Several modal split models have been created around the world to forecast which mode of transportation will be selected by the trip - maker from among a variety of available modes of transportation. This modeling is essential from a planning standpoint, as transportation systems typically receive significant investment. In this study, the main purpose was to develop a mode choice model using multiple linear regressions for Ramadi city in Iraq. The study area was divided into traffic analysis zones (TAZ) to facilitate data collection. The data was collected through a home interview of the trip makers in their home units through a questionnaire designed for this purpose. The result showed that the most influential factors on the mode choice for the general trips model using multiple linear regressions are car ownership, age, and trip cost. This model gave a good correlation coefficient of 0.829 meaning that the independent variables explain 82.9 of variance in the dependent variable (type of mode), which will help transport planners in developing policies and solutions for future
RSM and DOEs approach were used to optimize parameters for hypoeutectic A356 Alloy. Statistical analysis of variance (ANOVA) was adopted to identify the effects of process parameters on the performance characteristics in the inclined plate casting process of semisolid A356 alloy which are developed using the Response surface methodology (RSM) to explain the influences of two processing parameters (tilting angle and cooling length) on the performance characteristics of the Mean Particle Size (MPS) of α-Al solid phase and to obtain optimal level of the process parameters. The residuals for the particle size were found to be of significant effect on the response and the predicted regression model has extracted all available information from the experimental data. By applying regression analysis, a mathematical predictive model of the particle size was developed as a function of the inclined plate casting process parameters. In this study, the DOEs results indicated that the optimum setting was approx. (44) degree tilt angle and (42) cm cooling length with particle size (30.5) μm
Turning is the most popular machining operation. The quality of the product may be determined using a variety of metrics, such as the surface generation method and the surface roughness of the product. This work uses cutting variables to obtain the best surface quality through a mathematical model. The suggested surface generation in this work results from deriving it using the Bezier technique, with degree (5th) having six chosen control points. One of the critical indicators of the quality of machined components is the surface roughness created during the machining process. Surface roughness improvement via machining process parameter optimization has been extensively researched. The Taguchi Method and actual tests were employed for evaluating the surface quality of complicated forms; regression models with three different variables for the cutting process, such as cutting speed, depth of cut, and feed rate, were also used. According to the experimental findings, the most significant effect of feed rate on the surface roughness is approximately (40.9%), and the more minor effect of depth of cut on the surface roughness is almost (16.23%). In addition, the average percentage error is 4.93%, the maximum error is 0.14 mm, and the minimum error is -0.143 mm for the prediction using the regression equation.
Due to the expansion of industrial operations globally in recent years, waste output has risen. So these wastes must be reduced by recycling and reusing to achieve environmentally friendly buildings and find various alternative materials in critical cases. The statistical indicators are used as practical study including Multiple linear regression (MLR) and artificial neural network (ANN) models. The study's goals were to assess the effectiveness of granite waste (GW) as a replacement for cement, sand, plastic, and binder in specific building applications and the relationships between MLR and ANN approaches. Results show the efficiency of adding granite waste to some construction stages and replacing it with cement in the mixture and examining its strength, it gave excellent results in addition to good results for its use as a binder in cement mortar, while the results were weak when used as a substitute for sand and plastic in insulator because it's classified as fine sand, Therefore, it cannot be used as a substitute for sand in the construction. The statistical models give an effective indicator to use GW as an alternative material ( binder and cement) based on the coefficient of correlation (R2) for the two models MLR and ANN equal to 83.4 % and 80 % respectively.
Housing is one of the main concerns raising critical problems in the Kurdistan Region. Due to the fast growth of the urban population through the last period, the Kurdistan-Investment Board was ongoing in construction of effective amount of housing estates by investment companies. Due to the rapid increase in population, many housing estate projects neglected specific family requirements, with low commitment to housing standards, quality of dwellings, and the resident's lifestyles. This study investigates living space and services parts of dwellings in those estates, finding out the factors that direct residential satisfaction supported by fixing correlations among determinants of overall satisfaction. The adopted methodology consists of evaluation based on two steps, the first was by a technical assessment using checklist comparing spaces with Iraqi standards, while the second part was through survey of resident's satisfaction. Field data collection had consisted of a questionnaire list and data collection performed for five housing projects (10 types of dwelling units covered by eighty-three samples) selected in Erbil city. Then the questionnaire results were analyzed using the SPSS program using correlations, regression, and descriptive statistics. Low commitment to Iraqi Standards was obvious in most cases. Results also showed that dwellers were satisfied with their dwelling units in cases despite differences with Iraqi urban housing standards. For instance, in two bedrooms dwellings, areas of services were below the standard by 21% while the resident's satisfaction in this group varied between neutral and satisfied. It had been found a clear correlation between indicators of dwellings units’ spaces. The regression has shown that the indicators of the kitchen location's size and shape highly affected the householder's satisfaction. The descriptive statistics have shown the satisfaction level mostly been neutral in all projects. Finally, the study suggests some recommendations regarding dwelling units in addition to the need to amend the Iraqi standards of housing.
This research focuses on studying the speed flow density relationships which are considered the fundamental traffic flow relationships. The objective of the present study is to predict statistical models represent these relationships depending on a field survey data collected from Al-Thirthar road in Falluja city.Data were collected by using video-recording technique. The required data were abstracted, analyzed, grouped, and processed using computer programs developed for this purpose. Standard statistical analysis techniques were used to examine and analyze the observed data.FWASIM simulation traffic software program was used to verify the predicted traffic stream models, while the obtained results were presented in this research. To test the validity and reliability of the model, the output results of the predicated model were compared with the output data obtained from FWASIM model using similar input data and segment geometry. The comparison leads to consider that the developed regression model may be used to evaluate the performance of urban streets in Falluja city.
In this paper the artificial neural network used to predict dilly evaporation. The model was trained in MATLAB with five inputs. The inputs are Min. Temperature, Max. Temperature, average temperature, wind speed and humidity. The data collected from Alramadi meteorological station for one year. The transfer function models are sigmoid and tangent sigmoid in hidden and output layer, it is the most commonly used nonlinear activation function. The best numbers of neurons used in this paper was three nodes. The results concludes, that the artificial neural network is a good technique for predicting daily evaporation, the empirical equation can be used to compute daily evaporation (Eq.6) with regression more than 96% for all (training, validation and testing) as well as, in this model that the Max. Temperature is a most influence factor in evaporation with importance ratio equal to (30%) then humidity (26%).