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Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive

Faezehossadat KHADEMI,Mahmoud AKBARI,Sayed Mohammadmehdi JAMAL,Mehdi NIKOO

《结构与土木工程前沿（英文）》 2017年 第11卷 第1期   页码 90-99 doi: 10.1007/s11709-016-0363-9

摘要： Evaluating the in situ concrete compressive strength by means of cores cut from hardened concrete is acknowledged as the most ordinary method, however, it is very difficult to predict the compressive strength of concrete since it is affected by many factors such as different mix designs, methods of mixing, curing conditions, compaction, etc. In this paper, considering the experimental results, three different models of multiple linear regression model (MLR), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) are established, trained, and tested within the Matlab programming environment for predicting the 28 days compressive strength of concrete with 173 different mix designs. Finally, these three models are compared with each other and resulted in the fact that ANN and ANFIS models enables us to reliably evaluate the compressive strength of concrete with different mix designs, however, multiple linear regression model is not feasible enough in this area because of nonlinear relationship between the concrete mix parameters. Finally, the sensitivity analysis (SA) for two different sets of parameters on the concrete compressive strength prediction are carried out.

关键词： concrete     28 days compressive strength     multiple linear regression     artificial neural network     ANFIS     sensitivity analysis (SA)    

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Multiple regression models for energy consumption of office buildings in different climates in China

Siyu ZHOU, Neng ZHU

《能源前沿（英文）》 2013年 第7卷 第1期   页码 103-110 doi: 10.1007/s11708-012-0220-z

摘要： The energy consumption of office buildings in China has been growing significantly in recent years. Obviously, there are significant relationships between building envelope and the energy consumption of office buildings. The 8 key building envelope influencing factors were found in this paper to evaluate their effects on the energy consumption of the air-conditioning system. The typical combinations of the key influencing factors were performed in Trnsy simulation. Then on the basis of the simulated results, the multiple regression models were developed respectively for the four climates of China—hot summer and warm winter, hot summer and cold winter, cold, and severely cold. According to the analysis of regression coefficients, the appropriate building envelope design schemes were discussed in different climates. At last, the regression model evaluations consisting of the simulation evaluations and the actual case evaluations were performed to verify the feasibility and accuracy of the regression models. The error rates are within±5% in the simulation evaluations and within±15% in the actual case evaluations. It is believed that the regression models developed in this paper can be used to estimate the energy consumption of office buildings in different climates when various building envelope designs are considered.

关键词： regression model     energy consumption     building envelope     office building     different climates    

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A method to predict cooling load of large commercial buildings based on weather forecast and internal occupancy

Junbao JIA,Jincheng XING,Jihong LING,Ren PENG

《能源前沿（英文）》 2016年 第10卷 第4期   页码 459-465 doi: 10.1007/s11708-016-0424-8

摘要： Considering the fact that customers of large commercial buildings have the characteristics of the higher density and randomness, this paper presented an air-conditioning cooling load prediction method based on weather forecast and internal occupancy density. The multiple linear feedback regression model was applied to predict, with precision, the air conditioning cooling load. Case analysis showed that the largest mean relative error of hourly and the daily predicting cooling load maximum were 18.1% and 5.14%, respectively.

关键词： commercial building     load prediction     multiple linear regression    

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Models of thermodynamic and transport properties of POE VG68 and R410A/POE VG68 mixture

WEI Wenjian, DING Guoliang, HU Haitao, WANG Kaijian

《能源前沿（英文）》 2008年 第2卷 第2期   页码 227-234 doi: 10.1007/s11708-008-0020-7

摘要： The thermodynamic properties of a refrigerant-oil mixture are the foundation to predict the performance of air-conditioning and refrigeration systems and to evaluate the influence of oil on heat transfer and pressure drop. Models of the thermodynamic and transport properties of POE VG68 and R410A/POE VG68 mixture were provided based on the analysis of state-of-the-art correlations. New models were developed by modifying the coefficients in existing correlations with multiple regression method according to experimental data. The maximum deviation of the predicted values of these models to the experimental data is within 5%. These models can be used for R410A/POE VG68 to obtain accurate and reliable thermodynamic and transport parameters to evaluate the influence of POE VG68 on the performance of an R410A air-conditioning and refrigeration system.

