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Presentation of regression analysis, GP and GMDH models to predict the pedestrian density in various

Iraj BARGEGOL; Seyed Mohsen HOSSEINIAN; Vahid NAJAFI MOGHADDAM GILANI; Mohammad NIKOOKAR; Alireza OROUEI

《结构与土木工程前沿（英文）》 2022年第16卷第2期页码 250-265 doi: 10.1007/s11709-021-0785-x

摘要： In this study, the relationship between space mean speed (SMS), flow rate and density of pedestrians was investigated in different pedestrian facilities, including 1 walkway, 2 sidewalks, 2 signalized crosswalks and 2 mid-block crosswalks. First, statistical analysis was performed to investigate the normality of data and correlation of variables. Regression analysis was then applied to determine the relationship between SMS, flow rate, and density of pedestrians. Finally, two prediction models of density were obtained using genetic programming (GP) and group method of data handling (GMDH) models, and k-fold and holdout cross-validation methods were used to evaluate the models. By the use of regression analysis, the mathematical relationships between variables in all facilities were calculated and plotted, and the best relationships were observed in flow rate-density diagrams. Results also indicated that GP had a higher R² than GMDH in the prediction of pedestrian density in terms of flow rate and SMS, suggesting that GP was better able to model SMS and pedestrian density. Moreover, the application of k-fold cross-validation method in the models led to better performances compared to the holdout cross-validation method, which shows that the prediction models using k-fold were more reliable. Finally, density relationships in all facilities were obtained in terms of SMS and flow rate.

关键词： pedestrian density regression analysis GP model GMDH model

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Identifying factors that influence soil heavy metals by using categorical regression analysis: A case

Jun Yang, Jingyun Wang, Pengwei Qiao, Yuanming Zheng, Junxing Yang, Tongbin Chen, Mei Lei, Xiaoming Wan, Xiaoyong Zhou

《环境科学与工程前沿（英文）》 2020年第14卷第3期 doi: 10.1007/s11783-019-1216-2

摘要： A method was proposed to identify the main influence factors of soil heavy metals. The influence degree of different environmental factors was ranked. Parent material, soil type, land use and industrial activity were main factors. Interactions between some factors obviously affected soil heavy metal distribution. Identifying the factors that influence the heavy metal contents of soil could reveal the sources of soil heavy metal pollution. In this study, a categorical regression was used to identify the factors that influence soil heavy metals. First, environmental factors were associated with soil heavy metal data, and then, the degree of influence of different factors on the soil heavy metal contents in Beijing was analyzed using a categorical regression. The results showed that the soil parent material, soil type, land use type, and industrial activity were the main influencing factors, which suggested that these four factors were important sources of soil heavy metals in Beijing. In addition, population density had a certain influence on the soil Pb and Zn contents. The distribution of soil As, Cd, Pb, and Zn was markedly influenced by interactions, such as traffic activity and land use type, industrial activity and population density. The spatial distribution of soil heavy metal hotspots corresponded well with the influencing factors, such as industrial activity, population density, and soil parent material. In this study, the main factors affecting soil heavy metals were identified, and the degree of their influence was ranked. A categorical regression represents a suitable method for identifying the factors that influence soil heavy metal contents and could be used to study the genetic process of regional soil heavy metal pollution.

关键词： Soil Heavy metal Influencing factor Categorical regression Identification method

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Seismic analysis of steel structures considering damage cumulation

SHEN Zuyan, WU Aihui

《结构与土木工程前沿（英文）》 2007年第1卷第1期页码 1-11 doi: 10.1007/s11709-007-0001-7

摘要： The research on the development of a reliable analytical model for seismic analysis of steel structures is presented. The non-linear damage cumulation hysteretic model incorporating the deterioration of stiffness, strength and strain hardening for structural steel is proposed and validated. The complete loading history, energy dissipation and the effect of the maximum plastic strain are taken into account in the model. The constants in the model are determined from regression analysis of experimental results of simple standard tensile and cyclic tests. Finite element formulations for beam and structural solid element considering the damage cumulation are derived. A computer program capable of calculating the hysteretic model of steel members, predicting the damage state and crack initiation, and carrying out non-linear time history seismic analysis of steel structures is developed. Solutions obtained from the model are in good agreement with experimental results. It was demonstrated that the damage cumulation effect is considerable and important in structural seismic analysis.

