گونه بندی و استخراج ویژگی های پلان معماری با به کارگیری روش های یادگیری ماشین؛ نمونه موردی: خانه های بومی بندر کنگ (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
منطقه گرم و مرطوب ایران دارای تابستان های گرم با رطوبت بالا و از بحرانی ترین اقلیم های جهان است. مطالعه ویژگی های مسکن بومی این مناطق می تواند منجر به ارائه راهکارهای پیشنهادی برای طراحی مسکن معاصر شود. یکی از چالش هایی که پژوهشگران در مطالعه گونه های معماری با آن مواجه هستند، انتخاب نمونه های موردی می باشد. هدف این پژوهش استفاده از روش های یادگیری ماشین برای انتخاب نمونه های موردی و دسته بندی خانه های بندر کنگ بر اساس شکل و نحوه قرارگیری بادگیر و فضاهای مجاور آن است. برای این منظور از نرم افزار آناکوندا 3.9 و جوپیتر 6.4.5 استفاده شده است. سنجش شباهت از لحاظ شکلی و روابط فضایی توسط الگوریتم فاصله کسینوسی انجام شده است. دسته بندی بر اساس ویژگی های استخراج شده توسط الگوریتم سلسله مراتبی به روش پیوند میانگین انجام شده است. بر اساس نتایج شباهت سنجی نمونه های موردی منتخب پلان خانه های یونسی، گلبت و کرچی، با بالاترین میزان شباهت شکلی و روابط فضایی با دیگر پلان ها می باشند. نتایج حاصل از دسته بندی نشان می دهد پلان ها از نظر ویژگی های استخراج شده به سه دسته قابل تقسیم می باشند. با تحلیل نمودار پراکندگی ویژگی های هر دسته، نتایج زیر استخراج شده است. در دسته اول اتاق بادگیر در شرق خانه قرار دارد و ساباط و یا حیاط در سمت غرب آن قرار گرفته است. اتاق های اصلی غالباً در ضلع شمالی و فضاهای خدماتی در ضلع شرقی و غربی قرار دارند. در دسته دوم اتاق بادگیر در میانه ضلع غربی خانه قرار گرفته است. در این دسته کشیدگی پلان شمالی- جنوبی است و اتاق های زیستی در سمت غربی و شرقی قرار گرفته اند. در دسته سوم بادگیر در ضلع غربی پلان قرار دارد. در این دسته کشیدگی پلان ها بیشتر شرقی- غربی می باشد. مجاورت شمالی اتاق بادگیر گتیه و یا اتاق می باشد و در اکثر پلان های این دسته اتاق بادگیر به اتاق شمالی و یا گتیه راه دارد.Classification and extraction of architectural plan features using machine learning methods; Case study: Traditional houses of Bandar Kong
Extended AbstractBackground and Objectives: 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.Methods: 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.Findings: 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.Conclusion: 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.
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