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Showing 8 results for Prediction Models

Behbahani H., Mohammad Elahi S.,
Volume 1, Issue 1 (9-2003)

This Paper is the result of a research project on a pavement management system that was performed by the Transportation Division of Iran University of Science and Technology. Information used in the project was gathered from 20 zones of the Tehran Municipality. Any maintenance and repair system for roads has a number of general and coordinated activities in conjunction with programming, designing, construction, Maintenance, Evaluation, and research on road pavement. Prediction of pavement condition is one of the most important parts of, such system. Prediction models have their application at the network level as well as project level activities. At the network level it is used in predicting the condition for budget programming. While in project level it is used in economical analysis. Many factors have been used in determination of pavement condition. These factors are the design life of the pavement, loading, climatic condition, and the type of road. To be able to plan for future improvements we need to predict the future condition of the pavement. In this paper, factors affecting the prediction of pavement condition are discussed. A model is developed exclusively for Tehran based on the distress data collected.
Mahmoud Saffarzadeh, Maghsoud Pooryari,
Volume 3, Issue 2 (6-2005)

This paper specifies the relationship among various factors contributing to road accidents including geometrical design characteristics, environmental and traffic specifications, by multiple regression analysis. The main objective of this paper is identification of problems associated with the safety issue of road networks by application of accident prediction models. Data from previous accidents were used to develop the models. Results of this study showed that the rate of road accidents is to a large extent dependent on the rate of traffic volume. Type of road and land-use are other important factors influencing the number and intensity of accidents. The mountainous roads in this respect require special attention regarding their safety factors. The quantitative rate of road safety upgrading has also been specified by adding traffic lanes in road networks.
H. Behbahani, S.a. Sahaf,
Volume 5, Issue 3 (9-2007)

The available methods for predicting mechanical characteristics of pavement layers are categorized into two general groups, Destructive and Non-destructive. In destructive method, using coring and pavement subgrade and performing necessary experiments on them, the quantities of layers properties will be identified. In Non-destructive method, the attained deflection is measured by applying the loading on pavement surface using equipments such as FWD which charges the impact dynamic load, and the mechanical characteristics of pavement layers are determined using back calculations. The procedure of conducting these calculations is that by knowing the thickness of the pavement layers and assuming the initial amounts for mechanical characteristics of the layer, the attained deflection at the desired points on the pavement surface will be calculated. Then, new figures are assumed for the characteristics of layers in a reattempt and calculations are repeated again. This trial and error is continued until the produced basin deformations from the calculations with true value, differs in an acceptable range. Using this method may have no accurate and single answer, since the various compositions of layers characteristics can produce similar deformations in different points of pavement surface. In this article, using an innovative method, a measurement is taken in constructing and introducing a mathematical model for determining the elastic module of surface layer using deflections attained from FWD loading equipment. The procedure is such that by using dynamic analysis software of finite elements like ABAQUS and ANSYS, the deformation of corresponding points on the surface of the pavement will be attained by FWD loading equipment. This analysis will be performed on a number of pavements with different thicknesses and different layers properties. The susceptibility analysis of different points deformations show, which will be performed as a result of the change of properties and layers thicknesses. Using this artificial data base as well as deflection basin parameters (DBP), a measurement will be taken toward constructing a regression model for determination of asphalt layer model, i.e. Eac =f(DBP) function shall be attained. To achieve the maximum correlation coefficient, an attempt is made to use the parameters of deformations basin which has the most susceptibility in changing asphalt layer module.
R. A. Memon, G. B. Khaskheli, M. H. Dahani,
Volume 10, Issue 1 (3-2012)

Present study is an extension of earlier work carried out on two-lane two way roads in the two provinces of Pakistan i.e. N-25,

N-55 and N-5 regarding the measure of operating speed and development of operating speed prediction models. Curved sections

of two-lane rural highways are the main location of run-off road accidents. In addition to that the road alignment having

combination of geometric elements may be more harmful to the drivers than the successive features with adequate separation.

This study is carried out on two-lane two- way road along N-65 (from Sibi to Quetta). Three sections are selected for study with

thirty three horizontal curves. Continuous speed profile data was recorded with the help of VBox (GPS based device) which was

attached with a vehicle to detect vehicle position through satellite signals. VBox is new equipment with modern technology in this

field and it helps in recording continuous speed profile and saving of this information on the computer as a permanent record.

Through the regression analysis, models were developed for estimation of operating speed on horizontal curves and on tangent,

and estimation of maximum speed reduction from tangent to curve. The validation of developed model shows compatibility with

the experimental data.

