پديد آورندگان :
گرانمايه، ارسلان دانشگاه خوارزمي - مهندس عمران، ايران , همامي، پيمان دانشگاه خوارزمي - مهندس عمران، ايران , حسيني لواساني، حسين دانشگاه خوارزمي - مهندس عمران، ايران
كليدواژه :
پايش‌سلامت‌سازه‌اي , پل معلق , تشخيص خرابي , يادگيري ماشين پشتيبان , ماشين بردار , شبكه‌ي عصبي مصنوعي , فتوگرامتري برد كوتاه هوشمند
چكيده فارسي :
در دهه‌هاي اخير علم پايش‌سلامت‌سازه نقش اساسي در پيشگيري از خرابي و افزايش طول عمر سازه‌ها ايفا كرده است. استفاده از ابزار‌هايي براي انجام رفتارسنجي مطلوبست كه دقت كافي را همراه با هزينه‌ي كم تحقق بخشند. براي پردازش دادههاي بدست آمده از رفتارسنجي به روش‌هايي نياز است كه قادر باشند سطوح مختلف آسيب را از اطلاعات موجود شناسايي و به‌درستي عيبيابي كنند.
رفتارسنجي اپتيكي و عمليات فتوگرامتري بردكوتاه بدليل هزينه كم و دقت مناسب، اخيراً مورد توجه قرار گرفته اند در اين مقاله تلاش شده است تا كاربرد روش مذكور در تركيب با روش تحليل استقرايي (با ابزارهاي مقايسه و يادگيري ماشين) براي رفتارسنجي و عيبيابي ماكت آزمايشگاهي سازهي يك پل معلق كه داراي رفتار نسبتاً پيچيدهاي است مورد ارزيابي قرار گيرد. به اين منظور، سازهي پل مورد نظر تحت سه تراز بارگذاري استاتيكي در سه حالت سالم و آسيب ديده در عرشه و كابلها مورد رفتارسنجي قرار گرفت. آسيبها كاملاً آگاهانه در مدل آزمايشگاهي ايجاد شدند و از اطلاعات حاصل، پايگاه‌داده‌اي از رفتار پل در حالات گوناگون ايجاد شد. به‌منظور امكان سنجي استفاده از روشهاي مختلف در پردازش دادهها و عيبيابي، ابتدا دادههاي موجود در پايگاه، در روش خطي ساده (مقايسه مستقيم) و آموزش در الگوريتمهاي روشهاي يادگيري‌ماشين، مورد استفاده قرار گرفتند. پس از آن، مجدداً آسيبهاي آگاهانهاي در سازهي آزمايشگاهي ايجاد شد تا امكان آزمون كارآيي و دقت روشهاي مختلف فراهم شود. در انتها، دقت، صحت و پايداري روشهاي پردازش داده ماشين بردارپشتيبان و شبكه‌عصبي‌مصنوعي با يكديگر مقايسه شدند.
نتايج نشان داد كه با توجيه به باندل اجسمنت رفتارسنجي دو بعدي اپتيكي فتوگرامتري بردكوتاه، ميتوان به دقت تضمين شده‌ي mm0021/0 دست يافت. در سطح نخست پردازش داده ها يعني تشخيص وجود آسيب يا عدم وجود آن موفقيت شبكه‌هاي عصبي بطور كامل و با دقت 100% همراه بود و در سطح دوم يعني تشخيص منطقه‌ي آسيب ديده، شبكه‌عصبي با تابع انتقال تانژانت‌هايپربوليك 93% موفقيت داشت و ماشين بردارپشتيبان با موفقيت 68% همراه بود.
چكيده لاتين :
In recent decades, the science of structural health monitoring has played a key role in preventing damage and extending the life of structures. To conduct behavioral assessment, it is desirable to use tools that achieve sufficient accuracy with low cost. The processing of behavioral data requires methods that are able to identify and correctly troubleshoot different levels of damage from existing information.
Nowadays, sensors are used to measure the behavior of structures including deformations and displacements and even deflections, but these sensors have some weak points. For example, Risk of damage to the sensor, pointwise and one-dimensional measuring, their data is difficult to analyze and using multiple or high-tech sensors becomes expensive.
Optical behavior measurement and close-range photogrammetric operations have recently received attention due to their low cost and good accuracy. This method has some advantages like Indirect contact with objects, high-speed image capture, easy access to convenient digital cameras, low viewing costs, and the ability to process composite and instant data with easy operation. In addition, the high flexibility of this method in measuring accuracy and design capability to achieve predetermined accuracy is an important feature of this tool.
Analytical methods are based on rules or equations that provide a clear definition of the problem. These methods work well in the cases which the rules are accurately clear and defined but there are many practical cases for which the rules are not known or it is very difficult to discover that calculations cannot be performed using analytical methods.
Neural network is a generalizable model, which is based on the experience of a set of training data and therefore free of explicit law. Neural networks have the ability to collect, store, analyze, and process large amounts of data from numerical analyzes or experiments. Therefore, they have the ability to predict and build diagnostic models to solve various engineering problems and tasks
In this paper, an attempt has been made to use this method to measure and troubleshoot laboratory model of a scaled suspension bridge that has a relatively complex behavior. For this purpose, the structure was subjected to uniform static loading in three step levels with three states: healthy and damaged in the deck and cables. Damages were created quite intentionally in the laboratory model, and from the information obtained, a database of bridge behavior in various situations was created. In order to assess the feasibility of using different methods in data processing and troubleshooting, first the data in the database were used in a simple linear method (direct comparison) and training in algorithms of machine learning methods. After that, deliberate damage was done again in the laboratory structure to allow testing the efficiency and accuracy of different methods. Finally, the accuracy, precision, and stability of the data processing methods of the support vector machine and artificial neural network were compared.
The results showed that by object bundle justification of two-dimensional optical behaver measurement with close-range photogrammetry, a guaranteed accuracy of 0.0021 mm could be achieved. Using intensity image processing seems helpful to ease the calculation. Using high number of nodes in hidden layer makes it more difficult and time-consuming to train the neural network. In the first level of processing, the detection of the presence or absence of damage was associated with the complete superiority of neural networks with 100% accuracy and in the second level, the detection of the affected area, depending on the type of processing, the neural network with hyperbolic tangent transfer function archived 93% accuracy and the support vector machine archived 68% of the accuracy.
