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Development of machine learning multi-city model for municipal solid waste generation prediction

《环境科学与工程前沿（英文）》 2022年 第16卷 第9期 doi: 10.1007/s11783-022-1551-6

摘要：

● A database of municipal solid waste (MSW) generation in China was established.

关键词： Municipal solid waste     Machine learning     Multi-cities     Gradient boost regression tree    

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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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An innovative model for predicting the displacement and rotation of column-tree moment connection under

Mohammad Ali NAGHSH, Aydin SHISHEGARAN, Behnam KARAMI, Timon RABCZUK, Arshia SHISHEGARAN, Hamed TAGHAVIZADEH, Mehdi MORADI

《结构与土木工程前沿（英文）》 2021年 第15卷 第1期   页码 194-212 doi: 10.1007/s11709-020-0688-2

摘要： In this study, we carried out nonlinear finite element simulations to predict the performance of a column-tree moment connection (CTMC) under fire and static loads. We also conducted a detailed parameter study based on five input variables, including the applied temperature, number of flange bolts, number of web bolts, length of the beam, and applied static loads. The first variable is changed among seven levels, whereas the other variables are changed among three levels. Employing the Taguchi method for variables 2–5 and their levels, 9 samples were designed for the parameter study, where each sample was exposed to 7 different temperatures yielding 63 outputs. The related variables for each output are imported for the training and testing of different surrogate models. These surrogate models include a multiple linear regression (MLR), multiple Ln equation regression (MLnER), an adaptive network-based fuzzy inference system (ANFIS), and gene expression programming (GEP). 44 samples were used for training randomly while the remaining samples were employed for testing. We show that GEP outperforms MLR, MLnER, and ANFIS. The results indicate that the rotation and deflection of the CTMC depend on the temperature. In addition, the fire resistance increases with a decrease in the beam length; thus, a shorter beam can increase the fire resistance of the building. The numbers of flanges and web bolts slightly affect the rotation and displacement of the CTMCs at temperatures of above 400°C.

关键词： column-tree moment connection     Finite element model     parametric study     fire     regression models     gene expression programming    

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Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regressionand M5 model tree

Tanvi SINGH, Mahesh PAL, V. K. ARORA

《结构与土木工程前沿（英文）》 2019年 第13卷 第3期   页码 674-685 doi: 10.1007/s11709-018-0505-3

摘要： M5 model tree, random forest regression (RF) and neural network (NN) based modelling approaches were used to predict oblique load carrying capacity of batter pile groups using 247 laboratory experiments with smooth and rough pile groups. Pile length ( ), angle of oblique load ( ), sand density ( ), number of batter piles ( ), and number of vertical piles ( ) as input and oblique load ( ) as output was used. Results suggest improved performance by RF regression for both pile groups. M5 model tree provides simple linear relation which can be used for the prediction of oblique load for field data also. Model developed using RF regression approach with smooth pile group data was found to be in good agreement for rough piles data. NN based approach was found performing equally well with both smooth and rough piles. Sensitivity analysis using all three modelling approaches suggest angle of oblique load ( ) and number of batter pile ( ) affect the oblique load capacity for both smooth and rough pile groups.

关键词： batter piles     oblique load test     neural network     M5 model tree     random forest regression     ANOVA    

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图引导正则最小化的随机超梯度的交替方向方法 None

Qiang LAN, Lin-bo QIAO, Yi-jie WANG

《信息与电子工程前沿（英文）》 2018年 第19卷 第6期   页码 755-762 doi: 10.1631/FITEE.1601771

摘要： 提出并比较额外梯度交替方向的几种随机变体方法，称为带拉格朗日函数（SEGL）的随机超梯度交替方向法和带增广拉格朗日函数（SEGAL）的随机超梯度交替方向法。这些方法由两个大规模凸目标函数组成，可最小化图形引导的优化问题。机器学习中一些重要应用遵循图导引优化公式等作为线性回归、逻辑回归、Lasso结构化扩展以及结构化正则化逻辑回归的原则。通过融合逻辑回归和图形引导正则化回归，在几类数据集上进行了试验。试验结果表明所提算法优于其他竞争算法，且在实际应用中，SEGAL比SEGL性能更好。

关键词： 随机优化；图形引导最小化；超梯度法；融合逻辑回归；图导向正则化逻辑回归    

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Estimation of flexible pavement structural capacity using machine learning techniques

Nader KARBALLAEEZADEH, Hosein GHASEMZADEH TEHRANI, Danial MOHAMMADZADEH SHADMEHRI, Shahaboddin SHAMSHIRBAND

