Optimization Algorithms

Landslide is a geological phenomenon that occurs in the unstable slopes of mountainous areas and in some cases causes very severe human and economic losses.  Research shows that by using the classification of landslide prone areas, possible future damage can be prevented.  The purpose of this study is to produce a landslide sensitivity map for Ardabil province using two machine learning methods ANFIS and SVM and combining them with PSO and GWO metaheuristic algorithms.  For this purpose, first a landslide map of 253 points was prepared.  Among the slip points, 70% were considered for Training and the remaining 30% were used for validation.  Continuing and according to previous studies and available data, fourteen effective factors including height, slope, slope direction,profile curvature and plan curvature of the slope, land use, lithology, rainfall, distance from the road, distance from the river, distance from the fault, road density , river density and fault density were selected.  After preparing the database using MATLAB software, the combined models SVR - PSO , SVR - GWO , ANFIS - GWO and ANFIS - PSO were implemented and then the landslide sensitivity index was obtained for each model.  During the modeling process, the performance of each method was evaluated using the RMSE statistical index.  Finally, landslide sensitivity maps were generated for each model using ArcMap 10.5 software and then the accuracy of each map was estimated using the ROC curve.  The results show that the ANFIS - psd model is more efficient than the other three models. The results of ROC curve obtained by applying ANFIS - PSO  , ANFIS - GWO , SVR - PSO , SVR - GWO were 89.4, 85.7, 88.1, 88.7,respectively.

In this paper, a new nonlinear static (pushover) analysis method is presented to evaluate the displacement-based demands of steel moment resisting frames (MRFs) at the collapse prevention performance level. In this method, the modal pushover responses are integrated using modal combination coefficients, which are calculated from optimization procedures. Two metaheuristic algorithms, including particle swarm optimization and colliding bodies optimization, are utilized in for this purpose. In the proposed procedure, the collapse prevention performance level is obtained by a new suggested criterion, which is based on the onset of severe local damages at the structure. This criterion corresponds to occur backward shape in the story capacity curves. The modal combination coefficients are obtained from incremental dynamic analysis (IDA) results of 5, 9, and 11 story steel moment resisting frames. The optimized modal pushover (OMPA) method is applied to two 9 and 12 story steel MRF buildings. The results show that the proposed method is easy to implement and is accurate enough to evaluate the displacement-based responses at the CP performance level.

It is a fact that people are often looking for a way that combines the parameters of shortness, low cost, and low energy consumption. Hence traffic is one of the most influential factors in choosing the route to reach the destination. It can be said that people often prefer along with low traffic than a short one with heavy traffic. Therefore, it is clear that the main criterion for choosing a route is the traffic situation in the relevant route. Traffic has become a major social problem in all societies today. Understanding the causes of traffic and its aggravation parameters can reduce traffic problems. Meanwhile, the issue of traffic forecasting has become a goal among different nations. Since traffic can be predicted, it is possible to avoid wasting energy and time, which has become a crisis in metropolises today. But predicting traffic conditions and behavior, especially in large cities, requires management, planning, and using technologies such as GIS. In recent years, the urban transportation network has become more complex in modern societies. The reason for this is the creation of different infrastructures with the motivation of creating more convenience for the movement of citizens. The high complexity, multi-layered nature, and multi-structured nature of the urban, transportation network do not make it easy for citizens to move, and these factors may even confuse citizens more than just moving from one place to another. There is a direct link between transportation and traffic. So far, urban plans have been made to improve the traffic situation. The variability of the parameters affecting the traffic situation and its direct impact on the traffic problem has always been a big problem for different communities. Therefore, these parameters should be identified and the role of each of them on the traffic situation should be measured. Then it is possible to improve the traffic situation. To achieve this issue, the role of Geographic Information System (GIS) to solve problems that have a specific spatial and temporal dimension (such as traffic) should not be overlooked. This highlights the need for the present research to collect traffic data. If the goal of the research is achieved, it will save time, money, and energy at least. To measure traffic behavior in metropolitan areas and to achieve up-to-date traffic data, there is a need to provide and use methods to analyze traffic behavior. By achieving this goal, transportation can be prospered and the economic burden can be reduced on different communities every year. For this reason, solutions for traffic forecasting should be sought. In the meantime, the use of science called neural networks can be very practical. In this research, first, a system was designed to collect the traffic data required for the research. The issue of access to traffic data has always been a problem, which is concerned in this area. Therefore, in the present study, first, by designing a system for collecting traffic data, the desired problem was solved. In the next step, the collected traffic data is called and normalized. Then the error rate was calculated using test and training data. At this stage, the error rate was 72%. In the next step, traffic data analysis was performed using a combination of baseline and wavelet neural network, and the error rate was then calculated. The results show that using wavelet transforms is more accurate, but the error values ​​were calculated using test and training data, 28% due to the smaller number of inputs. In other words, the desirability rate was about 72%. Finally, the collected traffic data were optimized using optimization algorithms and the best point was calculated with the least possible error for each optimization algorithm.

Several studies have been performed to study the scouring depth at bridge piers. Due to the complication of the problem and variety of the hydraulic and geometric parameters affecting the scouring phenomena, a generalized relationship has not been presented yet. Therefore, adaptive neuro-fuzzy inference system (ANFIS) is an alternative to overcome these problems. This approach is an effective tool to provide the hydraulic engineers, precise estimation of the scouring depth around the bridge piers. Although a large number of former studies have just focused on scouring at bridge piers under steady flow condition and uniform-graded bed materials even by applying ANFIS model, a lack of studies exists on scouring under unsteady flood flow condition as well as for non-uniform bed materials. Generally, river beds are composed mainly of non-uniform materials. Motion of the finer sediment particles initiates results in the protective effect of greater particles, namely armoring effect on the bed surface, thereby eliminating further erosion of the bed. Furthermore, in most of the rivers the flow regime is commonly unsteady. During a flood, the maximum scouring depth regarding to the peak of the flood hydrograph would be smaller than the equilibrium scouring depth which is commonly estimated using a constant flow discharge. When the flow unsteadiness is pronounced, the difference between the maximum scouring depth and the equilibrium scouring depth is quite substantial and thus should be addressed.In the present study, armoring effect on local scouring under unsteady flow condition was investigated based on a comprehensive dataset collected by different former investigators using ANFIS model. For this purpose, two different models were constructed. The first model was based on 372 dataset collected in a practical study on different bridges in USA. Measurements were conducted under steady flow condition. The second model was developed for estimating the maximum scouring depth in the beds of uniform and armored materials under unsteady flow condition. To present a more accurate model, some strategies including; reduction of dimension and detection of outlier were used to improve the performance of calculations. Genetic algorithm and particle swarm optimization methods were applied to develop a novel hybrid learning algorithm for ANFIS model. The new hybrid learning algorithm train the antecedent part of the fuzzy rules. Then the least square method was applied for training the conclusion part of the rules. It was shown that ANFIS model gives more accurate results compared to the empirical equations. Results highlighted the effectiveness of the data on the estimations of ANFIS model. Furthermore, according to the results, this approach is potentially able to train the ANFIS model in both steady and unsteady flow conditions.