clustering

The hot and humid region of Iran experiences extremely hot summers with high humidity, making it one of the most challenging climates globally. Analyzing the features of vernacular houses in these areas can offer valuable insights for modern housing design. One of the key challenges researchers encounter in architectural typology studies is selecting appropriate case studies. Bandar Kong, a coastal city along the Persian Gulf, features traditional houses with four main components: windcatchers, Sabat (shaded walkways), main rooms (living areas), and yards, along with non-living spaces. Understanding the organization of these elements can help develop a typology of vernacular houses in Bandar Kong.

One of the key applications of machine learning methods recently employed in architectural research is the measurement of similarity in architectural images. Categorizing and describing architectural features within each category is essential for identifying architectural types. Previous studies have utilized cosine similarity for measuring the similarity of architectural plans. Cosine similarity measurement criterion is particularly effective for evaluating sparse vectors and is commonly used in positive spaces with a range of [0,1]. Due to the diverse nature of architectural data, this method has proven effective for evaluating plan image similarities. The aim of this research is to apply machine learning techniques to select case studies and cluster the houses of Bandar Kong based on the shape and arrangement of windcatchers, sabat, courtyards, and living spaces. For this, Anaconda version 3.9 and Jupiter 6.4.5 were utilized. The cosine distance algorithm was employed to measure similarity in terms of shape and spatial relationships. The hierarchical algorithm, using the average linkage method, was used to extract and categorize the features of each plan.

According to the analysis, the architectural plans of Bandar Kong houses can be divided in 3 different clusters. Scatter diagrams of each cluster can shows characteristics of each cluster. According to the scatter diagram, the length, width, and height consistently fall within the ranges of 2.5-3.5 meters for length and width, and 9-9.5 meters for height. By analyzing the scatter diagram of the characteristics of each cluster, the following results have been extracted. In the first cluster, the windcatcher is located in the east, and the sabbat or courtyard is located on the west side of it. The main rooms are mostly located on the north side and the service spaces are located on the east and west sides. In the second cluster, the windcatcher is centrally placed on the west side of the house. Here, the plan layout tends to extend along a north-south axis, with living rooms positioned on both the west and east sides. In the third cluster, the windcatcher is located on the west side of the plan. In this category, the extension of the plans is mostly east-west. The northern side of the windcatcher typically features the Gatieh room, and in most plans in this group, the wind room connects to either the northern room or the Gatieh. According to the similarity measurement, the plans of Younesi, Golbat and Karchi houses have the highest shape similarity and spatial relationships with other plans.

Nowadays, with the growing volume of data and the complexity of data analysis, software solutions are increasingly used across various fields, including architecture, to minimize errors. One major challenge in architectural research is the classification and selection of case studies for analyzing architectural types. In this study, after evaluating the shape similarity and spatial relationships of architectural plans, the Younesi, Karchi, and Golbat houses were selected as case studies due to their highest similarity in both shape and spatial relations compared to other plans. Using a hierarchical classification method with average linkage, the plans were grouped into three main categories. The defining characteristics of each category were extracted from the charts and compared with the corresponding case study from each group. As a result, the Karchi house represents the first category, the Golbat house represents the second, and the Younesi house represents the third, with their respective features aligning closely with the extracted characteristics of each category.

The aim of the research is to use and apply the artificial intelligence network and data mining of the non-form pattern in the ten valuable landmark buildings of Tehran (1330s to 1350s) in the direction of modernization.

In the present study, the research method used in terms of purpose is applied-developmental and the method of study is descriptive-survey in terms of method and nature. In this research, the MLP (Multilayer perceptron) artificial intelligence network and clustering have been used to validate the non-form analysis of residential building plans in the period 1330-1350. The data were randomly divided into three sets, 70% of the data were used for training, 15% for validation, and 15% for testing.

According to the analysis and matching with non-formal analysis, the results show that plans have 15, 14, 13 and 11 components in terms of non-form. which exactly corresponds to the plan's amorphous analytical tables. Therefore, the results of the non-form analysis of the plans have been validated by artificial intelligence.

Modernization of buildings and preservation of historical buildings are important for the majority of people and the results of this research showed that by using modern technology such as creating an artificial intelligence network, it is possible to find the invisible and hidden components in the plans of the mentioned period and use them in today's residential plans. The use of modern technologies such as artificial intelligence in order to cluster and identify the hidden relationships of plans can be very helpful.

The quality of pedestrian access in urban areas as one of the main pillars in the theory of Transit-Oriented Development (TOD), affects the ability and motivation of individuals to use public transportation. The evaluation of pedestrian access to public transport station centers has considered as the main goal of this research. A set of pedestrian access parameters for public transportation, including walking distance, number of transit stations, transport penetration, parking, connectivity, and transport modes have been reviewed. In addition, using self-organized neural network (SOM) technique, clustering and then analyzing the quality level of each cluster based on the characteristics of each parameter has discussed. In order to evaluate the results, studies on zone 6 of Tehran have been implemented. The results show that about 37% of the area is at the best pedestrian access level, 35% at good level, 26% in moderate level and only 2% in weak level.

The construction management of Mehr Housing projects, which has an increasing volume and is required to high speed, can be appropriately possible just through receiving and processing the reliable data and information on the right time. Long-term programs, which have been made by managers and can determine the whole levels of Mehr Housing projects, their executive process and the people’s operation who are involved in Mehr Housing project, are entirely on the basis of existing data mining. Hashtgerd New Town Development Corporation is one of the most fundamental administrators of Mehr Housing project throughout the country and therefore, an efficient construction management is essential to execute the projects in this town. Considering the fact that more than 80 projects of Mehr Housing project are being executed in Hashtgerd New Town and regarding that the memoranda of understanding have been contracted and operated in a short time, assembling the appropriate information in the field of contractors and projects is fundamental to apply the long-term policies and coherent methods. To cluster the contractors of Mehr Housing projects in this research, firstly the required data has been collected with regard to these contractors and then they have been clustered through cluster analysis method. Correctness of achieved findings has been certified through validation methods. Finally, contractors have been ranked in every cluster by VIKOR as a multiple criteria decision-making method.