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Gradient boosting dendritic network for ultra-short-term PV power prediction

《能源前沿（英文）》 doi: 10.1007/s11708-024-0915-y

摘要： To achieve effective intraday dispatch of photovoltaic (PV) power generation systems, a reliable ultra-short-term power generation forecasting model is required. Based on a gradient boosting strategy and a dendritic network, this paper proposes a novel ensemble prediction model, named gradient boosting dendritic network (GBDD) model which can reduce the forecast error by learning the relationship between forecast residuals and meteorological factors during the training of sub-models by means of a greedy function approximation. Unlike other machine learning models, the GBDD proposed is able to make fuller use of all meteorological factor data and has a good model interpretation. In addition, based on the structure of GBDD, this paper proposes a strategy that can improve the prediction performance of other types of prediction models. The GBDD is trained by analyzing the relationship between prediction errors and meteorological factors for compensating the prediction results of other prediction models. The experimental results show that the GBDD proposed has the benefit of achieving a higher PV power prediction accuracy for PV power generation and can be used to improve the prediction performance of other prediction models.

关键词： photovoltaic (PV) power prediction     dendrite network     gradient boosting strategy    

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Application of extreme gradient boosting in predicting the viscoelastic characteristics of graphene oxide

《结构与土木工程前沿（英文）》 2024年 第18卷 第6期   页码 899-917 doi: 10.1007/s11709-024-1025-y

摘要： Complex modulus (G*) is one of the important criteria for asphalt classification according to AASHTO M320-10, and is often used to predict the linear viscoelastic behavior of asphalt binders. In addition, phase angle (φ) characterizes the deformation resilience of asphalt and is used to assess the ratio between the viscous and elastic components. It is thus important to quickly and accurately estimate these two indicators. The purpose of this investigation is to construct an extreme gradient boosting (XGB) model to predict G* and φ of graphene oxide (GO) modified asphalt at medium and high temperatures. Two data sets are gathered from previously published experiments, consisting of 357 samples for G* and 339 samples for φ, and these are used to develop the XGB model using nine inputs representing the asphalt binder components. The findings show that XGB is an excellent predictor of G* and φ of GO-modified asphalt, evaluated by the coefficient of determination R2 (R2 = 0.990 and 0.9903 for G* and φ, respectively) and root mean square error (RMSE = 31.499 and 1.08 for G* and φ, respectively). In addition, the model’s performance is compared with experimental results and five other machine learning (ML) models to highlight its accuracy. In the final step, the Shapley additive explanations (SHAP) value analysis is conducted to assess the impact of each input and the correlation between pairs of important features on asphalt’s two physical properties.

关键词： complex modulus     phase angle     graphene oxide     asphalt     extreme gradient boosting     machine learning    

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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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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）》 2023年 第17卷 第9期   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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Development of gradient boosting-assisted machine learning data-driven model for free chlorine residual

《环境科学与工程前沿（英文）》 2024年 第18卷 第2期 doi: 10.1007/s11783-024-1777-6

摘要：

● A machine learning approach was applied to predict free chlorine residuals.

关键词： Machine learning     Data-driven modeling     Drinking water treatment     Disinfection     Chlorination    

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

《结构与土木工程前沿（英文）》 2023年 第18卷 第1期   页码 30-50 doi: 10.1007/s11709-024-1041-y

摘要： 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 R2 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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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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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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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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Influence of freeze–thaw damage gradient on stress–strain relationship of stressed concrete

《结构与土木工程前沿（英文）》 2023年 第17卷 第9期   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

Gradient boosting dendritic network for ultra-short-term PV power prediction

期刊论文

Application of extreme gradient boosting in predicting the viscoelastic characteristics of graphene oxide

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

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

期刊论文

Development of gradient boosting-assisted machine learning data-driven model for free chlorine residual

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

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

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

Gradient-based compressive image fusion

Yang CHEN,Zheng QIN

期刊论文

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

期刊论文

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

期刊论文