technology developments in structural health monitoring of large-scale bridges

Engineering Structures27(2005)

1715–1725

https://www.sodocs.net/doc/028990254.html,/locate/engstruct

Technology developments in structural health monitoring of

large-scale bridges

J.M.Ko?,Y.Q.Ni

Department of Civil and Structural Engineering,The Hong Kong Polytechnic University,Hung Hom,Kowloon,Hong Kong

Available online14July2005

Abstract

The signi?cance of implementing long-term structural health monitoring systems for large-scale bridges,in order to secure structural and operational safety and issue early warnings on damage or deterioration prior to costly repair or even catastrophic collapse,has been recognized by bridge administrative authorities.Developing a long-term monitoring system for a large-scale bridge—one that is really able to provide information for evaluating structural integrity,durability and reliability throughout the bridge life cycle and ensuring optimal maintenance planning and safe bridge operation—poses technological challenges at different levels,from the selection of proper sensors to the design of a structural health evaluation system.This paper explores recent technology developments in the?eld of structural health monitoring and their application to large-scale bridge projects.The need for technological fusion from different disciplines,and for a structural health evaluation paradigm that is really able to help prioritize bridge rehabilitation,maintenance and emergency repair,is highlighted.

?2005Elsevier Ltd.All rights reserved.

Keywords:Large-scale bridge;Structural health monitoring(SHM);Instrumentation system;Damage detection;Bridge maintenance

1.Introduction

The development of structural health monitoring tech-nology for surveillance,evaluation and assessment of exist-ing or newly built bridges has now attained some degree of maturity.On-structure long-term monitoring systems have been implemented on bridges in Europe[1–4],the United States[5,6],Canada[7,8],Japan[9,10],Korea[11,12], China[13–15]and other countries[16–18].Bridge struc-tural health monitoring systems are generally envisaged to: (i)validate design assumptions and parameters with the po-tential bene?t of improving design speci?cations and guide-lines for future similar structures;(ii)detect anomalies in loading and response,and possible damage/deterioration at an early stage to ensure structural and operational safety; (iii)provide real-time information for safety assessment immediately after disasters and extreme events;(iv)provide evidence and instruction for planning and prioritizing bridge ?Corresponding author.Tel.:+852********;fax:+852********.

E-mail address:cejmko@https://www.sodocs.net/doc/028990254.html,.hk(J.M.Ko).

0141-0296/$-see front matter?2005Elsevier Ltd.All rights reserved. doi:10.1016/j.engstruct.2005.02.021inspection,rehabilitation,maintenance and repair;(v)mon-itor repairs and reconstruction with the view of evaluating the effectiveness of maintenance,retro?t and repair works; and(vi)obtain massive amounts of in situ data for leading-edge research in bridge engineering,such as wind-and earthquake-resistant designs,new structural types and smart material applications.

The development and implementation of a structural health monitoring system capable of fully achieving the above objectives and bene?ts is still a challenge at present,and needs well coordinated interdisciplinary research for full adaptation of innovative technologies developed in other disciplines to applications in the civil engineering community.Actually,structural health monitoring has been a subject of major international research in recent years[19–21].The research in this subject covers sensing,communication,signal processing,data management,system identi?cation,information technology, etc.It requires collaboration between civil,mechanical, electrical and computer engineering among others.The current challenges for bridge structural health monitoring

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are being identi?ed as distributed and embedded sensing, data management and storage,data mining and knowledge discovery,diagnostic methods,and presentation of useful and reliable information to bridge owners/managers for decision making on maintenance and management.In this article,after an overview of current status of large-scale bridge health monitoring practice,the authors explore some key issues concerning the above challenges,in a perspective of both researchers and practicers,by referring to several health monitoring engineering paradigms.

