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prediction and optimization design of sustainable concrete based on squirrel search algorithm-extreme gradientboosting 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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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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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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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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Assessment of different machine learning techniques in predicting the compressive strength of self-compacting concrete

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

《结构与土木工程前沿（英文）》 2022年 第16卷 第7期   页码 928-945 doi: 10.1007/s11709-022-0837-x

摘要： The compressive strength of self-compacting concrete (SCC) needs to be determined during the construction design process. This paper shows that the compressive strength of SCC (CS of SCC) can be successfully predicted from mix design and curing age by a machine learning (ML) technique named the Extreme Gradient Boosting (XGB) algorithm, including non-hybrid and hybrid models. Nine ML techniques, such as Linear regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Trees (DTR), Random Forest (RF), Gradient Boosting (GB), and Artificial Neural Network using two training algorithms LBFGS and SGD (denoted as ANN_LBFGS and ANN_SGD), are also compared with the XGB model. Moreover, the hybrid models of eight ML techniques and Particle Swarm Optimization (PSO) are constructed to highlight the reliability and accuracy of SCC compressive strength prediction by the XGB_PSO hybrid model. The highest number of SCC samples available in the literature is collected for building the ML techniques. Compared with previously published works’ performance, the proposed XGB method, both hybrid and non-hybrid models, is the most reliable and robust of the examined techniques, and is more accurate than existing ML methods (R2 = 0.9644, RMSE = 4.7801, and MAE = 3.4832). Therefore, the XGB model can be used as a practical tool for engineers in predicting the CS of SCC.

关键词： compressive strength     self-compacting concrete     machine learning techniques     particle swarm optimization     extreme gradient boosting    

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Vibration analysis of nano-structure multilayered graphene sheets using modified strain gradient theory

Amir ALLAHBAKHSHI,Masih ALLAHBAKHSHI

《机械工程前沿（英文）》 2015年 第10卷 第2期   页码 187-197 doi: 10.1007/s11465-015-0339-9

摘要：

In this paper, for the first time, the modified strain gradient theory is used as a new size-dependent Kirchhoff micro-plate model to study the effect of interlayer van der Waals (vdW) force for the vibration analysis of multilayered graphene sheets (MLGSs). The model contains three material length scale parameters, which may effectively capture the size effect. The model can also degenerate into the modified couple stress plate model or the classical plate model, if two or all of the material length scale parameters are taken to be zero. After obtaining the governing equations based on modified strain gradient theory via principle of minimum potential energy, as only infinitesimal vibration is considered, the net pressure due to the vdW interaction is assumed to be linearly proportional to the deflection between two layers. To solve the governing equation subjected to the boundary conditions, the Fourier series is assumed for w=w(x, y). To show the accuracy of the formulations, present results in specific cases are compared with available results in literature and a good agreement can be seen. The results indicate that the present model can predict prominent natural frequency with the reduction of structural size, especially when the plate thickness is on the same order of the material length scale parameter.

关键词： graphene     van der Waals (vdW) force     modi- fied strain gradient elasticity theory     size effect parameter    

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Velocity gradient elasticity for nonlinear vibration of carbon nanotube resonators

Hamid M. SEDIGHI, Hassen M. OUAKAD

《结构与土木工程前沿（英文）》 2020年 第14卷 第6期   页码 1520-1530 doi: 10.1007/s11709-020-0672-x

摘要： In this study, for the first time, we investigate the nonlocality superimposed to the size effects on the nonlinear dynamics of an electrically actuated single-walled carbon-nanotube-based resonator. We undertake two models to capture the nanostructure nonlocal size effects: the strain and the velocity gradient theories. We use a reduced-order model based on the differential quadrature method (DQM) to discretize the governing nonlinear equation of motion and acquire a discretized-parameter nonlinear model of the system. The structural nonlinear behavior of the system assuming both strain and velocity gradient theories is investigated using the discretized model. The results suggest that nonlocal and size effects should not be neglected because they improve the prediction of corresponding dynamic amplitudes and, most importantly, the critical resonant frequencies of such nanoresonators. Neglecting these effects may impose a considerable source of error, which can be amended using more accurate modeling techniques.

