matlab software

These days, the problem of traffic and traffic congestion on crowded streets has become an integral part of life in metropolitan areas. Intelligent traffic control will be crucial as the number of continuous road users increases and the resources provided by the current infrastructure are limited. The purpose of this study is to model traffic flow and control ramp measurement using fuzzy method; To achieve this goal, traffic ramps at the entrances are used to regulate access to the main corridor of the highway or freeway, in order to maintain the flow close to the capacity of the highway. In this research, by presenting a new solution, modeling traffic flow and controlling ramp measurement has been done by fuzzy intelligent method in MATLAB software environment. Data collection was performed using field method and on the highways of Isfahan. This algorithm tries to make the ramp control system intelligent by maintaining simplicity and non-computational complexity. According to the information received from the vehicle, which are located at certain distances from each ramp, the queue length, vehicle entry rate and demand level of each ramp are measured, and then the controller determines the minimum time and phase of the ramp according to this information. According to the obtained results, the average intensity of the factors of total queue length in the ramp, stop time in the ramp, stopping times and traffic congestion in the open loop mode is higher than the closed loop mode based on the fuzzy method algorithm.

In this study, a simplified approach is proposed for design optimization of the reinforced concrete frame considering the effects of chloride-induced corrosion. The objective function is to minimize the frame weight, and optimization will be conducted utilizing the genetic algorithm. The constraints of the optimization problem are set in a manner that bending moment of beams and axial force of columns are not exceeding the respective resistant values, and the maximum drift of the frame is not exceeding the code-defined allowable drift. In order to examine the corrosion effects, a 5-story reinforced concrete frame is optimized in two different time points 0 and 50 years in its service life. The zero time point indicates the sound uncorroded frame. Monte Carlo sampling method in Rt software is utilized to estimate the corrosion initiation time incorporating the involved uncertainties. At time point of 50 years, a cross-sectional area of steel bars in beam and column sections is reduced due to the corrosion effects. The proposed framework for the frame optimization is implemented in MATLAB software; and for computing the structural response during the optimization process, the linear static analysis of the structure in OpenSees software is conducted. Results indicate an ability of the proposed framework for design optimization of reinforced concrete frames considering the corrosion effects in their service life.

The use of FRP and other composite materials as bar or sheets is one of the most technically and economically viable options in the construction, refurbishment, and reinforcement of structures such as concrete structures. One of the most important issues to consider when using such materials is their bond strength to structural concrete. In this paper, the effect of combining ensemble prediction models with single estimation models on improving the results of single models is estimated to estimate the bond strength of GFRP bars to concrete. To this end, neural networks with predictive results inputs are first used to estimate the bond strength of GFRP to improve the best model result from the two previous models- Be. Then, by considering the prediction outputs of the first neural network model and the best single model above mentioned as input, the neural networks are again used to present a better model than the first ANN model. The final results show the reduction of the prediction error of the ANN model combined with single and ensemble methods compared to the single models previously presented, the weighted average output model of the two single models above and the ANN model. The combination of the two models usefulness a single.

The extracted traffic information from satellite images is an important issue which is concentrated during the past two decades. The overall goal of this issue is to find the quantity of vehicles by separating the light and heavy vehicles in an aerial or satellite images. Due to the time consuming manual methods in this paper, the process is carried out by using image processing techniques. Extracting this information has many applications in different fields of science and practice of urban traffic management that became a direct need for the society. Therefore, access to the upgraded information and yet available to process will be so important. The presented method in this paper is, separating and counting vehicles using satellite images based on programming technique of GUI in the MATLAB software. The carried out separation is directly proportional with the image quality, thus, by having a high resolution image we can separate the light and heavy, two-or three-axis load vehicles or even all vehicles according to the Scheme Regulations. It seems that human accuracy should be use in extraction of traffic data as an effective factor. Also the cost and time issues as a serious impediment to access this information indicates more need to this software.