2.State-of-the-practice in bridge monitoring systems

Successful implementation and operation of long-term structural health monitoring systems on bridges have been widely reported.So far about40long-span bridges(with spans of100m or longer)worldwide have been instrumented with structural health monitoring systems[22].Typical examples are the Great Belt Bridge in Demark[1],the Confederation Bridge in Canada[23],the Tsing Ma Bridge in Hong Kong[24],the Commodore Barry Bridge in United States[25],the Akashi Kaikyo Bridge in Japan[26], and the Seohae Bridge in Korea[27].Table1lists 20large-scale bridges in China(including the Hong Kong Special Administrative Region)instrumented with real-time monitoring systems.This listing does not comprise the East Sea Bridge(consisting of two cable-stayed bridges with main spans of420m and332m respectively),the Hangzhou Bay Bridge(consisting of two cable-stayed bridges with main spans of448m and318m respectively)and the3rd Nanjing Yangtze River Bridge(a cable-stayed bridge with a main span of648m)of which the long-term structural health monitoring systems are currently under design.

Several recent trends in structural health monitoring practice for large-scale bridges are worth mentioning.(i)For some recent bridges such as the Shenzhen Western Corridor, the Stonecutters Bridge,the Shanghai Chongming Crossing (a cable-stayed bridge with a main span of1200m) and the Messina Strait Bridge(a suspension bridge with a main span of3300m),the design of a monitoring system is required in the tender as part of the bridge design.Integration of bridge design and monitoring system design ensures that design engineers’important concerns are re?ected in the monitoring system while civil provisions for implementing a monitoring system are considered in the bridge design.(ii)The implementation of long-term monitoring systems on new bridges such as the 4th Qianjiang Bridge,the Shenzhen Western Corridor,the Stonecutters Bridge and the Sutong Bridge is accomplished in synchronism with the bridge construction progress.In this way some speci?c types of sensors,e.g.,corrosion sensors, strain gauges and?ber optic sensors can be embedded into the structure during certain bridge erection stages. (iii)The recently devised long-term health monitoring systems emphasize multi-purpose monitoring of the bridge integrity,durability and reliability.In Hong Kong,an improvement from the Tsing Ma Bridge monitoring system to the Stonecutters Bridge monitoring system includes more environment-measuring sensors such as corrosion sensors, barometers,hygrometers and pluviometers to facilitate bridge safety/reliability assessment[40].Another example is the Sutong Bridge monitoring system that will incorporate a majority of the embedded sensors currently belonging to a foundation stability and safety monitoring system (designed for construction monitoring only)to enhance the superstructure long-term monitoring system for bridge durability assessment.

3.Sensing and data acquisition systems

In the past decade signi?cant progress has been made in sensing technology and kinds of innovative sensing systems such as?ber optic sensors and wireless sensors are now becoming commercially available[41–44].A sensing system is essential to realizing structural health monitoring of bridges.The envisioned future for bridge health monitoring uses an array of inexpensive,spatially distributed,wirelessly powered,wirelessly networked, embedded sensing devices supporting frequent and on-demand acquisition of real-time information about the loading and environmental effects,structural characteristics and responses.Fiber optic sensors have successfully been applied for long-term structural health monitoring of large-scale bridges(e.g.,[45,46]among others),whereas the application of wireless sensors for bridge monitoring is still in the technology demonstration stage[43].

It is worth mentioning that some conventional sensors become infeasible and impracticable when applied to large-scale bridges for long-term monitoring.For example,it is a dif?cult task to measure the de?ection(absolute displace-ment)of long-span bridges.The traditional displacement transducers can only be used for relative displacement mea-surement,while laser transducers and total stations have been proven unsuitable for long-term monitoring of long-span bridges.The current solution to this problem is to use a global positioning system(GPS).However,the application of a GPS for bridge monitoring has two limitations:(i)the measurement accuracy of a GPS is not good enough to com-pletely meet bridge health monitoring requirement;and(ii)a GPS does not work well for monitoring the displacement of piers beneath the bridge deck(caused by ships collid-ing,settlement,etc.).Strain measurement is another issue which is essential for bridge health assessment.There are two types of commonly used strain sensor:electrical resis-tance strain gauges and vibrating wire strain gauges.Both of them have defects:electrical resistance strain gauges are capable of measuring dynamic strain but possess low zero-stability which results in drift of the measurand over time; vibrating wire strain gauges have high zero-stability but can only be used for quasi-static strain measurement.These de?-ciencies of traditional strain gauges have invoked increasing applications of innovative?ber optic sensors for long-term

