جستجوی مقالات مرتبط با کلیدواژه « Fuzzy Rules » در نشریات گروه « عمران »

Construction projects, due to their size and complexity and their unique and dynamic nature, makes them impossible to model accurately. These attributes reveal the enormous need in these projects for a proper method of risk assessment. Furthermore, the main uncertainties involved in these projects are resulted because of vagueness (e.g. in cost estimation, time estimation, etc.), so, traditional methods used in the context of risk assessment, which are mainly based on probability theory, are not satisfactory for these projects. The aim of this paper is to propose a methodology based on hierarchical fuzzy rule based expert systems to assess the total risk of construction projects and also rank the risks based on their magnitude. For this purpose, a suitable hierarchical risk breakdown structure (HRBS) for construction projects is used to develop a formal frame for qualitative risk assessment. The highest layer of HRBS is the total risk of the project and the lowest layer consists of the factors in uencing each risk. Then, for relating the dierent layers of the HRBS and developing the expert fuzzy system, a method is used to extract the fuzzy rules based on expert opinion. Finally, this fuzzy system is programmed. The eciency of the proposed model has been analyzed for assessing risks of a construction project in Tehran. Risk assessment in this case study has been undertaken from the client, contractor and consultant points of view, separately, and also after aggregation of their opinions. The results show that the presented methodology can be used to assess risks related to cost, time, quality and safety in construction projects.

Nowadays, powerful detection systems, which the learning procedure of them is black box and is not available, have been widely used to classify data. However, the understandability of the acquired knowledge from these detection systems can significantly help operator in carrying out classification performance with high accuracy and precision. Hence, knowledge acquisition in a form of fuzzy rule set is an important issue in the image processing that causes to comprehend the classification methods appropriately and to improve them subsequently. The purpose of this paper is proposing a method to extract fuzzy rules in IF-THEN form via an Adaptive Neuro-Fuzzy Inference System (ANFIS) for classification LiDAR data and digital aerial images. Detection of building and tree in urban areas needs to determine some features to perform the detection procedure; because classification algorithms decide about pixel entity based on its feature vector. These features can make the object separation possible by the textural, the spectral, and the structural characteristics. Nowadays, by increasing the number of the active and passive sensors, it is possible to record the textural, the spectral, and the structural characteristics of objects in different wavelengths by various approaches. In this paper, some potentially features were generated, and then optimal features were selected using the genetic algorithm. Using the selected optimum features, an ANFIS was used to recognize the objects accurately. In this regard, at first, the prepared training data was utilized as inputs of grid partitioning algorithm and a Sugeno fuzzy inference system with one output was generated by determining the type and the number of input membership functions, as well as theS type of output membership functions. Then, the grid partitioning algorithm figured out the best state of the membership functions after investigating the whole of the possible states. Afterwards, the training and checking data were entered into the generated ANFIS and during the training procedure, the final classifier was concluded to detect buildings and trees. Finally, by proposing a different fuzzy-based method and using the selected training data, as well as the output membership functions of the proposed ANFIS, a set of effective fuzzy rules were extracted. The proposed method has three main steps. In the first step, the tuned premise parameters (after training process) of inputs training data of ANFIS were extracted according to the mean values of the membership functions. In the second step, firstly, based on the number of membership functions of each feature, the total number of feasible fuzzy rules was determined. Then, for each training data, the fired values for all rules were computed. The rule that had the most effect in the process was chosen as the fired fuzzy rule of each training data related to the desired object class. In the third step, the fuzzy rules which has the importance more than a specified threshold in the classification procedure were extracted. The extracted fuzzy rules were considered and analyzed logically regarding the feature layers, and the results show the high capability of the fuzzy-based proposed method in extracting rules from the objects detection procedure.