دانلود مقاله تشخیص ناهنجاری دمایی ساختار خرپایی با روش زنجیره مارکوف-مونته کارلو
خلاصه
1. مقدمه
2 مفهوم و رویکرد
3 تست تشخیص آسیب
4 تشخیص تغییر حالت ساختاری در فرآیند ساخت سایت
5 خلاصه و نتیجه گیری
منابع
Abstract
1 Introduction
2 Concept and approach
3 Damage diagnosis test
4 Structural state change diagnosis in site construction process
5 Summary and conclusions
References
چکیده
در نظر گرفتن عوامل محیطی مانند دما در پیشرفت پایش سلامت سازه مورد توافق بوده است. با این حال، عدم قطعیت داده های نظارت معمولاً آن را دشوار می کند. در این مقاله، عامل عدم قطعیت به فرآیند تشخیص ناهنجاری معرفی شده است، یک روش تشخیص ناهنجاری زنجیره مارکوف-مونتا کارلو (MCMC) بر اساس پاسخ ناشی از دما پیشنهاد شده است. ابتدا، یک شاخص تشخیص جدید بر اساس داده‌های دما و داده‌های پاسخ کرنش استاتیک جمع‌آوری‌شده توسط سیستم SHM ایجاد شده است، فرآیند MCMC برای تجزیه و تحلیل شاخص تشخیص استفاده می‌شود و هیستوگرام توزیع فرکانس خلفی شاخص تشخیص واقعی به‌دست می‌آید. در نهایت با تجزیه و تحلیل هیستوگرام یک حالت مجهول و حالت اولیه (حالت پایه) سازه، احتمال ناهنجاری وضعیت مجهول به دست می آید که می توان از آن برای تشخیص احتمال ناهنجاری اجزاء استفاده کرد. در دسترس بودن روش با آزمایش ساختار خرپایی آزمایشگاهی تحت یک سری شرایط کاری ارزیابی می‌شود و با داده‌های نظارت میدانی ساختار سقف آویز تأیید می‌شود. نتایج نشان می‌دهد که روش می‌تواند از اثر دمایی سازه برای تشخیص ناهنجاری بهتر استفاده کند و عدم قطعیت به خوبی در نظر گرفته شده است.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
Considering environmental factors such as temperature in structural health monitoring progress has been a consensus. However, the uncertainty of monitoring data usually makes it difficult. In this paper, the uncertainty factor has been introduced into the anomaly diagnosis process, a Markov chain-Monta Carlo (MCMC) anomaly diagnosis method based on temperature-induced response has been proposed. First, a novel diagnosis index has been developed based on the temperature data and static strain response data collected by the SHM system, the MCMC process is used to analyze the diagnosis index, and the posterior frequency distribution histogram of the actual diagnosis index is obtained. Finally, by analyzing the histogram of an unknown state and the initial state (baseline state) of the structure, the anomaly probability of the unknown condition is obtained, which can be used for anomaly probability diagnosis of components. The availability of the method is evaluated by a laboratory truss structure test under a series of working conditions and is verified by field monitoring data of a hanger roof structure. The results show that the method can make better use of the temperature effect of the structure for anomaly diagnosis, and the uncertainty is well considered.
Introduction
Large building structures, including truss structures, are often used in airports, stations, factories, stadiums, and other important civil infrastructures [1], such structures often encounter sudden load changes (snow loads, high wind loads), changes in restraint conditions, member damage due to material degradation, and other structural anomalies during construction or service. Monitoring and diagnosing these structural anomalies using sensors placed on the surface of the structure is an efective means to ensure the safety of the structure throughout its life, and in recent years, with the development of sensor technology and intelligent algorithms, structural anomaly diagnosis (SAD) technique is becoming an increasingly important area.
The vibration-based method is one of the most widely used SAD methods, which refects the abnormal state of the structure by monitoring the changes of vibration features [2–4]. However, the structural vibration features (e.g., natural frequencies) are not only related to the state of the structure itself, but also to environmental factors such as temperature, which will have an impact on the accuracy of SAD [5–7]. Although diferent solutions have been proposed for vibration-based SAD under environmental changes, these methods are still greatly limited by other defects in long-term practical monitoring, such as low sensitivity of vibration features to small local damage of the structure, the complicated method of sensor arrangement, and the large data transmission and storage caused by the high sampling frequency of the monitoring process, etc. [8].
Summary and conclusions
In this paper, a temperature-based anomaly diagnosis of truss structure system using Markov chain-Monte Carlo method is proposed, which not only introduces the uncertainty method into a SAD method based on static response monitoring, but also actively uses the temperature efect of the structure for SAD. In this method, a novel diagnosis index based on T-stress-induced strain that takes uncertainties into account is proposed, which is obtained from the stress-induced strain and temperature measured by sensors arranged in the focus area of the structure. Then, the diagnosis index measurement data set is processed by MCMC to obtain the posterior relative frequency distribution histogram of the actual diagnosis index, and the histogram of the baseline state and an unknown state are analyzed to obtain the structural anomaly probability of the members under this unknown state. As two aspects of anomaly diagnosis, the structural damage diagnosis and state change diagnosis were verifed by a truss test and feld monitoring data of the BDIA hanger roof structure respectively, and the following conclusions were obtained: