جستجوی مقالات مرتبط با کلیدواژه « regression » در نشریات گروه « عمران »

Core testing is the most direct method to assess the in-situ concrete compressive strength in an existing structure, generally related to suspected construction malpractice or deficiency of concrete supply, to carry out the condition assessment of buildings before taking up repair and upgrading work. Although this test is quite simple to conduct, the results obtained may sometimes contain considerable errors because of the great variety of parameters involved. The general problems of core testing are well known. The factors including core diameter, length-to-diameter ratio (L/D), concrete age, aggregate characteristics, direction of coring and the moisture condition at the time of testing are known which affect the relationship between core strength and the corresponding standard cube or cylinder strength are fully reported by researchers. Another potential factor influencing the testing of cores is the presence of reinforcing bars within the core. The effects of the presence of steel bars on the strength of cores have been investigated by only a few researchers. Reinforcement bars passing through a core will increase the uncertainty of results and should be avoided wherever possible. Regression analysis and generalized GMDH network, whose structure is investigated using genetic algorithm and single-particle number optimization method for predicting the compressive strength of concrete using the results of coring tests with and without fittings. The form and ability of the multivariate linear regression models and the importance of regression coefficients based on the experimental data obtained for samples in two different processing conditions, in order to predict the cubic compressive strength of the concrete and using the input parameters including (1) the length to diameter ratio Core, (2) core diameter, (3) diameter, (4) number and (5) axial axial axis of the rebar in the core, (6) reinforcement of the rebar, and (7) core compressive strength as independent and input variables, as well as resistance Concrete pressure is evaluated as the response variable (or output of the models). This method is used for the GMDH neural network. The objective of the GMDH neural network method is to obtain a polynomial function that can be used to retrieve the output parameter by the input of the considered variables. The GMDH neural network can, after training, estimate the relationship between inputs and outputs in a polynomial, which depends on the accuracy of this polynomial on the data and structure of the network. The single-particle decomposition (SVD) method in the GMDH structure for the case where the number of equations is greater than that of unknowns, uses the least squares error method to solve such devices. The results showed that the models used have high ability to express the problem, since more than 95% of variations of response variables with fitted models in regression models and about 99% of changes in the response variable values ​​in the GMDH model can be expressed. But in a comparative position, GMDH model with a general structure optimized with Genetic Algorithm and SVD has shown the best performance, with this superiority becoming noticeable, considering that about 75% of the data is involved in training the neural model. Subsequently, nonlinear regression models show a certain advantage over linear models.

This study explores multiple mathematical models for estimating house prices in Tehran, Iran. Estimation of house prices is vital from various perspectives. Estimated prices can be used in the planning, design, and construction of residential building projects. Based on the estimated future prices of residential units, investors and constructors can conduct an investment analysis and mitigate the risk of the financial failure of residential projects. Reducing the investment risk of residential projects can lead to jobs creation and economic growth. Several factors such as previous house prices, population changes, house construction costs, and seasonal effects can significantly affect house prices. In this research, first, the macro and microeconomic factors that affect house prices were reviewed, and relevant data were collected. The next step was data preparation and pre-processing. Then, the data was used to train several models using regression and time series analysis models, namely Autoregressive Integrated Moving Average and Vector Autoregression. The current house prices can be estimated using the regression model based on a set of independent variables such as the age of the building. The ARIMA model receives the output of the regression model and estimate house price in the subsequent year. Alternatively, the Vector Autoregression model can be used independently to estimate future prices. To compare the performance of the models, their error was measured by two methods Mean Absolute Percentage Error and Relative Standard Error. The error of the Autoregression model is less than the combination of regression and ARIMA models because in the Autoregression model, the effect of independent variables is directly applied in the model. The models developed in this research can help decision-makers, investors, developers, homebuyers, and financial institutes obtain appropriately estimated prices and make informed decisions in construction projects.

