI). The optimum spatial steel dome designs achieved by proposed WS, GW, and BSO algorithms are compared with respect to solution accuracy, convergence rates, and reliability, utilizing three real-size design examples for considering both the previously reported optimum design results obtained by classical metaheuristic algorithms and a gradient descent-based hyperband optimization (HBO) algorithm.

关键词： steel dome optimization     water strider algorithm     grey wolf algorithm     brain storm optimization algorithm     hyperband optimization algorithm    

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Optimal design of steel skeletal structures using the enhanced genetic algorithm methodology

Tugrul TALASLIOGLU

《结构与土木工程前沿（英文）》 2019年 第13卷 第4期   页码 863-889 doi: 10.1007/s11709-019-0523-9

摘要： This study concerns with the design optimization of steel skeletal structures thereby utilizing both a real-life specification provisions and ready steel profiles named hot-rolled I sections. For this purpose, the enhanced genetic algorithm methodology named EGAwMP is utilized as an optimization tool. The evolutionary search mechanism of EGAwMP is constituted on the basis of generational genetic algorithm (GGA). The exploration capacity of EGAwMP is improved in a way of dividing an entire population into sub-populations and using of a radial basis neural network for dynamically adjustment of EGAwMP’s genetic operator parameters. In order to improve the exploitation capability of EGAwMP, the proposed neural network implementation is also utilized for prediction of more accurate design variables associating with a new design strategy, design codes of which are based on the provisions of LRFD_AISC V3 specification. EGAwMP is applied to determine the real-life ready steel profiles for the optimal design of skeletal structures with 105, 200, 444, and 942 members. EGAwMP accomplishes to increase the quality degrees of optimum designations Furthermore, the importance of using the real-life steel profiles and design codes is also demonstrated. Consequently, EGAwMP is suggested as a design optimization tool for the real-life steel skeletal structures.

关键词： design optimization     genetic algorithm     multiple populations     neural network    

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标题 作者 时间 类型 操作

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

期刊论文

The influence of social media on stock volatility

Xianjiao WU, Xiaolin WANG, Shudong MA, Qiang YE

期刊论文

Predicting shear strength of slender beams without reinforcement using hybrid gradient boosting trees

Thuy-Anh NGUYEN; Hai-Bang LY; Van Quan TRAN

期刊论文

Photoreduction adjusted surface oxygen vacancy of BiMoO for boosting photocatalytic redox performance

期刊论文

Floret-like Fe–N nanoparticle-embedded porous carbon superstructures from a Fe-covalent triazine polymer boosting

期刊论文

余弦忆导忆阻系统周期变化初值位移调控行为

Mo CHEN, Xue REN, Hua-gan WU, Quan XU, Bo-cheng BAO

期刊论文

Machine learning enabled prediction and process optimization of VFA production from riboflavin-mediated sludge fermentation

期刊论文

SPT based determination of undrained shear strength: Regression models and machine learning

Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU

期刊论文

Boosting the direct conversion of NHHCO electrolyte to syngas on Ag/Zn zeolitic imidazolate framework

期刊论文

Beyond bag of latent topics: spatial pyramid matching for scene category recognition

Fu-xiang LU,Jun HUANG

期刊论文

退火-遗传算法寻优及其实现

王英

期刊论文

Application of machine learning technique for predicting and evaluating chloride ingress in concrete

Van Quan TRAN; Van Loi GIAP; Dinh Phien VU; Riya Catherine GEORGE; Lanh Si HO

期刊论文

Multiobjective image recognition algorithm in the fully automatic die bonder

JIANG Kai, CHEN Hai-xia, YUAN Sen-miao

期刊论文

Comparative seismic design optimization of spatial steel dome structures through three recent metaheuristic algorithms

期刊论文

Optimal design of steel skeletal structures using the enhanced genetic algorithm methodology

Tugrul TALASLIOGLU

期刊论文