关键词： hardening regression analysis computer program cumulation hysteretic strength

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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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Response surface regression analysis on FeCrBSi particle in-flight properties by plasma spray

Runbo MA,Lihong DONG,Haidou WANG,Shuying CHEN,Zhiguo XING

《机械工程前沿（英文）》 2016年第11卷第3期页码 250-257 doi: 10.1007/s11465-016-0401-2

摘要：

This work discusses the interactive effects between every two of argon flow rate, voltage, and spray distance on in-flight particles by plasma spray and constructs models that can be used in predicting and analyzing average velocity and temperature. Results of the response surface methodology show that the interactive effects between voltage and spray distance on particle in-flight properties are significant. For a given argon flow rate, particle velocity and temperature response surface are obviously bending, and a saddle point exists. With an increase in spray distance, the interactive effects between voltage and argon flow rate on particle in-flight properties appear gradually and then weaken. With an increase in voltage, the interactive effects between spray distance and argon flow rate on particle in-flight properties change from appearing to strengthening and then to weakening.

关键词： particle velocity particle temperature interactive effects response surface

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Seismic performance of viaducts with probabilistic method

ZHU Xi, WANG Jianmin

《结构与土木工程前沿（英文）》 2007年第1卷第3期页码 267-273 doi: 10.1007/s11709-007-0034-y

摘要： Due to the uncertainty of both ground motions and structural capacity, it is necessary to consider the seismic performance of viaduct structures using the probabilistic method. The risk is quantified by a procedure on the basis of a numerical determination of the fragility curves. A group of ground motions, Large Magnitude-Short Distance Bin (LMSR-N), selected specially due to its response spectra, is accorded well with the corresponding spectra of the Chinese code for seismic design. The characteristic values of the curvature ductility factors for the serviceability and the damage control limit states are obtained, and two equations for estimating the characteristic values of the curvature ductility factors are developed through regression analysis. Then, the serviceability and damage control limit states were proposed. Three damage states were constituted according the results of the experiment by Pacific Earthquake Engineering Research (PEER) Center. The analytical fragility curves were obtained specifically, using both Capacity Spectrum Method (CSM) (non-linear static) analysis and Ineremental Dynamic Method (IDM) (non-linear dynamic) analysis, respectively, in this paper. The structural fragility curves developed by CSM method can help make the structural analysis simple and quick, avoiding the implementation of the dynamic response history analysis (RHA). Although the dynamic RHA requires a lot of complicated analysis for the structure, the results from RHA are reliable and accurate. Fragility curves are powerful tools for use in performance-based seismic bridge design.

关键词： uncertainty Earthquake Engineering regression analysis accurate fragility

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Parametric analysis and design equation of ultimate capacity for unstiffened overlapped CHS K-joints

CHEN Yu, ZHAO Xianzhong, CHEN Yiyi

《结构与土木工程前沿（英文）》 2008年第2卷第2期页码 107-115 doi: 10.1007/s11709-008-0014-x

摘要： A finite element model simulating an experiment on unstiffened, overlapped circular hollow structure (CHS) K-joints was generated and validated by comparing the ultimate capacities, deformation processes and failure modes of the experimental results. Using this model, the stress distribution, propagation of plasticity and the failure modes of overlapped joints with through-brace-in-compression and welded hidden seams were analyzed. The effect of geometric parameters, with or without hidden welds, and the loading hierarchy reversal of braces on the ultimate capacity of the joints were also studied. The results of finite element parametric analysis indicate that the brace-to-chord thickness ratio has relatively large effects on the failure mechanism and ultimate capacity of overlapped joints. It was also found that the absence of hidden welds has less significance on the ultimate capacity of through-brace-in-compression joints than through-brace-in tension joints. Finally, based on the design equation of gap joints, a formula predicting the ultimate capacity of overlapped CHS K-joints was derived by applying multivariate regression analysis. Results from the proposed design equation are consistent with experimental results.