关键词： velocity gradient elasticity theory     nanotube resonators     differential-quadrature method     nonlinear vibration    

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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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Gradient-based compressive image fusion

Yang CHEN,Zheng QIN

《信息与电子工程前沿（英文）》 2015年 第16卷 第3期   页码 227-237 doi: 10.1631/FITEE.1400217

摘要： We present a novel image fusion scheme based on gradient and scrambled block Hadamard ensemble (SBHE) sampling for compressive sensing imaging. First, source images are compressed by compressive sensing, to facilitate the transmission of the sensor. In the fusion phase, the image gradient is calculated to reflect the abundance of its contour information. By compositing the gradient of each image, gradient-based weights are obtained, with which compressive sensing coefficients are achieved. Finally, inverse transformation is applied to the coefficients derived from fusion, and the fused image is obtained. Information entropy (IE), Xydeas’s and Piella’s metrics are applied as non-reference objective metrics to evaluate the fusion quality in line with different fusion schemes. In addition, different image fusion application scenarios are applied to explore the scenario adaptability of the proposed scheme. Simulation results demonstrate that the gradient-based scheme has the best performance, in terms of both subjective judgment and objective metrics. Furthermore, the gradient-based fusion scheme proposed in this paper can be applied in different fusion scenarios.

关键词： Compressive sensing (CS)     Image fusion     Gradient-based image fusion     CS-based image fusion    

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Influence of freeze–thaw damage gradient on stress–strain relationship of stressed concrete

《结构与土木工程前沿（英文）》   页码 1326-1340 doi: 10.1007/s11709-023-0014-x

摘要： Influence of freeze–thaw damage gradient on stress–strain relationship of stressed concrete

关键词： strain relationship concrete    

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Preparation of hydrogels with uniform and gradient chemical structures using dialdehyde cellulose and

Peiwen Liu, Carsten Mai, Kai Zhang

《化学科学与工程前沿（英文）》 2018年 第12卷 第3期   页码 383-389 doi: 10.1007/s11705-018-1718-7

摘要：

Hydrogels with precisely designed structures represent promising materials with a broad application spectrum, such as for sensor, tissue engineering and biomimetic technology. However, with highly reactive compounds, the preparation of hydrogels still needs an efficient approach for desired distribution of each component within hydrogels. In addition, a method for in situ preparation of gradient hydrogels is still lacking. Herein, we report the formation of hydrogels with either uniform or gradient internal structures via a novel, simple but very efficient method by aerating ammonia gas (NH3 gas) into the solution of dialdehyde cellulose (DAC) and a diamine. As-prepared hydrogels exhibited uniform microscopic and chemical structure or gradient distribution of functional groups. Due to lots of aldehyde groups on DAC chains, functional hydrogels can be prepared by using diverse diamines. For instance, hydrogels prepared by using 1,6-hexanediamine as a cross-linker were responsive to pH values. Moreover, this controllable process of aerating NH3 gas allows the in situ formation of gradient hydrogels; for instance, by using cyanamide as a reaction counterpart, gradient hydrogels with gradient distributions of cyanide groups were prepared.

关键词： hydrogel     uniform     gradient     dialdehyde cellulose     ammonia gas     diamine    

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A three-dimensional two-level gradient smoothing meshfree method for rainfall induced landslide simulations