《结构与土木工程前沿（英文）》 2020年 第14卷 第5期   页码 1083-1096 doi: 10.1007/s11709-020-0654-z

摘要： The most common index for representing structural condition of the pavement is the structural number. The current procedure for determining structural numbers involves utilizing falling weight deflectometer and ground-penetrating radar tests, recording pavement surface deflections, and analyzing recorded deflections by back-calculation manners. This procedure has two drawbacks: falling weight deflectometer and ground-penetrating radar are expensive tests; back-calculation ways has some inherent shortcomings compared to exact methods as they adopt a trial and error approach. In this study, three machine learning methods entitled Gaussian process regression, M5P model tree, and random forest used for the prediction of structural numbers in flexible pavements. Dataset of this paper is related to 759 flexible pavement sections at Semnan and Khuzestan provinces in Iran and includes “structural number” as output and “surface deflections and surface temperature” as inputs. The accuracy of results was examined based on three criteria of , , and . Among the methods employed in this paper, random forest is the most accurate as it yields the best values for above criteria ( =0.841, =0.592, and =0.760). The proposed method does not require to use ground penetrating radar test, which in turn reduce costs and work difficulty. Using machine learning methods instead of back-calculation improves the calculation process quality and accuracy.

关键词： transportation infrastructure     flexible pavement     structural number prediction     Gaussian process regression     M5P model tree     random forest    

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Kd-tree and quad-tree decompositions for declustering of 2D range queries over uncertain space

Ahmet SAYAR,Süleyman EKEN,Okan ÖZTÜRK

《信息与电子工程前沿（英文）》 2015年 第16卷 第2期   页码 98-108 doi: 10.1631/FITEE.1400165

摘要： We present a study to show the possibility of using two well-known space partitioning and indexing techniques, kd trees and quad trees, in declustering applications to increase input/output (I/O) parallelization and reduce spatial data processing times. This parallelization enables time-consuming computational geometry algorithms to be applied efficiently to big spatial data rendering and querying. The key challenge is how to balance the spatial processing load across a large number of worker nodes, given significant performance heterogeneity in nodes and processing skews in the workload.

关键词： Kd tree     Quad tree     Space partitioning     Spatial indexing     Range queries     Query optimization    

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Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical

《机械工程前沿（英文）》 2021年 第16卷 第4期   页码 814-828 doi: 10.1007/s11465-021-0650-6

摘要： The fault diagnosis of bearings is crucial in ensuring the reliability of rotating machinery. Deep neural networks have provided unprecedented opportunities to condition monitoring from a new perspective due to the powerful ability in learning fault-related knowledge. However, the inexplicability and low generalization ability of fault diagnosis models still bar them from the application. To address this issue, this paper explores a decision-tree-structured neural network, that is, the deep convolutional tree-inspired network (DCTN), for the hierarchical fault diagnosis of bearings. The proposed model effectively integrates the advantages of convolutional neural network (CNN) and decision tree methods by rebuilding the output decision layer of CNN according to the hierarchical structural characteristics of the decision tree, which is by no means a simple combination of the two models. The proposed DCTN model has unique advantages in 1) the hierarchical structure that can support more accuracy and comprehensive fault diagnosis, 2) the better interpretability of the model output with hierarchical decision making, and 3) more powerful generalization capabilities for the samples across fault severities. The multiclass fault diagnosis case and cross-severity fault diagnosis case are executed on a multicondition aeronautical bearing test rig. Experimental results can fully demonstrate the feasibility and superiority of the proposed method.

关键词： bearing     cross-severity fault diagnosis     hierarchical fault diagnosis     convolutional neural network     decision tree    

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

《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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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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中国油用牡丹工程的战略思考

李育材

《中国工程科学》 2014年 第16卷 第10期   页码 58-63

摘要：

作为一种原产于我国的多年生小灌木，油用牡丹具有抗性强、适应范围广、产量高和油质好等特点。大力发展油用牡丹对促进我国油料生产、保障粮油安全、改善生态环境、增加农民收入、帮助贫困地区农民脱贫致富等都具有十分重要的意义。本文介绍了发展油用牡丹的重要意义，并通过分析油用牡丹发展中现存的问题，提出了对我国油用牡丹工程发展的建议与思考。

关键词： 油用牡丹     木本油料     工程     战略思考    

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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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Real-time simulation platform for photovoltaic system with a boost converter using MPPT algorithm in