J.M.Ko,Y.Q.Ni/Engineering Structures27(2005)1715–17251717 Table1

Major bridges in China instrumented with long-term monitoring systems

No.Bridge name Bridge type Location Main span(m)Sensors installed

1Jiangyin Bridge(after upgrade)[28]suspension Jiangsu1385(1),(2),(3),(4),(5),(6),(9),(10),(13) 21st Nanjing Yangtze River Bridge[29]steel truss Jiangsu160(1),(2),(3),(4),(5),(7),(14)

32nd Nanjing Yangtze River Bridge[30]cable-stayed Jiangsu628(1),(2),(3),(4),(7),(9),(13),(16) 4Runyang South Bridge[31]suspension Jiangsu1490(1),(2),(3),(4),(6)

5Runyang North Bridge[31]cable-stayed Jiangsu406(1),(2),(3),(4)

6Sutong Bridge[32]cable-stayed Jiangsu1088(1),(2),(3),(4),(5),(6),(7),(8),(9),

(10),(11),(16),(18)

7Tsing Ma Bridge[15]suspension Hong Kong1377(1),(2),(3),(4),(5),(6),(7),(12),(18) 8Kap Shui Mun Bridge[15]cable-stayed Hong Kong430(1),(2),(3),(4),(5),(6),(7),(12),(18) 9Ting Kau Bridge[15]cable-stayed Hong Kong475(1),(2),(3),(4),(5),(6),(7),(12),(18) 10Shenzhen Western Corridor[15]cable-stayed Hong Kong210(1),(2),(3),(4),(5),(7),(8),(15),

(16),(17),(18)

11Stonecutters Bridge[15]cable-stayed Hong Kong1018(1),(2),(3),(4),(5),(6),(7),(8),(9),

(10),(11),(15),(16),(17),(18)

12Tongling Yangtze River Bridge[33]cable-stayed Anhui432(1),(2),(4),(11),(13)

13Wuhu Bridge[34]cable-stayed Anhui312(2),(3),(4),(5),(10),(12)

14Humen Bridge[35]suspension Guangdong888(3),(6),(11),(12)

15Zhanjiang Bay Bridge[6]cable-stayed Guangdong480(1),(2),(3),(5),(6),(9),(11),(14),(16) 16Xupu Bridge[36]cable-stayed Shanghai590(2),(3),(4),(7),(12)

17Lupu Bridge[37]arch Shanghai550(2),(3),(4),(12)

18Dafosi Bridge[38]cable-stayed Chongqing450(2),(3),(4),(5),(10),(12)

19Binzhou Yellow River Bridge[14]cable-stayed Shandong300(1),(2),(3),(4),(6),(10)

204th Qianjiang Bridge[39]arch Zhejiang580(1),(2),(3),(4),(9),(13)

Note:(1)—anemometers;(2)—temperature sensors;(3)—strain gauges;(4)—accelerometers;(5)—displacement transducers;(6)—global positioning systems;(7)—weigh-in-motion systems;(8)—corrosion sensors;(9)—elasto-magnetic sensors;(10)—optic?ber sensors;(11)—tiltmeters;(12)—level sensors;(13)—total stations;(14)—seismometers;(15)—barometers;(16)—hygrometers;(17)—pluviometers;(18)—video cameras.

monitoring of large-scale bridges.Fig.1illustrates such an application where?ber optic sensors are deployed along the deck length of the suspension Jiangyin Bridge for both strain and temperature measurement.The most attractive feature of ?ber optic sensors is their capability of distributed sensing and measurement which will result in elaborate condition monitoring for large-scale bridges.The existent main obsta-cle to wide acceptance of?ber optic sensors for bridge mon-itoring application is the lack of engineering demonstration of the durability of the sensors in a harsh environment and long-term performance of their attachment to construction materials.