Accidents in Iran pose a significant public health burden, requiring effective prevention and management strategies. Latent variable modelling provides a promising approach to understanding the complex factors that contribute to accidents in Iran. This paper reviews the current state of knowledge of latent variable modelling in Iran's accidents. The paper provides a synthesis of the literature on the measurement and modelling of latent variables in accidents, including human factors, environmental factors, and organizational factors, and also reviews the common statistical techniques used in the analysis of latent variables, such as structural equation modelling, latent class analysis, and factor analysis. The strengths and limitations of these approaches are discussed. The review shows that although some studies have applied latent variable modelling to accidents in Iran, the use of these techniques is still relatively limited in comparison to other countries. Overall, It has been argued that latent variable modelling can offer valuable insights into the underlying mechanisms of accidents in Iran and guide more effective strategies for prevention and management. This study used data from accidents resulting in the death or injury of passengers gathered on Iranian roadways in 2015, and the latent class model was fitted to this data using the NLOGIT6 software. And the findings have been reported in this article. The findings suggest that elements such as the season and day of the accident, as well as weather conditions, have an impact on the severity of accidents.

Retrofit actions are amongst the most commonly used measures for reducing the seismic vulnerability of buildings. For any given building, multiple seismic retrofit options are often available. Each option has specific requirements, cost, and performance. Estimating the cost of each candidate action is essential to the selection, planning, and implementation of seismic retrofit initiatives. Primary cost estimation plays a vital role in allocating budget for retrofit projects. Past studies used a variety of methods to develop cost estimation models. This research harnesses the capabilities of various regression models via modern machine learning methods for cost estimation. A dataset from 167 retrofit projects for masonry school buildings in Iran was used to develop models. Three main retrofit actions were implemented in the projects, namely Shotcrete, Steel belt, and Fiber reinforced polymer. Several regression methods including multiple linear regression, ridge regression, lasso regression, and also elastic net regression were applied to the dataset. The proposed framework comprised 12 models, which were attained by four regression methods on three retrofit actions. The cross-validation method was used for model evaluation in order to use all available data for training and testing. The model at the beginning of the development process contained all the probable effective parameters. Next, to increase the simplicity and accuracy of the models, a simple model reduction method was implemented. This model reduction method eliminated almost two-thirds of the parameters in the majority of basic models. Then, the candidate models were evaluated in terms of quantity and quality of prediction, heteroscedasticity, autocorrelation of residuals, and non-normality. This paper identifies the height of the building as the most influential parameter governing retrofit cost. Furthermore, lateral area of walls, footprint area, and added lateral strength are influential in the mentioned retrofit actions. This research contributes to enhancing the understanding of the factors, the effects, and the costs of the retrofit actions.

In this study, four concrete types, including ordinary Portland cement concrete, fly ash concrete, slag concrete, and slag-fly ash concrete, are taken into account in order to estimate their compressive strength by two novel machine learning methods (genetic algorithm and soccer league competition algorithm), and four types of regressions (linear, 2nd order polynomial, exponential, and logarithmic). Subsequently, the precision of prediction models are compared based on performance indicators, and the most accurate models are applied in the optimization problem modeling. Drawing on results, the most precise model to estimate the compressive strength of ordinary Portland cement concrete is the genetic algorithm, and the soccer league competition is the most accurate model to estimate the strength of other concrete types. Afterward, a model is developed so as to design mixture proportions of 40MPa concretes. Fly ash concrete, slag-fly ash concrete, and slag concrete reduce the unit cost by 35.2%, 29.9%, and 23.1%, respectively, compared with ordinary Portland cement concrete. Fly ash concrete, slag-fly ash concrete, slag concrete, and ordinary Portland cement concrete require 217.25 kg, 150.47 kg, 102 kg, and 414.64 kg cement to be manufactured. Furthermore, the slag concrete can reduce the amount of cement in the mixture proportion by 75.4%, and it is the most eco-friendly concrete.

Travel time prediction as an essential issue has been scrutinized in recent decades. To this end, various techniques are applied to estimate travel duration in the dynamic networks and intelligent transportation systems. Accordingly, in this investigation, prediction of travel time is considered by machine learning techniques. Initially, the experimental test is planned, and the travel time effective parameters are spotted. Subsequently, with the assistance of the floating car method, and Mytacks application, the data are collected in six elected roads. After data preparation, stop delay, grades, and the number of the lane are determined as the most effective travel time criteria. In this study, a novel machine learning technique based on the coyote optimization algorithm is introduced, and its precision is compared with five conventional regression models. Drawing on results, the accuracy of the coyote optimization algorithm-based machine learning technique is more than that of other prediction methods. The coefficient of determination of the introduced machine learning technique for training and testing data is equal to 0.746 and 0.724, respectively. Furthermore, coyote optimization algorithm-based machine learning estimates 73% of testing data with an error of fewer than 20 seconds.