Geethanjali PURUSHOTHAMAN, Vimisha VENUGOPALAN, Aleena Mariya VINCENT

《能源前沿（英文）》 2013年 第7卷 第3期   页码 373-379 doi: 10.1007/s11708-013-0272-8

摘要： Recently, real-time simulation of renewable energy sources are indispensible for evaluating the performance of the maximum power point tracking (MPPT) controller, especially in the photovoltaic (PV) system in order to reduce cost in the testing phase. Nowadays, real time PV simulators are obtained by using analog and/or digital components. In this paper, a real-time simulation of a PV system with a boost converter was proposed using only the digital signal processor (DSP) processor with two DC voltage sources to emulate the temperature and irradiation in the PV system. A MATLAB/Simulink environment was used to develop the real-time PV system with a boost converter into a C-program and build it into a DSP controller TMS320F28335. Besides, the performance of the real-time DSP-based PV was tested in different temperature and irradiation conditions to observe the P-V and V-I characteristics. Further, the performance of the PV with a boost converter was tested at different temperatures and irradiations using MPPT algorithms. This scheme was tested through simulation and the results were validated with that of standard conditions given in the PV data sheets. Implementation of this project helped to attract more researchers to study renewable energy applications without real sources. This might facilitate the study of PV systems in a real-time scenario and the evaluation of what should be expected for PV modules available in the market.

关键词： photovoltaic (PV) module     digital signal processor (DSP) controller     power electronic converter     real-time simulation    

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Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured

《环境科学与工程前沿（英文）》 2023年 第17卷 第6期 doi: 10.1007/s11783-023-1667-3

摘要：

● Hybrid deep-learning model is proposed for water quality prediction.

关键词： Water quality prediction     Soft-sensor     Anaerobic process     Tree-structured Parzen Estimator    

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Rational design on photoelectrodes and devices to boost photoelectrochemical performance of solar-driven

《化学科学与工程前沿（英文）》 2022年 第16卷 第6期   页码 777-798 doi: 10.1007/s11705-022-2148-0

摘要： As an eco-friendly, efficient, and low-cost technique, photoelectrochemical water splitting has attracted growing interest in the production of clean and sustainable hydrogen by the conversion of abundant solar energy. In the photoelectrochemical system, the photoelectrode plays a vital role in absorbing the energy of sunlight to trigger the water splitting process and the overall efficiency depends largely on the integration and design of photoelectrochemical devices. In recent years, the optimization of photoelectrodes and photoelectrochemical devices to achieve highly efficient hydrogen production has been extensively investigated. In this paper, a concise review of recent advances in the modification of nanostructured photoelectrodes and the design of photoelectrochemical devices is presented. Meanwhile, the general principles of structural and morphological factors in altering the photoelectrochemical performance of photoelectrodes are discussed. Furthermore, the performance indicators and first principles to describe the behaviors of charge carriers are analyzed, which will be of profound guiding significance to increasing the overall efficiency of the photoelectrochemical water splitting system. Finally, current challenges and prospects for an in-depth understanding of reaction mechanisms using advanced characterization technologies and potential strategies for developing novel photoelectrodes and advanced photoelectrochemical water splitting devices are demonstrated.

关键词： photoelectrochemical water splitting     photoelectrodes     hydrogen production     charge separation     catalytic mechanism    

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标题 作者 时间 类型 操作

Development of machine learning multi-city model for municipal solid waste generation prediction

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

An innovative model for predicting the displacement and rotation of column-tree moment connection under

Mohammad Ali NAGHSH, Aydin SHISHEGARAN, Behnam KARAMI, Timon RABCZUK, Arshia SHISHEGARAN, Hamed TAGHAVIZADEH, Mehdi MORADI

期刊论文

Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regressionand M5 model tree

Tanvi SINGH, Mahesh PAL, V. K. ARORA

期刊论文

图引导正则最小化的随机超梯度的交替方向方法

Qiang LAN, Lin-bo QIAO, Yi-jie WANG

期刊论文

Estimation of flexible pavement structural capacity using machine learning techniques

Nader KARBALLAEEZADEH, Hosein GHASEMZADEH TEHRANI, Danial MOHAMMADZADEH SHADMEHRI, Shahaboddin SHAMSHIRBAND

期刊论文

Kd-tree and quad-tree decompositions for declustering of 2D range queries over uncertain space

Ahmet SAYAR,Süleyman EKEN,Okan ÖZTÜRK

期刊论文

Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical

期刊论文

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

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

中国油用牡丹工程的战略思考

李育材

期刊论文

Velocity gradient elasticity for nonlinear vibration of carbon nanotube resonators

Hamid M. SEDIGHI, Hassen M. OUAKAD

期刊论文

Real-time simulation platform for photovoltaic system with a boost converter using MPPT algorithm in

Geethanjali PURUSHOTHAMAN, Vimisha VENUGOPALAN, Aleena Mariya VINCENT

期刊论文

Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured

期刊论文

Rational design on photoelectrodes and devices to boost photoelectrochemical performance of solar-driven

期刊论文