Another promising application of?ber optic sensors for cable-supported bridges is the embedment of sensors in-side the bridge cables for both temperature and strain mea-surement.An interdisciplinary research team in Hong Kong Polytechnic University has devised such a?ber optic sens-ing system for the cable-stayed Sutong Bridge.In this design shown in Fig.2,seven out of the wires compos-ing the cable cross-section have been replaced by stainless steel tubes for the deployment of?ber optic sensors.Optic ?bers in terms of the Brillouin scattering sensors are laid ‘strain-free’inside each steel tube for distributed tempera-ture measurement along the cable length.The technology of Brillouin-optical time-domain re?ectometry(B-OTDR)is used,by which a laser pulse is launched into the optic?ber that serves as the sensing element and the temperature mea-surement is achieved by combining the scattering informa-tion with propagation time of the laser pulses along the?ber. It is noted that seven galvanized wires have been added at the outermost of the cable cross-section to keep the total area of the wires unaltered.Meanwhile,?ber Bragg grating(FBG) sensors are embedded in the cable ends for strain measure-ment.The strain of the cable near its anchorages is mea-sured with FBG arrays epoxied onto the outside surface of the steel tubes,as shown in Fig.2.The FBG arrays consist of three FBG strain sensors spaced2m apart.The FBGs are sensitive to both strain and temperature.The tempera-ture of the FBGs is obtained from the B-OTDR system,and therefore the strain applied to the FBGs can be determined. Because the FBG arrays are installed along the steel tubes which are used to accommodate B-OTDR for temperature measurement,extra steel tubes and wire area reduction are eschewed.

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https://www.sodocs.net/doc/028990254.html,yout of?ber optic sensors on the Jiangyin Bridge.

Fig.2.Fiber optic sensors embedded inside a bridge cable cross-section.

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https://www.sodocs.net/doc/028990254.html,yout of sensors and DAUs on the Sutong Bridge.

On-structure data acquisition units (DAUs)or outstations are indispensable to structural health monitoring systems for long-span bridges.DAUs are assigned at several locations of the bridge to collect the signals from neighboring sensors,digitize the analog signals and transmit the data into a central room outside the bridge.They also have the function of short-term data storage and preliminary signal processing.For large-scale bridges with densely distributed sensors,optimal deployment of DAUs plays a signi?cant role in assuring the quality and ?delity of the acquired data.The number of DAUs required relies on the number of sensor channels in high sampling rate and in low sampling rate.The placement of the DAUs is primarily dependent on the location of analog-type sensors,especially those with low-sensitivity voltage-signal output.The operating environment and allowable maximum distance between the sensors and DAUs have to be considered to eliminate transmission loss/noise and to protect the DAUs from interference.

