{"id":148601,"date":"2017-08-09T17:35:00","date_gmt":"2017-08-09T16:35:00","guid":{"rendered":"https:\/\/www.um.edu.mo\/sem-categoria\/2017\/08\/42499-pt_pt\/"},"modified":"2020-11-26T20:29:11","modified_gmt":"2020-11-26T12:29:11","slug":"42499","status":"publish","type":"post","link":"https:\/\/www.um.edu.mo\/pt-pt\/news-and-press-releases\/campus-news\/detail\/42499\/","title":{"rendered":"What Is Structural Health Monitoring?"},"content":{"rendered":"<p>Source: <a style=\"font-style: italic;\" href=\"http:\/\/www.umac.mo\/umagazine\/issue16\/pdf\/umagazine16.pdf\">umagazine<\/a><\/p><p>Due to rapid economic and technological development, the scale of our\ninfrastructure has increased dramatically. For example, skyscrapers can\nbe almost one kilometre tall. Furthermore, newly-invented materials and\nstructural member types complicate structural behaviour. Traditional types of\nhuman investigations are costly and they have various limitations. In\nparticular, non-destructive testing methods for the determination of concrete\ncompressive strength are popular, but they can be used to determine the concrete\nstrength of a localised area only. For large-scale structures, it is difficult,\nif not prohibitive, to test all positions of the structure. More recently,\nother advanced technology allows the acquisition of the very tiny vibration\nresponse of structures. Such vibration is of the order of 10-5g or 10-6g, that\nis one of a million of the gravity acceleration. Nevertheless, the problem is\nvery challenging because such response is very random.<b><\/b><\/p>\n\n<p><b><br\/>\nStructural Health Monitoring<\/b><b><br\/>\n<br\/>\n<\/b>Structural health monitoring (SHM) is an automated strategy to\ndiagnose if a structure is in its healthy state, and it has attracted\ntremendous interest in the last few decades. In particular, there have been\nimportant advancements in the past decade due to rapid development in sensor technology\nand data acquisition systems. There are four major goals in SHM: (1) to\nindicate if there is any damage to a structure; (2) to determine the\nlocation(s) of the damage(s); (3) to estimate the severity of the damage(s);\nand (4) to evaluate the reliability of the entire structure due to possible\ndamages.<br\/>\n<br\/>\n\u00adThere are two major approaches\nof SHM: static and dynamic. For static approaches, usually the strains of\ndifferent locations of a structure (especially bridge) will be measured and\nthey will be judged on whether or not they are unacceptably large compared with\nthe normal thermal expansion. However, a major problem of this approach is that\nstrains depend directly on the force or excitation exhibited to the structure.\nTherefore, the values of strains may reflect only the change of loading\nconditions instead of the health status of the structure. Furthermore, the\nstrain measurements are also localised quantities.<\/p>\n\n<p><br\/>\n<b>Dynamic Monitoring<br\/>\n<br\/>\n<\/b>For the dynamic approach, the idea is similar to pulse checking but substantially\nmore difficult. Instead of evaluating if the vibration magnitude of the\nstructure is too large, this approach attempts to investigate the\ntime-frequency content of the response. For instance, if one clicks a cup, a\nspecific c sound frequency will be generated and this frequency is insensitive\nto how hard the cup was hit. However, if the cup is cracked, the sound\nfrequency will change. In particular, the location, and the length and depth of\nthe crack, will result in different change of such frequency. Nevertheless, the\nproblem in structural health monitoring is far more complicated because the\ndynamic loadings (ground motion, wind loads, sea wave, traffic induced loads,\nand so on) are random and usually cannot be measured. Furthermore, the scale of\ncivil engineering structures is huge, especially for those necessary for the\nstructural health monitoring scheme.<br\/>\n<br\/>\nIn the dynamic structural health monitoring system, sensors (usually\naccelerometers) are mounted at different locations on the underlying structure.\nSince the important frequency band of typical structures is from 0.1 Hz to 20\nHz, the sampling frequency will have to be 100 Hz or above in order to have\nsufficient description of the response, ie to measure 100 times or more within\none second at each location.<br\/>\n<br\/>\nAnother difficulty lies with the large number of unknown parameters to be\nidentified, since there are numerous components in civil engineering structures\n(such as, say, a bridge). We know that we need to have at least the same number\nof equations to solve the unknowns. In our problem of structural health\nmonitoring, such equations are obtained from sensors located on the structures.\nIn this case, we may need a huge number of sensors but this induces both cost\nand computational burdens.<\/p>\n\n<p><br\/>\n<b>Real-time Structural Identification<br\/>\n<br\/>\n<\/b>On 23 October 2015, a ship struck the Kap Shui Mun Bridge in Hong Kong. \u00ad The\nbridge was closed for about 1.5 hours. Since it is a critical line to the Chap\nLap Kok Airport, the economic loss of this incident was huge. Therefore, if a\nstructural identification can work in a real-time fashion, it can help identify\nwhether or not the structure is safe almost immediately, instead of closing the\nbridge and conducting an investigation by engineers, or by other offline\nstructural identification calculation. \u00ad is will be very useful from the point\nof both safety and economic concerns. However, real-time structural identification\nis a very challenging problem. First, the scale of civil engineering structures\nis huge so one can expect a very complicated finite element model is necessary.