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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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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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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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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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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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忆容振荡器初值切换调控的超级多稳定性及其机理分析 Research Articles

陈蓓,徐权,陈墨,武花干,包伯成

《信息与电子工程前沿（英文）》 2021年 第22卷 第11期   页码 1517-1531 doi: 10.1631/FITEE.2000622

摘要： 超级多稳定性以其丰富多样的动力学状态和工程应用中的极大灵活性受到科学家们关注。利用4个线性电路元件和一个具有余弦逆忆容值的非线性荷控型忆容元件，构造了一个四维忆容振荡器。四维忆容振荡器具有一个线平衡集，其稳定性随忆容的初始条件周期性演化。由于周期性演化的稳定性，四维忆容振荡器展现了初值切换调控的超级多稳定性。通过分岔图、李雅普诺夫指数和相轨图，揭示了周期倍增/减半分岔、混沌危机和初值切换共存吸引子的复杂动力学行为。在此基础上，通过积分变换得到一个重构系统，揭示了忆容振荡器中初值切换调控超级多稳定性的形成机理。最后设计了重构系统的实现电路，并进行了PSIM电路仿真，验证了数值分析的有效性。

关键词： 超级多稳定性；初值切换调控；忆容振荡器；机理分析    

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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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气候变暖背景下的极端天气气候事件与防灾减灾

翟盘茂,刘静

《中国工程科学》 2012年 第14卷 第9期   页码 55-63

摘要：

首先概括极端天气气候事件以及“气候极值”的相关定义，并把极端事件分为单要素的极端事件、与天气现象有关的极端事件、多要素极端事件和极端气候事件。在此基础上，总结上述几类极端事件在气候变暖背景下的变化趋势及影响。指出气候变暖背景下我国长江中下游区域强降水事件更趋频繁，我国东部地区高温热浪天气更为明显；东北华北地区干旱趋势增加，尤其在20世纪末期和21世纪初期最为明显；近10年来西南地区干旱频繁发生。为减轻日益增加的重大气象灾害的损失，我国有必要加强高影响极端事件的监测、预警能力建设，同时还必须根据极端天气气候事件变化规律加强工程性防御措施，以防范和应对强降水引发的洪涝灾害和城市渍涝，以及与降水持续不足有关的重大干旱和高温热浪等气象灾害。

关键词： 极端气候指数     高影响     气象灾害     工程    

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Concrete corrosion in wastewater systems: Prediction and sensitivity analysis using advanced extreme

Mohammad ZOUNEMAT-KERMANI, Meysam ALIZAMIR, Zaher Mundher YASEEN, Reinhard HINKELMANN

《结构与土木工程前沿（英文）》 2021年 第15卷 第2期   页码 444-460 doi: 10.1007/s11709-021-0697-9

摘要： The implementation of novel machine learning models can contribute remarkably to simulating the degradation of concrete due to environmental factors. This study considers the sulfuric acid corrosive factor in wastewater systems to simulate concrete mass loss using five machine learning models. The models include three different types of extreme learning machines, including the standard, online sequential, and kernel extreme learning machines, in addition to the artificial neural network, classification and regression tree model, and statistical multiple linear regression model. The reported values of concrete mass loss for six different types of concrete are the target values of the machine learning models. The input variability was assessed based on two scenarios prior to the application of the predictive models. For the first assessment, the machine learning models were developed using all the available cement and concrete mixture input variables; the second assessment was conducted based on the gamma test approach, which is a sensitivity analysis technique. Subsequently, the sensitivity analysis of the most effective parameters for concrete corrosion was tested using three different approaches. The adopted methodology attained optimistic and reliable modeling results. The online sequential extreme learning machine model demonstrated superior performance over the other investigated models in predicting the concrete mass loss of different types of concrete.

关键词： sewer systems     environmental engineering     data-driven methods     sensitivity analysis    

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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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compressive strength of concrete containing micro-silica, nano-silica, and polypropylene fibers using extreme

Fatemeh ZAHIRI, Hamid ESKANDARI-NADDAF

《结构与土木工程前沿（英文）》 2019年 第13卷 第4期   页码 821-830 doi: 10.1007/s11709-019-0518-6

摘要： Many studies have evaluated the effects of additives such as nano-silica (NS), micro-silica (MS) and polymer fibers on optimizing the mechanical properties of concrete, such as compressive strength. Nowadays, with progress in cement industry provides, it has become possible to produce cement type I with strength classes of 32.5, 42.5, and 52.5 MPa. On the one hand, the microstructure of cement has changed, and modified by NS, MS, and polymers; therefore it is very important to determine the optimal percentage of each additives for those CSCs. In this study, 12 mix designs containing different percentages of MS, NS, and polymer fibers in three cement strength classes(CSCs) (32.5, 42.5, and 52.5 MPa) were designed and constructed based on the mixture method. Results indicated the sensitivity of each CSCs can be different on the NS or MS in compressive strength of concrete. Consequently, strength classes have a significant effect on the amount of MS and NS in mix design of concrete. While, polymer fibers don’t have significant effect in compressive strength considering CSCs.

关键词： mixture method     compressive strength     nano-silica     micro-silica     polypropylene fibers    

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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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标题 作者 时间 类型 操作

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

期刊论文

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

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

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

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

忆容振荡器初值切换调控的超级多稳定性及其机理分析

陈蓓,徐权,陈墨,武花干,包伯成

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

气候变暖背景下的极端天气气候事件与防灾减灾

翟盘茂,刘静

期刊论文

Concrete corrosion in wastewater systems: Prediction and sensitivity analysis using advanced extreme

Mohammad ZOUNEMAT-KERMANI, Meysam ALIZAMIR, Zaher Mundher YASEEN, Reinhard HINKELMANN

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

compressive strength of concrete containing micro-silica, nano-silica, and polypropylene fibers using extreme

Fatemeh ZAHIRI, Hamid ESKANDARI-NADDAF

期刊论文

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

期刊论文