In implementing the structural health monitoring system for the Sutong Bridge,a distributed data acquisition system based on the PXI/SCXI and MXI-3techniques has been devised to overcome the transmission cabling length limitation while minimizing the number of DAUs.The system is comprised of seven DAUs connected by a FDDI dual-loop ?ber optic network to the central room.Each DAU is comprised of a main station (MS)and optional sub-stations (SSs)as illustrated in Fig.3.The main station consists of a PXI instrument platform,a signal conditioning system and data acquisition modules,while the sub-station (SS)has no PXI instrument platform.Each sub-station is connected to its main station with an MXI-3interface kit and is remotely controlled by the main station.The PXI instrument platform with a PXI controller is responsible for the operation of the DAU,signal preprocessing and communication with the central room.In the Sutong Bridge,two sub-stations (SS2-1and SS6-1;refer to Fig.3),respectively connected to the main stations MS2and MS6,are placed inside the two towers (one inside each tower)near the base to collect data from sensors placed below the pylon base level,including those currently belonging to the foundation stability and safety monitoring system (more than 1400sensors are involved in this system).Making use of the MXI-3technique,the transmission cabling length limitation can be released without employing additional DAUs.In this design,each DAU is able to support at most eight sub-stations with the aid of MXI-3extension slots.Lessons learned from the practice on existing bridge monitoring systems tell us that utmost care must be taken with the protection of DAUs.At least two health monitoring systems for the instrumented bridges listed in Table 1were found with malfunction in all or some DAUs after operation for a few years,due to improper protection.The on-structure DAUs must be designed against a variety of environmental conditions such as temperature,humidity,lightning and electromagnetic interference.The Sutong Bridge project provides an excellent example to demonstrate this issue.Due to limited space of carriageway,?ve DAUs in the Sutong Bridge have to be placed inside the box girders.Moreover,in order to prevent the steel girders from corrosion,the structural design requires that the humidity inside the sealed box is kept constant by a dehumidi?cation system,and the inner air is prohibited to circulate with the outer air.We have to meet this requirement in designing the protective system for DAUs.A stainless steel cabinet as illustrated in Fig.4is designed to house each DAU and to protect it from dust,temperature,humidity,and electromagnetic interference.An air conditioning system is settled inside the cabinet to accommodate severe temperature conditions in the interior of the steel box girders,which may vary from below 0?C to over 60?C.To disable the air inside the cabinet from circulating with the air in the interior of the steel box,two holes of 150mm in diameter are reserved at the lower deck ?ange in the vicinity of the main station and a refrigerant entry pipe and a refrigerant exit pipe are used to

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Fig.4.Design of a cabinet for housing and protecting DAUs.

circulate the interior air of the cabinet with the air outside the deck box directly.Also,an isolation transformer is adopted at each DAU to protect it from lightning.

4.Effect of environmental factors on measurement data A main distinction of structural health monitoring systems from conventional measurement systems is that the former incorporates damage diagnostic and prognostic algorithms.Extensive research on structural damage identi?cation algorithms has been conducted in the past decades and literature reviews on this subject are available [47–49].Most widely studied are vibration-based damage detection methods.The vibration-based damage detection methods use measured changes in dynamic features (mainly modal parameters)to evaluate changes in physical properties that may indicate structural damage or degradation.In reality,however,a civil structure is subjected to varying environmental and operational conditions such as traf?c,humidity,wind,solar-radiation and,most important,temperature.These environmental effects also cause change in modal parameters which may mask the change caused by structural damage.The evaluation results on the effectiveness of a variety of vibration-based damage detection methods applied to the I-40Bridge indicated that the environmental effects were one of the main pitfalls limiting the practical applicability of modal-based methods [5,50].For reliability performance of damage detection algorithms,it is of paramount importance to discriminate abnormal changes in dynamic features caused by structural damage from normal changes due to environmental and operational ?uctuations,so that neither will the normal changes raise a false-positive alarm nor will the abnormal changes raise a false-negative alarm in damage detection.With a thorough understanding of the effect of environmental variability on modal properties,it is possible to detect subtle structural damage by incorporating a well de?ned environmental effect model into appropriate damage detection algorithms [51–54].

Numerous investigations indicate that temperature is the critical source causing modal variability,and the variations of modal frequencies caused by temperature may reach 5%to 10%for highway bridges,which in most cases exceed the changes of frequencies due to structural damage or deterioration.Since long-term structural health monitoring systems for large-scale bridges usually include both vibration transducers and temperature sensors,quantitative understanding and modeling of the effect of temperature on modal properties can be made by using the measurement data covering a full cycle of in-service environmental conditions.With one year of measurement data from the instrumented Ting Kau Bridge [55],a comparative study of evaluating the effectiveness of various statistical regression/learning methods [56]for modeling the effect of temperature on modal frequencies is being conducted by the writers.As part of a long-term structural health monitoring system for this cable-stayed bridge,a total of 83temperature sensors and 45accelerometers (67channels)have been installed for real-time measurement of temperature and dynamic response [15,24].The modal frequencies of the bridge are obtained by applying an automatic modal identi?cation program to the measured acceleration data at one-hour intervals,and the corresponding temperatures at 20selected sensor locations are obtained by averaging over