关键词： multiple regression     foundation     thermodynamic     influence     air-conditioning    

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Presentation of machine learning methods to determine the most important factors affecting road traffic accidents on rural roads

Hamid MIRZAHOSSEIN; Milad SASHURPOUR; Seyed Mohsen HOSSEINIAN; Vahid Najafi Moghaddam GILANI

《结构与土木工程前沿（英文）》 2022年 第16卷 第5期   页码 657-666 doi: 10.1007/s11709-022-0827-z

摘要： The purpose of this research was to develop statistical and intelligent models for predicting the severity of road traffic accidents (RTAs) on rural roads. Multiple Logistic Regression (MLR) was used to predict the likelihood of RTAs. For more accurate prediction, Multi-Layer Perceptron (MLP) and Radius Basis Function (RBF) neural networks were applied. Results indicated that in MLR, the model obtained from the backward method with the correct percent of 84.7% and R2 value of 0.893 was the best method for predicting the likelihood of RTAs. Also, MLR showed that the variables of not paying attention to the front not paying attention to the frontroad ahead, followed byand then vehicle-motorcycle/bike accidents were the greatest problems. Among the models, MLP had a better performance, so that the prediction accuracy of MLR, MLP, and RBF were 84.7%, 96.7%, and 92.1%, respectively. MLP model, due to higher accuracy, showed that the variable of reason of accident had the highest effect on the prediction of accidents, and considering MLR results, the variables of not paying attention to the front and then vehicle-motorcycle/bike accidents had the most influence on the occurrence of accidents. Therefore, motorcyclists and cyclists are more prone to accidents, and appropriate solutions should be adopted to enhance their safety.

关键词： safety     rural accidents     multiple logistic regression     artificial neural networks    

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Estimation of photolysis half-lives of dyes in a continuous-flow system with the aid of quantitative structure-property relationship

Davoud BEIKNEJAD,Mohammad Javad CHAICHI

《环境科学与工程前沿（英文）》 2014年 第8卷 第5期   页码 683-692 doi: 10.1007/s11783-014-0680-y

摘要： In this paper the photolysis half-lives of the model dyes in water solutions and under ultraviolet (UV) radiation were determined by using a continuous-flow spectrophotometric method. A quantitative structure-property relationship (QSPR) study was carried out using 21 descriptors based on different chemometric tools including stepwise multiple linear regression (MLR) and partial least squares (PLS) for the prediction of the photolysis half-life ( ) of dyes. For the selection of test set compounds, a K-means clustering technique was used to classify the entire data set, so that all clusters were properly represented in both training and test sets. The QSPR results obtained with these models show that in MLR-derived model, photolysis half-lives of dyes depended strongly on energy of the highest occupied molecular orbital ( ), largest electron density of an atom in the molecule ( ) and lipophilicity (log ). While in the model derived from PLS, besides aforementioned and descriptors, the molecular surface area ( ), molecular weight ( ), electronegativity ( ), energy of the second highest occupied molecular orbital ( ) and dipole moment ( ) had dominant effects on logt values of dyes. These were applicable for all classes of studied dyes (including monoazo, disazo, oxazine, sulfonephthaleins and derivatives of fluorescein). The results were also assessed for their consistency with findings from other similar studies.