K. J. Tu, Y. W. Huang,
Volume 11, Issue 4 (12-2013)

The decisions made in the planning phase of a building project greatly affect its future operation and maintenance (O&M) cost. Recognizing the O&M cost of condominiums’ common facilities as a critical issue for home owners, this research aims to develop an artificial neural network (ANN) O&M cost prediction model to assist developers and architects in effectively assessing the impacts of their decisions made in the planning phase of condominium projects on future O&M costs. A regression cost prediction model was also developed as a benchmark model for testing the predictive accuracy of the ANN model. Six critical building design attributes (building age, number of apartment units, number of floors, average sale price, total floor area, and common facility floor area) which are usually available in the project planning phase, were identified as the input factors to both models and average monthly O&M cost as the output factor. 55 of the 65 existing condominium properties randomly selected were treated as the training samples whose data were used to develop the ANN and regression models the other ten as the test samples to compare and verify the predictive performance of both models. The study results revealed that the ANN model delivers more accurate and reliable cost prediction results, with lower average absolute error around 7.2% and maximum absolute error around 16.7%, as compared with the regression model. This study shows that ANN is an effective method in predicting building O&M costs in the project planning phase. Keywords: Project management, Facility management, Common facilities, Cost modeling.
H. Shahnazari, M. A. Shahin, M. A. Tutunchian,
Volume 12, Issue 1 (1-2014)

Due to the heterogeneous nature of granular soils and the involvement of many effective parameters in the geotechnical behavior of soil-foundation systems, the accurate prediction of shallow foundation settlements on cohesionless soils is a complex engineering problem. In this study, three new evolutionary-based techniques, including evolutionary polynomial regression (EPR), classical genetic programming (GP), and gene expression programming (GEP), are utilized to obtain more accurate predictive settlement models. The models are developed using a large databank of standard penetration test (SPT)-based case histories. The values obtained from the new models are compared with those of the most precise models that have been previously proposed by researchers. The results show that the new EPR and GP-based models are able to predict the foundation settlement on cohesionless soils under the described conditions with R2 values higher than 87%. The artificial neural networks (ANNs) and genetic programming (GP)-based models obtained from the literature, have R2 values of about 85% and 83%, respectively which are higher than 80% for the GEP-based model. A subsequent comprehensive parametric study is further carried out to evaluate the sensitivity of the foundation settlement to the effective input parameters. The comparison results prove that the new EPR and GP-based models are the most accurate models. In this study, the feasibility of the EPR, GP and GEP approaches in finding solutions for highly nonlinear problems such as settlement of shallow foundations on granular soils is also clearly illustrated. The developed models are quite simple and straightforward and can be used reliably for routine design practice.
A. Ghare, A. Badar,
Volume 12, Issue 4 (12-2014)

The objective of field water measurement is to conserve water by improving management of its distribution and field application. A simple mobile flume to measure a discharge through small rectangular open channels in agricultural fields has been experimentally investigated. The flume consisting of a vertical cylinder inserted axially into the horizontal prismatic rectangular channel, referred as a simple cylindrical flume, has been calibrated. The flow rate in rectangular channel can be measured by constricting the flow due to presence of cylinder, resulting in critical flow conditions. Experiments have been performed on two simple cylindrical flumes of different diameters, to evaluate the hydraulic characteristics of subcritical incoming flow under free flow conditions. The results of laboratory experiments on the flume have been analysed and two different discharge prediction models have been developed. The two models developed for the prediction of discharge for simple cylindrical flumes developed for use in rectangular channel sections, are based on the energy concept and the direct regression approach, respectively. Both the proposed models have been validated using the limited experimental data available in the literature. Formation of critical depth at the throat section has also been verified. Plots have also been developed for the dimensionless column head and the corresponding Froude number of the incoming flow. The discharge prediction model giving the least error has been proposed for use in practice.
A.r. Hariharan, A.s. Santhi , G. Mohan Ganesh ,
Volume 13, Issue 3 (9-2015)

This research paper presents the use of wasteful supplementary cementitious materials like fly ash and silica fume to conserve the cement used in concrete. The cement industry is one of the major producers of greenhouse gases and an energy user. In this study, Portland cement was used as a basic cementitious material. Fly ash and silica fume were used as the cement replacements by weight. The replacement levels of fly ash were 30%, 40% and 50%, and silica fume were 6% and 10%. The water binder ratio was kept constant as 0.4 and super plasticizer was added based on the required workability. Results of the binary and ternary concrete mixtures compressive strength, split tensile strength and flexural tensile strength were taken for studyup to 90 days. Based on the experimental results of compressive strength, prediction models were developed using regression analysis and coefficients were proposed to find the split tensile strength and flexural strength of binary-ternary concrete mixtures at 28 and 90 days.

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