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Fig. 5.Frequency sequences measured and generated by the linear regression model:(a)training data;(b)validation data.

one hour.A total of770hours of data covering one year’s measurement are used for studying the correlation between the modal frequencies and temperatures.

The correlation analysis has been conducted by means of linear and nonlinear regression models,neural network models and support vector machine models.Although several investigators[57,58]suggested taking into account thermal inertia by relating current output not only with current input but also with previous input,we use only static correlation models because the measurement data sequence is not always temporally continuous at constant sampling intervals.Fig.5shows the?rst modal frequency sequences obtained by measurement and by the linear regression model.The linear regression model is obtained by least-squares?tting of the training data.It is observed that this model reproduces the training data well but is poor in predicting fresh validation data.Also illustrated in the?gure is the distribution of residual error.An adequate model should generate the distribution close to Gaussion population.Fig.6shows the corresponding results obtained by a nonlinear ridge regression model in terms of the Radial-basis kernel function[59].The nonlinear regression

model Fig.6.Frequency sequences measured and generated by the nonlinear regression model:(a)training data;(b)validation data.

shows stronger generalization capability(less standard deviation of residual error and better normality of the error distribution)than the linear model,but is still short of accurately predicting the frequency variations.Figs.7and 8illustrate the reproduced and predicted results obtained by neural network and support vector machine models, respectively.These two models exhibit good capabilities in both reproduction and prediction.It is found that a perceptron neural network with a single hidden layer is suf?cient for modeling the correlation and an appropriate number of hidden nodes is crucial to achieve superior prediction performance of the trained model(excessive hidden nodes may cause serious attenuation of the prediction capability).When a support vector machine(SVM)is used for modeling the correlation,the prediction capability of the trained model is heavily dependent on the selection of the SVM coef?cients[60].If the SVM coef?cients are optimally determined also using the training data like the model parameters,such an obtained model may manifest over-?tting and perform poorly in prediction.A more advisable way is to determine the model parameters by means of

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Fig.7.Frequency sequences measured and generated by the neural network model:(a)training data;(b)validation data.

training data while optimizing the SVM coef?cients with the use of independent validation data.

5.Linkage with bridge maintenance and management

The development of structural health monitoring methods for the detection of damage occurrence,location and severity has now attained some degree of maturity.However,the application of these monitoring data/results for instructing bridge inspection,maintenance and management is still in its infancy[61].A gap between health monitoring technology and bridge inspection,maintenance and management exercises exists currently which impedes bridge managers from bene?ting from the monitoring system.From the monitoring data the bridge managers want to get answers to the serviceability and reliability issues:(a)has the load capacity or resistance of the structure changed?(b)what is the probability of failure of the structural members and the whole structure?Indicators of these performance issues are needed to enable the owners to allocate resources toward inspection,maintenance and rehabilitation of their structures.

A method for evaluating the failure probability(or safety index)of bridge components based on long-term monitoring data and time-dependent reliability analysis

is Fig.8.Frequency sequences measured and generated by the support vector machine model:(a)training data;(b)validation data.

proposed by the writers and envisaged for application in the management of the instrumented Sutong and Jiangyin Bridges.The proposed method is targeted to provide quantitative information to bridge managers for decision making on optimizing and prioritizing bridge inspection and maintenance.According to the reliability theory,the failure probability P f(or safety indexβ)of a structural component can be evaluated by considering both the member resistance (capacity)R and the load effect S as random variables:

P f=

r?s<0

f R(r)f S(s)d r d s(1)

where f R(r)and f S(s)are probability density functions of R and S.In this method,the probability density function of the load effect S(stress,bending moment,shear force, etc.)is obtained directly from continuous measurement or derived from continuously measured strain.Fig.9illustrates such a distribution of measured stresses at a deck component of the instrumented Tsing Ma Bridge.The probability density function f S(s)is then estimated from this measured distribution which is not required necessarily to be Gaussian. When the measured strain/stress distribution varies due to structural damage or loading condition,the probability density function f S(s)thus obtained will also be changed accordingly.