For the preparation of small-scale maps from large-scale maps, generalization of vector features is important. This method increases the quality of maps published, enables the analysis of data at various levels of detail, and reduces the volume needed to store them. The methods of linear and polygonal features generalization are performed with the aim of preserving their geometry and area while reducing their details. Various models have been used and evaluated by researchers in this field; However, most of them summarize the features with the aim of selecting a few points from them and deleting other points. Even so, the deleted points may contain valuable information for this complication and their removal will lead to a defect in its geometry and area. In this study, the generalization of multi-linear features was performed using minimizing the vertical distance from the main line. In order to study the proposed model, after its implementation on different shapes, the multi-lines of Lake Urmia and its islands were generalized and the results of the proposed model were compared with the common Douglas-Poker and Viswalingam methods. The results were then evaluated using the indices of area differences, the similarity of the mean curvature, the similarity of the amount of angle changes and the modified average Hausdorff distance. The results showed an average superiority of 99.91, 66.29 and 60.99% compared to conventional simplification approaches with proposed model (in the first three indicators). The proposed model has a advantage over the Douglas-Poker and Viswalingam methods 0.16 and 0.2 percent based on the area difference index, 7 and 5 percent based on the average curvature index, and 6 and 2 percent based on the  sharp change index. but In the the modified average Hausdorff distance index,  it was about 2 meters worse than the aforementioned methods, due to the lack of reliance on the initial points of the complication.

Labyrinth weirs are among the hydraulic structures that are constructed to regulate the water level and control the flow in canals, rivers and reservoirs of dams. This structure is designed to transmit large currents in low heads by increasing the effective length of the overflow crown. The crest axis of this type of weirs is indirect and in showing the horizontal surface, the overflow is composed of interconnected walls. Labyrinth weirs are repeated with triangular, trapezoidal, rectangular and arc geometries alternating in flow width. The main criterion in the labyrinth design of the weirs was to increase the flow transmission capacity on the overflow with a fixed canopy and for a certain height of the water level upstream of the weir. In this study, 145 laboratory data were used, including four types of arc labyrinth weirs with different arc radius and tangent lengths. The discharge efficiency was introduced as a dimensionless parameter  using dimensional analysis. The relationships between  and the critical depth of flow (y_C/P) were obtained using graphs for two different types, R2 = 0.983 and R2 = 0.998. Then a graph to y_(C_Lt )/P and H_t/P a relation to calculate the critical depth were presented. The results showed that the calculated flow rate obtained using yc is consistent with laboratory values so that the relationship between them is R2 = 0.982.Labyrinth weirs are among the hydraulic structures that are constructed to regulate the water level and control the flow in canals, rivers and reservoirs of dams. This structure is designed to transmit large currents in low heads by increasing the effective length of the overflow crown. The crest axis of this type of weirs is indirect and in showing the horizontal surface, the overflow is composed of interconnected walls. Labyrinth weirs are repeated with triangular, trapezoidal, rectangular and arc geometries alternating in flow width. The main criterion in the labyrinth design of the weirs was to increase the flow transmission capacity on the overflow with a fixed canopy and for a certain height of the water level upstream of the weir. In this study, 145 laboratory data were used, including four types of arc labyrinth weirs with different arc radius and tangent lengths. The discharge efficiency was introduced as a dimensionless parameter  using dimensional analysis. The relationships between  and the critical depth of flow (y_C/P) were obtained using graphs for two different types, R2 = 0.983 and R2 = 0.998. Then a graph to y_(C_Lt )/P and H_t/P a relation to calculate the critical depth were presented. The results showed that the calculated flow rate obtained using yc is consistent with laboratory values so that the relationship between them is R2 = 0.982.Labyrinth weirs are among the hydraulic structures that are constructed to regulate the water level and control the flow in canals, rivers and reservoirs of dams. This structure is designed to transmit large currents in low heads by increasing the effective length of the overflow crown. The crest axis of this type of weirs is indirect and in showing the horizontal surface, the overflow is composed of interconnected walls. Labyrinth weirs are repeated with triangular, trapezoidal, rectangular and arc geometries alternating in flow width. The main criterion in the labyrinth design of the weirs was to increase the flow transmission capacity on the overflow with a fixed canopy and for a certain height of the water level upstream of the weir. In this study, 145 laboratory data were used, including four types of arc labyrinth weirs with different arc radius and tangent lengths. The discharge efficiency was introduced as a dimensionless parameter  using dimensional analysis. The relationships between  and the critical depth of flow (y_C/P) were obtained using graphs for two different types, R2 = 0.983 and R2 = 0.998. Then a graph to y_(C_Lt )/P and H_t/P a relation to calculate the critical depth were presented. The results showed that the calculated flow rate obtained using yc is consistent with laboratory values so that the relationship between them is R2 = 0.982.Labyrinth weirs are among the hydraulic structures that are constructed to regulate the water level and control the flow in canals, rivers and reservoirs of dams. This structure is designed to transmit large currents in low heads by increasing the effective length of the overflow crown. The crest axis of this type of weirs is indirect and in showing the horizontal surface, the overflow is composed of interconnected walls. Labyrinth weirs are repeated with triangular, trapezoidal, rectangular and arc geometries alternating in flow width. The main criterion in the labyrinth design of the weirs was to increase the flow transmission capacity on the overflow with a fixed canopy and for a certain height of the water level upstream of the weir. In this study, 145 laboratory data were used, including four types of arc labyrinth weirs with different arc radius and tangent lengths. The discharge efficiency was introduced as a dimensionless parameter  using dimensional analysis. The relationships between  and the critical depth of flow (y_C/P) were obtained using graphs for two different types, R2 = 0.983 and R2 = 0.998. Then a graph to y_(C_Lt )/P and H_t/P a relation to calculate the critical depth were presented. The results showed that the calculated flow rate obtained using yc is consistent with laboratory values so that the relationship between them is R2 = 0.982.