关键词： dye     photolysis half-life     quantitative structure-property relationship     continuous-flow     stepwise multiple linear regression     partial least squares    

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Relationships of nitrous oxide fluxes with water quality parameters in free water surface constructed wetlands

Juan WU, Jian ZHANG, Wenlin JIA, Huijun XIE, Bo ZHANG

《环境科学与工程前沿（英文）》 2009年 第3卷 第2期   页码 241-247 doi: 10.1007/s11783-009-0023-6

摘要： The effects of chemical oxygen demand (COD) concentration in the influent on nitrous oxide (N O) emissions, together with the relationships between N O and water quality parameters in free water surface constructed wetlands, were investigated with laboratory-scale systems. N O emission and purification performance of wastewater were very strongly dependent on COD concentration in the influent, and the total N O emission in the system with middle COD influent concentration was the least. The relationships between N O and the chemical and physical water quality variables were studied by using principal component scores in multiple linear regression analysis to predict N O flux. The multiple linear regression model against principal components indicated that different water parameters affected N O flux with different COD concentrations in the influent, but nitrate nitrogen affected N O flux in all systems.

关键词： free water surface constructed wetland     nitrous oxide emission     water quality parameter     principal component analysis     multiple linear regression    

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Integrated uncertain models for runoff forecasting and crop planting structure optimization of the Shiyang River Basin, north-west China

Fan ZHANG, Mo LI, Shanshan GUO, Chenglong ZHANG, Ping GUO

《农业科学与工程前沿（英文）》 2018年 第5卷 第2期   页码 177-187 doi: 10.15302/J-FASE-2017177

摘要： To improve the accuracy of runoff forecasting, an uncertain multiple linear regression (UMLR) model is presented in this study. The proposed model avoids the transfer of random error generated in the independent variable to the dependent variable, as this affects prediction accuracy. On this basis, an inexact two-stage stochastic programming (ITSP) model is used for crop planting structure optimization (CPSO) with the inputs that are interval flow values under different probabilities obtained from the UMLR model. The developed system, in which the UMLR model for runoff forecasting and the ITSP model for crop planting structure optimization are integrated, is applied to a real case study. The aim of the developed system is to optimize crops planting area with limited available water resources base on the downstream runoff forecasting in order to obtain the maximum system benefit in the future. The solution obtained can demonstrate the feasibility and suitability of the developed system, and help decision makers to identify reasonable crop planting structure under multiple uncertainties.

关键词： crop planting structure optimization     inexact two-stage stochastic programming     runoff forecasting     Shiyang River Basin     uncertain multiple linear regression    

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Dynamic response surface methodology using Lasso regression for organic pharmaceutical synthesis

《化学科学与工程前沿（英文）》 2022年 第16卷 第2期   页码 221-236 doi: 10.1007/s11705-021-2061-y

摘要： To study the dynamic behavior of a process, time-resolved data are collected at different time instants during each of a series of experiments, which are usually designed with the design of experiments or the design of dynamic experiments methodologies. For utilizing such time-resolved data to model the dynamic behavior, dynamic response surface methodology (DRSM), a data-driven modeling method, has been proposed. Two approaches can be adopted in the estimation of the model parameters: stepwise regression, used in several of previous publications, and Lasso regression, which is newly incorporated in this paper for the estimation of DRSM models. Here, we show that both approaches yield similarly accurate models, while the computational time of Lasso is on average two magnitude smaller. Two case studies are performed to show the advantages of the proposed method. In the first case study, where the concentrations of different species are modeled directly, DRSM method provides more accurate models compared to the models in the literature. The second case study, where the reaction extents are modeled instead of the species concentrations, illustrates the versatility of the DRSM methodology. Therefore, DRSM with Lasso regression can provide faster and more accurate data-driven models for a variety of organic synthesis datasets.

关键词： data-driven modeling     pharmaceutical organic synthesis     Lasso regression     dynamic response surface methodology    

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Interaction behavior and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based

《结构与土木工程前沿（英文）》 2021年 第15卷 第5期   页码 1181-1198 doi: 10.1007/s11709-021-0744-6

摘要： In the recent era, piled raft foundation (PRF) has been considered an emergent technology for offshore and onshore structures. In previous studies, there is a lack of illustration regarding the load sharing and interaction behavior which are considered the main intents in the present study. Finite element (FE) models are prepared with various design variables in a double-layer soil system, and the load sharing and interaction factors of piled rafts are estimated. The obtained results are then checked statistically with nonlinear multiple regression (NMR) and artificial neural network (ANN) modeling, and some prediction models are proposed. ANN models are prepared with Levenberg–Marquardt (LM) algorithm for load sharing and interaction factors through backpropagation technique. The factor of safety (FS) of PRF is also estimated using the proposed NMR and ANN models, which can be used for developing the design strategy of PRF.

关键词： interaction     load sharing ratio     piled raft     nonlinear regression     artificial neural network    

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Multivariable regression model for Fox depth correction factor

Ravi Kant MITTAL, Sanket RAWAT, Piyush BANSAL

《结构与土木工程前沿（英文）》 2019年 第13卷 第1期   页码 103-109 doi: 10.1007/s11709-018-0474-6

摘要： This paper presents a simple and efficient equation for calculating the Fox depth correction factor used in computation of settlement reduction due to foundation embedment. Classical solution of Boussinesq theory was used originally to develop the Fox depth correction factor equations which were rather complex in nature. The equations were later simplified in the form of graphs and tables and referred in various international code of practices and standard texts for an unsophisticated and quick analysis. However, these tables and graphs provide the factor only for limited values of the input variables and hence again complicates the process of automation of analysis. Therefore, this paper presents a non-linear regression model for the analysis of effect of embedment developed using “IBM Statistical Package for the Social Sciences” software. Through multiple iterations, the value of coefficient of determination is found to reach 0.987. The equation is straightforward, competent and easy to use for both manual and automated calculation of the Fox depth correction factor for wide range of input values. Using the developed equation, parametric study is also conducted in the later part of the paper to analyse the extent of effect of a particular variable on the Fox depth factor.

关键词： settlement     embedment     Fox depth correction factor     regression     multivariable    

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of driver-response relationships: identifying factors using a novel framework integrating quantile regression

《环境科学与工程前沿（英文）》 2023年 第17卷 第6期 doi: 10.1007/s11783-023-1676-2

摘要：

● A novel framework integrating quantile regression with machine learning is proposed.

关键词： Driver-response     Upper boundary of relationship     Interpretable machine learning     Quantile regression     Total phosphorus     Chlorophyll a    

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compressive strength of soil-RAP blend stabilized with Portland cement using multivariate adaptive regression

Ali Reza GHANIZADEH, Morteza RAHROVAN

《结构与土木工程前沿（英文）》 2019年 第13卷 第4期   页码 787-799 doi: 10.1007/s11709-019-0516-8

摘要： The recycled layer in full-depth reclamation (FDR) method is a mixture of coarse aggregates and reclaimed asphalt pavement (RAP) which is stabilized by a stabilizer agent. For design and quality control of the final product in FDR method, the unconfined compressive strength of stabilized material should be known. This paper aims to develop a mathematical model for predicting the unconfined compressive strength (UCS) of soil-RAP blend stabilized with Portland cement based on multivariate adaptive regression spline (MARS). To this end, two different aggregate materials were mixed with different percentages of RAP and then stabilized by different percentages of Portland cement. For training and testing of MARS model, total of 64 experimental UCS data were employed. Predictors or independent variables in the developed model are percentage of RAP, percentage of cement, optimum moisture content, percent passing of #200 sieve, and curing time. The results demonstrate that MARS has a great ability for prediction of the UCS in case of soil-RAP blend stabilized with Portland cement ( is more than 0.97). Sensitivity analysis of the proposed model showed that the cement, optimum moisture content, and percent passing of #200 sieve are the most influential parameters on the UCS of FDR layer.

关键词： full-depth reclamation     soil-reclaimed asphalt pavement blend     Portland cement     unconfined compressive strength     multivariate adaptive regression spline    

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Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach

Pijush Samui, Jagan J

《结构与土木工程前沿（英文）》 2013年 第7卷 第2期   页码 133-136 doi: 10.1007/s11709-013-0202-1

摘要： This article examines the capability of Gaussian process regression (GPR) for prediction of effective stress parameter ( ) of unsaturated soil. GPR method proceeds by parameterising a covariance function, and then infers the parameters given the data set. Input variables of GPR are net confining pressure ( ), saturated volumetric water content ( ), residual water content ( ), bubbling pressure ( ), suction ( ) and fitting parameter ( ). A comparative study has been carried out between the developed GPR and Artificial Neural Network (ANN) models. A sensitivity analysis has been done to determine the effect of each input parameter on . The developed GPR gives the variance of predicted . The results show that the developed GPR is reliable model for prediction of of unsaturated soil.