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Fig.9.Distribution of measured stresses in a structural component.

The probability density function of the resistance

(capacity)R for an intact(healthy)member is calculated using the mean value and standard deviation of material

strength prescribed in standards or obtained by in situ

material tests.When damage-induced change in member

stiffness or structural properties is detected by the health monitoring system,the probability density function f R(r) will be changed accordingly to account for the structural

damage.For instance,the moment resistance will be reduced byα/(1?α)when a fractional lossα(=1?EI d/EI u) in bending stiffness is detected[62].In this way,the effect of detectable structural damage(expressed in either a deterministic or probabilistic manner)on the failure probability is accounted for.

Corrosion and fatigue are two important factors affecting the structural performance of bridges.With the measured chloride data and stress history from long-term monitoring systems,the effects on the failure probability caused by corrosion and fatigue are estimated using the corresponding reliability models[63,64]and interpreted in terms of a ‘condition factor’f c.The overall failure probability is obtained by multiplying the‘base probability of failure’given in Eq.(1)with the‘condition factor’f c as well as the ‘loading factor’f l and‘redundancy factor’f r[65]:

P o f=P f·f c·f l·f r.(2) When the failure probability(or safety index)of structural components or sections under monitoring is obtained at regular intervals,it is easy to decide a bridge inspection/maintenance strategy because the correspondence between the safety index value and the required maintenance action has been established[66].For unmonitored structural components,the method is applied for safety index evaluation with the help of a statically calibrated structural(?nite element)model,measuredly determined loading spectrum and measurement data of neighboring monitored members.The proposed method possesses several attractive merits:(i)the load effect and its distribution are determined from?eld measurements; (ii)damage-induced change in member stiffness or structural dimensions can be re?ected in the resistance variable; and(iii)the effects caused by material deterioration or environmental loading are accounted for in the evaluation of the safety index.

6.Conclusions

In this paper,key technology issues concerning structural health monitoring of large-scale bridges have been outlined. These include(i)the current status and recent trends in bridge health monitoring exercises,(ii)implementation of innovative sensing and data acquisition systems in long-term health monitoring practice,(iii)application of advanced computational techniques in inferring knowledge from measurement data,and(iv)linkage between structural health monitoring technology and bridge inspection,maintenance and management exercises.It has been shown that advances in sensing systems,signal processing,communications and data-mining technology are providing a new way for inspection and monitoring of bridge safety.Bridge health monitoring systems can offer valuable information in evaluating structural integrity,durability and reliability,and in ensuring optimal maintenance planning and safe bridge operations.With the establishment of an appropriate linkage, structural health monitoring is ultimately able to provide direct information for bridge inspection and maintenance, and would most bene?t bridge owners with regard to asset management.

Acknowledgements

The work described in this paper was supported in part by a grant from the Research Grants Council of the Hong Kong Special Administrative Region,China(Project No.PolyU5142/04E)and partially by a grant from the Hong Kong Polytechnic University through the Area of Strategic Development Programme(Research Centre for Urban Hazards Mitigation).Sincere appreciation goes to Dr.K.Y. Wong of the Hong Kong SAR Highways Department for his all-out support to the research described in this paper.We are grateful to our colleagues Prof.H.Y.Tam,Dr.T.H.T. Chan,Dr.K.Q.Fan,Mr.X.G.Hua,Mr.H.F.Zhou and Mr.G.Chen for their contributions in relevant research and development works.Thanks are also given to the collaborative partners at Jiangsu Transportation Research Institute,especially Dr.Y.F.Zhang,on the structural health monitoring projects of the Sutong and Jiangyin Bridges. References