\nSecond, since the sampling frequency is usually hundreds Hz, it is straightforward\nto imagine the difficulty in updating a huge structural model several hundred\ntimes every second.<\/p>\n\n<p><br\/>\n<b>Model Class Selection<br\/>\n<br\/>\n<\/b>One important problem is to select a proper class of models for the purpose\nof structural health monitoring.\u00ad This sounds to be a simple problem but it is\nindeed much more difficult than it looks at first glance. For instance, let\u2019s\nconsider a simple problem for the sake of explanation. Imagine a student,\ncalled student A, who takes a physics class and learns F=ma. In the laboratory\nsection, he conducts experiments and obtains 10 data points. Then, he uses\nEXCEL to fit a line to obtain the constant \u2018m\u2019 to determine whether or not it\nmatches with the mass. Unfortunately, student B does not attend the lecture,\nand therefore when he conducts the experiments he does not know the formula\nF=ma. He then uses EXCEL to fit a parabola, ie, second order polynomial. It turns\nout that his data fitting is better than student A because a parabola offers\nmore flexibility to t the data better. Then, student C does the same but with a\nninth-order polynomial. Guess what? She got zero error because this polynomial,\nwith ten adjustable coefficients, can go through all ten points exactly.\nHowever, a ninth-order polynomial is very bumpy and it is by no means a good\nmodel for prediction in this case. What can we learn from this story? We cannot\nselect a class of models solely due to the fitting errors. In general, a class\nof models with too many adjustable parameters has great power in fitting the\ndetails of the data, including the noise, but this will lead to the so-called\nover-fitting behaviour. Such models are not reliable for future prediction.<br\/>\n<br\/>\nBeck and Yuen (2004) presented the first paper to tackle the model class\nselection problem in the area of structural health monitoring. This is problem is\nparticularly difficult in this field due to the large number of unknowns, the\nunavailability of the excitation measurements, and a high level of uncertainty\nin civil engineering structures. In this paper, model complexity was quantified\nand the model class candidates are ranked according to the tradeoff between the\nmodel fitting power and its complexity, which is a measure of the model\nrobustness. Model robustness is a very crucial indicator of the performance of\na model class because it represents how fragile a model class is in resisting\nmodelling errors and other uncertainties. This paper received tremendous\nattention, even from researchers in other areas such as chemistry, physics,\nelectrical engineering, education measurements, medical science, and materials\nscience. It was ultimately listed as one of the top ten cited papers among\nseveral thousand papers published in the long history of the Journal of\nEngineering Mechanics of the American Society of Civil Engineers.<br\/>\n<br\/>\nRecently, we extended this work to consider a more challenging problem to\nselect the model class in the real-time manner (Yuen and Mu, 2015). The problem\nis difficult because the data acquisition time interval is usually 1\/200 or\n1\/500 second. In other words, we have to finish model class selection and\nparametric identification 200 or 500 times per second.\u00ad This paper proposed an\nimportant foundation to build reliable real-time structural health monitoring\nsystems. Due to its importance, it was published in the Computer-aided Civil\nand Infrastructure Engineering journal, which is ranked the top among 126\nSCI\/SCIE indexed civil engineering journals. More importantly, it was the\nsecond most cited paper among the over 120 papers published in the same period.<\/p>\n\n<p><br\/>\n<b>Monitoring of the East Asia Hall on the\nOld Campus<br\/>\n<br\/>\n<\/b>On the old UM campus, we monitored the East Asia Hall round the clock for\nabout five years. We investigated how environmental conditions (temperature and\nhumidity) affect the long-term structural behaviour (Yuen and Kuok, 2010).\nFurthermore, we also studied the extreme behaviour under a number of severe\ntyphoons. We participated in the National Challenge Cup in 2009 and won the\nfirst-class award, which was the first time this award was won by scholars in\nMacao. We were also invited to conduct an investigation of the Ting Kau Bridge\n(Kuok and Yuen, 2016).<\/p><p><\/p>","protected":false},"excerpt":{"rendered":"<p>Source: umagazineDue to rapid economic and technological development, the scale of our infrastructure has increased dramatically. For example, skyscrapers can be almost one kilometre tall. Furthermore, newly-invented materials and structural&#8230;<\/p>\n","protected":false},"author":1,"featured_media":128408,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[61],"tags":[],"class_list":["post-148601","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-campus-news"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v24.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>What Is Structural Health Monitoring? | Universidade de Macau<\/title>\n<meta name=\"description\" content=\"Universidade de Macau: Uma universidade abrangente p\u00fablica de n\u00edvel internacional fundada em 1981\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.um.edu.mo\/pt-pt\/news-and-press-releases\/campus-news\/detail\/42499\/\" \/>\n<meta property=\"og:locale\" content=\"pt_PT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"What Is Structural Health Monitoring? 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