关键词： unsaturated soil     effective stress parameter     Gaussian process regression (GPR)     artificial neural network (ANN)     variance    

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海洋二号扫描微波辐射计冷空定标和地球物理参数反演研究

周武,林明森,李延民,王振占,黄磊

《中国工程科学》 2013年 第15卷 第7期   页码 75-80

摘要：

本文研究了海洋二号（HY-2A）扫描微波辐射计冷空定标方法和数据反演算法。针对HY-2A扫描微波辐射计对地观测和定标扫描的设计原理和观测几何，基于微波辐射计的对地观测数据修正进入冷空反射器的地球信号，建立地面观测亮温的冷空反射器权重系数矩阵修正冷空观测信号；基于海面温度、海面风速、水汽含量和云液态含量的微波辐射计辐射传输模型，计算HY-2A扫描微波辐射计不同条件下的各频率极化方式下的理论亮温，建立线性回归模型，拟合不同地球物理参数的反演系数，采用多元线性回归算法反演HY-2A微波扫描辐射计地球物理参数，对比国外成熟微波辐射计数据，得到产品精度。

关键词： 扫描微波辐射计     多元线性回归     星星交叉     冷空定标    

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标题 作者 时间 类型 操作

Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive

Faezehossadat KHADEMI,Mahmoud AKBARI,Sayed Mohammadmehdi JAMAL,Mehdi NIKOO

期刊论文

Multiple regression models for energy consumption of office buildings in different climates in China

Siyu ZHOU, Neng ZHU

期刊论文

A method to predict cooling load of large commercial buildings based on weather forecast and internal occupancy

Junbao JIA,Jincheng XING,Jihong LING,Ren PENG

期刊论文

Models of thermodynamic and transport properties of POE VG68 and R410A/POE VG68 mixture

WEI Wenjian, DING Guoliang, HU Haitao, WANG Kaijian

期刊论文

Presentation of machine learning methods to determine the most important factors affecting road traffic accidents on rural roads

Hamid MIRZAHOSSEIN; Milad SASHURPOUR; Seyed Mohsen HOSSEINIAN; Vahid Najafi Moghaddam GILANI

期刊论文

Estimation of photolysis half-lives of dyes in a continuous-flow system with the aid of quantitative structure-property relationship

Davoud BEIKNEJAD,Mohammad Javad CHAICHI

期刊论文

Relationships of nitrous oxide fluxes with water quality parameters in free water surface constructed wetlands

Juan WU, Jian ZHANG, Wenlin JIA, Huijun XIE, Bo ZHANG

期刊论文

Integrated uncertain models for runoff forecasting and crop planting structure optimization of the Shiyang River Basin, north-west China

Fan ZHANG, Mo LI, Shanshan GUO, Chenglong ZHANG, Ping GUO

期刊论文

Dynamic response surface methodology using Lasso regression for organic pharmaceutical synthesis

期刊论文

Interaction behavior and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based

期刊论文

Multivariable regression model for Fox depth correction factor

Ravi Kant MITTAL, Sanket RAWAT, Piyush BANSAL

期刊论文

of driver-response relationships: identifying factors using a novel framework integrating quantile regression

期刊论文

compressive strength of soil-RAP blend stabilized with Portland cement using multivariate adaptive regression

Ali Reza GHANIZADEH, Morteza RAHROVAN

期刊论文

Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach

Pijush Samui, Jagan J

期刊论文

海洋二号扫描微波辐射计冷空定标和地球物理参数反演研究

周武,林明森,李延民,王振占,黄磊

期刊论文