Dongdong WANG, Jiarui WANG, Junchao WU, Junjun DENG, Ming SUN

《结构与土木工程前沿（英文）》 2019年 第13卷 第2期   页码 337-352 doi: 10.1007/s11709-018-0467-5

摘要： A three-dimensional two-level gradient smoothing meshfree method is presented for rainfall induced landslide simulations. The two-level gradient smoothing for meshfree shape function is elaborated in the three-dimensional Lagrangian setting with detailed implementation procedure. It is shown that due to the successive gradient smoothing operation without the requirement of derivative computation in the present formulation, the two-level smoothed gradient of meshfree shape function is capable of achieving a given influence domain more efficiently than the standard gradient of meshfree shape function. Subsequently, the two-level smoothed gradient of meshfree shape function is employed to discretize the weak form of coupled rainfall seepage and soil motion equations in a nodal integration format, as provides an efficient three-dimensional regularized meshfree formulation for large deformation rainfall induced landslide simulations. The exponential damage and pressure dependent plasticity relationships are utilized to describe the failure evolution in landslides. The plastic response of soil is characterized by the true effective stress measure, which is updated according to the rotationally neutralized objective integration algorithm. The effectiveness of the present three-dimensional two-level gradient smoothing meshfree method is demonstrated through numerical examples.

关键词： meshfree method     landslide     rainfall     three-dimensional two-level gradient smoothing     nodal integration    

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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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Effects of gradient concentration on the microstructure and electrochemical performance of LiNi

Wenming Li, Weijian Tang, Maoqin Qiu, Qiuge Zhang, Muhammad Irfan, Zeheng Yang, Weixin Zhang

《化学科学与工程前沿（英文）》 2020年 第14卷 第6期   页码 988-996 doi: 10.1007/s11705-020-1918-9

摘要： Nickel(Ni)-rich layered materials have attracted considerable interests as promising cathode materials for lithium ion batteries (LIBs) owing to their higher capacities and lower cost. Nevertheless, Mn-rich cathode materials usually suffer from poor cyclability caused by the unavoidable side-reactions between Ni ions on the surface and electrolytes. The design of gradient concentration (GC) particles with Ni-rich inside and Mn-rich outside is proved to be an efficient way to address the issue. Herein, a series of LiNi Co Mn O (LNCM622) materials with different GCs (the atomic ratio of Ni/Mn decreasing from the core to the outer layer) have been successfully synthesized via rationally designed co-precipitation process. Experimental results demonstrate that the GC of LNCM622 materials plays an important role in their microstructure and electrochemical properties. The as-prepared GC3.5 cathode material with optimal GC can provide a shorter pathway for lithium-ion diffusion and stabilize the near-surface region, and finally achieve excellent electrochemical performances, delivering a discharge capacity over 176 mAh·g at 0.2 C rate and exhibiting capacity retention up to 94% after 100 cycles at 1 C. The rationally-designed co-precipitation process for fabricating the Ni-rich layered cathode materials with gradient composition lays a solid foundation for the preparation of high-performance cathode materials for LIBs.

关键词： gradient concentration     Ni-rich     LiNi0.6Co0.2-Mn0.2O2     electrochemical performance     lithium-ion battery    

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标题 作者 时间 类型 操作

prediction and optimization design of sustainable concrete based on squirrel search algorithm-extreme gradientboosting technique

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

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

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

Assessment of different machine learning techniques in predicting the compressive strength of self-compacting concrete

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

期刊论文

Vibration analysis of nano-structure multilayered graphene sheets using modified strain gradient theory

Amir ALLAHBAKHSHI,Masih ALLAHBAKHSHI

期刊论文

Velocity gradient elasticity for nonlinear vibration of carbon nanotube resonators

Hamid M. SEDIGHI, Hassen M. OUAKAD

期刊论文

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

期刊论文

Gradient-based compressive image fusion

Yang CHEN,Zheng QIN

期刊论文

Influence of freeze–thaw damage gradient on stress–strain relationship of stressed concrete

期刊论文

Preparation of hydrogels with uniform and gradient chemical structures using dialdehyde cellulose and

Peiwen Liu, Carsten Mai, Kai Zhang

期刊论文

A three-dimensional two-level gradient smoothing meshfree method for rainfall induced landslide simulations

Dongdong WANG, Jiarui WANG, Junchao WU, Junjun DENG, Ming SUN

期刊论文

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

期刊论文

Effects of gradient concentration on the microstructure and electrochemical performance of LiNi

Wenming Li, Weijian Tang, Maoqin Qiu, Qiuge Zhang, Muhammad Irfan, Zeheng Yang, Weixin Zhang

期刊论文