关键词： regression analysis consistent unstiffened plasticity overlapped

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Parametric equations for notch stress concentration factors of rib–deck welds under bending loading

《结构与土木工程前沿（英文）》 2021年第15卷第3期页码 595-608 doi: 10.1007/s11709-021-0720-1

摘要： The effective notch stress approach for evaluating the fatigue strength of rib–deck welds requires notch stress concentration factors obtained from complex finite element analysis. To improve the efficiency of the approach, the notch stress concentration factors for three typical fatigue-cracking modes (i.e., root–toe, root–deck, and toe–deck cracking modes) were thoroughly investigated in this study. First, we developed a model for investigating the effective notch stress in rib–deck welds. Then, we performed a parametric analysis to investigate the effects of multiple geometric parameters of a rib–deck weld on the notch stress concentration factors. On this basis, the multiple linear stepwise regression analysis was performed to obtain the optimal regression functions for predicting the notch stress concentration factors. Finally, we employed the proposed formulas in a case study. The notch stress concentration factors estimated from the developed formulas show agree well with the finite element analysis results. The results of the case study demonstrate the feasibility and reliability of the proposed formulas. It also shows that the fatigue design curve of FAT225 seems to be conservative for evaluating the fatigue strength of rib–deck welds.

关键词： notch stress concentration factor rib–deck weld parametric analysis regression analysis parametric equation

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Mechanical properties of stabilized artificial organic soil

XU Riqing, GUO Yin, LIU Zengyong

《结构与土木工程前沿（英文）》 2008年第2卷第2期页码 161-165 doi: 10.1007/s11709-008-0023-9

摘要： In order to study the influence of organic matter on the mechanical properties of stabilized soil and the effect of XGL2005 on stabilizing organic soil, unconfined compressive strength tests were carried out. Test results indicated that the strength of stabilized soil decreased in the form of a logarithmic function as the organic matter content increased. In contrast, the strength increased in the form of a power function as the content of the stabilization agent increased. The strength of cement stabilized organic soil was reinforced greatly by adding the stabilizer XGL2005. Based on the law obtained from the test, a strength prediction model was established by regression analysis. The model included the influence of the curing time, the content of the cement, the organic matter content and the stabilization agent on the strength of stabilized soil.

关键词： compressive strength stabilized stabilization regression analysis stabilizer XGL2005

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PyLUR: Efficient software for land use regression modeling the spatial distribution of air pollutants

Xuying Ma, Ian Longley, Jennifer Salmond, Jay Gao

《环境科学与工程前沿（英文）》 2020年第14卷第3期 doi: 10.1007/s11783-020-1221-5

摘要： PyLUR comprises four modules for developing and applying a LUR model. It considers both conventional and novel potential predictor variables. GDAL/OGR libraries are used to do spatial analysis in the modeling and prediction. Developed on Python platform, PyLUR is rather efficient in data processing. Land use regression (LUR) models have been widely used in air pollution modeling. This regression-based approach estimates the ambient pollutant concentrations at un-sampled points of interest by considering the relationship between ambient concentrations and several predictor variables selected from the surrounding environment. Although conceptually quite simple, its successful implementation requires detailed knowledge of the area, expertise in GIS, statistics, and programming skills, which makes this modeling approach relatively inaccessible to novice users. In this contribution, we present a LUR modeling and pollution-mapping software named PyLUR. It uses GDAL/OGR libraries based on the Python platform and can build a LUR model and generate pollutant concentration maps efficiently. This self-developed software comprises four modules: a potential predictor variable generation module, a regression modeling module, a model validation module, and a prediction and mapping module. The performance of the newly developed PyLUR is compared to an existing LUR modeling software called RLUR (with similar functions implemented on R language platform) in terms of model accuracy, processing efficiency and software stability. The results show that PyLUR out-performs RLUR for modeling in the Bradford and Auckland case studies examined. Furthermore, PyLUR is much more efficient in data processing and it has a capability to handle detailed GIS input data.