In the Least-Square Fitting method, instead of using the absolute magnitude of the error, we consider its square. Therefore, minimizing the total number of error squares leads to the magnification, which considers smaller errors. The most common way to determine the parameters that determine the curve is to determine the direction of error reduction and a small step in that direction and repeat the process until we reach convergence. This process of resolving parameters repeatedly is also known as the "gradient descent" method. In this tutorial, we use basic matrix calculations and use them to get the parameters for the best fit of the curve.

The leakage losses from the unlined canals are a major contributor to water losses in the agricultural sector. An understanding of the nature of leakage losses can help to improved water conveyance efficiency and provides solutions to solving water scarcity problems. In this research, numerical simulation was used to study the effective factors on seepage from earth canals. To verify the available information, some of the earthen canals in Zayandehrud irrigation network were used. A number of 246 numerical models including different sections of trapezoidal, rectangular and triangular earthen canals were performed using SEEP/W software. The results showed that, for numerical simulation a width of 15 times the width of the water surface in the canal would be required for the modelling of the left and right lateral boundaries. The comparison of seepage with empirical relationships showed that the empirical relationships show a large error in the seepage estimation, although Moritz's relation with the coefficient of determination 0.373 was better than the rest. Linear and nonlinear multivariate regression relationships provide a suitable match for seepage discharge estimation. Linear relationship was preferable due to small root mean square error (RMSE) and its simplicity. Wetted perimeter has been distinguished effective parameter in seepage from canal, but canal side slope had low effect on seepage. It is suggested that in future studies, the effect of groundwater depth on leakage from the canal should be considered.

With increasing urbanization, transportation technology has progressed and developed economy. In addition, roads and highways, have made in an effort to balance road capacity and demand. Knowing the capacity of a road is necessary in the planning, design and operation of roads. For an accurate estimate of the roads capacity, passenger car equivalent coefficients, are essential to provide a mechanism through which the vehicles are converted into reference vehicles. This paper discusses about urban highways, such as Tehran, which is significantly high degree of heterogeneity, and based on collected data from 8 highway in Tehran, presents passenger car equivalent factors from four different existing methods. in the following, obtained passenger car equivalent factors are compared with each other and with factors that are currently used in different traffic studies of Tehran. Significant differences are observed among obtained passenger car equivalent factors from each method