关键词： LUR Air pollution modelling GIS spatial analysis GDAL/OGR Python Pollutant concentration mapping

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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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Energy consumption of 270 schools in Tianjin, China

Jincheng XING,Junjie CHEN,Jihong LING

《能源前沿（英文）》 2015年第9卷第2期页码 217-230 doi: 10.1007/s11708-015-0352-z

摘要： With the rapid development of education cause, the increasing energy consumption of school buildings is gradually causing widespread concern in recent years in China. This paper presented an analysis of energy consumption of 270 schools located in the city of Tianjin, China. The analysis focused specifically on calculating the space heating energy consumption indexes and non-heating energy consumption indexes of different types of schools, aiming at providing reliable and precise data for the government to elaborate policies and measures. The space heating energy consumption of schools adopting district heating and gas boiler were 92.04 kWh/(m ·a) and 64.25 kWh/(m ·a), respectively. Comparing to the schools without a canteen, the non-heating energy consumption index of schools with a canteen can increase by 8%–37%. Furthermore, clustering of different energy sources, the total primary energy consumption indexes were also presented. Space heating energy consumption accounted for approximately 64%–79% of the total primary energy consumption. When using time-sharing control and self-contained gas boiler instead of district heating, an amount of almost 27.8 kWh/(m ·a) and 77.5 kWh/(m ·a) can be saved respectively. Through extensive statistical analysis of the data collected, this paper demonstrated that gross floor area, heating energy source and canteen had a close relationship with the total primary energy consumption regarding complete schools. Eventually, a linear regression equation was established to make a simple prediction about the total energy consumption of existing complete schools and to estimate the energy consumption of complete schools to be built.

关键词： schools energy consumption index primary energy energy saving regression analysis

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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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标题作者时间类型操作

Presentation of regression analysis, GP and GMDH models to predict the pedestrian density in various

Iraj BARGEGOL; Seyed Mohsen HOSSEINIAN; Vahid NAJAFI MOGHADDAM GILANI; Mohammad NIKOOKAR; Alireza OROUEI

期刊论文

Identifying factors that influence soil heavy metals by using categorical regression analysis: A case

Jun Yang, Jingyun Wang, Pengwei Qiao, Yuanming Zheng, Junxing Yang, Tongbin Chen, Mei Lei, Xiaoming Wan, Xiaoyong Zhou

期刊论文

Seismic analysis of steel structures considering damage cumulation

SHEN Zuyan, WU Aihui

期刊论文

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

Response surface regression analysis on FeCrBSi particle in-flight properties by plasma spray

Runbo MA,Lihong DONG,Haidou WANG,Shuying CHEN,Zhiguo XING

期刊论文

Seismic performance of viaducts with probabilistic method

ZHU Xi, WANG Jianmin

期刊论文

Parametric analysis and design equation of ultimate capacity for unstiffened overlapped CHS K-joints

CHEN Yu, ZHAO Xianzhong, CHEN Yiyi

期刊论文

Parametric equations for notch stress concentration factors of rib–deck welds under bending loading

期刊论文

Mechanical properties of stabilized artificial organic soil

XU Riqing, GUO Yin, LIU Zengyong

期刊论文

PyLUR: Efficient software for land use regression modeling the spatial distribution of air pollutants

Xuying Ma, Ian Longley, Jennifer Salmond, Jay Gao

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

Energy consumption of 270 schools in Tianjin, China

Jincheng XING,Junjie CHEN,Jihong LING

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

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