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关于运动的英语演讲稿 篇一：英语演讲运动sports 英语演讲运动sports All over the world people enjoy sports. Sports help to keep people healthy and happy, and to live longer. Sports change with the seasons. People play different games in winter and summer. Swimming is fun in warm weather, but skating is good in winter. Some sports are so interesting that people everywhere go in for them. Football, for example, has spread around the world. Swimming is popular in all countries near the sea or in those with many rivers. Some sports or games go back thousands of years, like running or jumping. Chinese wushu, for example, has a very long history. But basketball and volleyball are rather new. People are inventing new sports or games all the time. Water skiing is one of the newest in the family of sports. People from different countries may not be able to understand each other, but after a game together they often become good friends. Sports help to train a

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How to manage time Time treats everyone fairly that we all have 24 hours per day. Some of us are capable to make good use of time while some find it hard to do so. Knowing how to manage them is essential in our life. Take myself as an example. When I was still a senior high student, I was fully occupied with my studies. Therefore, I hardly had spare time to have fun or develop my hobbies. But things were changed after I entered university. I got more free time than ever before. But ironically, I found it difficult to adjust this kind of brand-new school life and there was no such thing called time management on my mind. It was not until the second year that I realized I had wasted my whole year doing nothing. I could have taken up a Spanish course. I could have read ten books about the stories of successful people. I could have applied for a part-time job to earn some working experiences. B ut I didn’t spend my time on any of them. I felt guilty whenever I looked back to the moments that I just sat around doing nothing. It’s said that better late than never. At least I had the consciousness that I should stop wasting my time. Making up my mind is the first step for me to learn to manage my time. Next, I wrote a timetable, setting some targets that I had to finish each day. For instance, on Monday, I must read two pieces of news and review all the lessons that I have learnt on that day. By the way, the daily plan that I made was flexible. If there’s something unexpected that I had to finish first, I would reduce the time for resting or delay my target to the next day. Also, I would try to achieve those targets ahead of time that I planed so that I could reserve some more time to relax or do something out of my plan. At the beginning, it’s kind of difficult to s tick to the plan. But as time went by, having a plan for time in advance became a part of my life. At the same time, I gradually became a well-organized person. Now I’ve grasped the time management skill and I’m able to use my time efficiently.

关于科技的英语演讲稿Technologyand

关于科技的英语演讲稿—Technologyand thepresenceofstudents,ladiesandteachers,everyone!iw aspreparedintervalsofcloudtoday,inhonorhereentitled“te chnologyandfuture”speech,iamveryproudofboth,butsomeune ase.inrecentyears,wehaveseenourgreatmotherland,thecause oftherapiddevelopmentoftechnology,whichallowmetoachines eifeelveryproud.rememberthatlongago,cellphoneusealmostt heonlyone,whichiscalled,butafewyearsago,cellphoneshasun dergonegreatchanges,notonlylookmorebeautiful,butalsouse more,youcanusethephonestotakepictures,meetings,internet ,textmessages,etc.aseriesofthingsthatitheirlifemoreconv enient,soiammoreawareofthestrengthofthetechnology,butia mjustafledglingsstudents,“technology”asthewordalsoawa reofthelimited,iamunabletousesomeverydifficulttheorytoe laboratetechnologyxuanji,norighttoworkontheireldersican promiseofthetechnologyblueprint.butiamwillingtouseastud ent’sperspectivetotheimaginetechnologyandthefuture.fromgene ticengineering“isaliveprinces”dream,nano-technology“

英语演讲稿：我喜欢运动

英语演讲稿：我喜欢运动 my favourite sport good afternoon, ladies and gentlemen. it’s my great pleasure to be here sharing my speech with you. today my topic is my favourite sport. look at my healthy skin , oh! can you guess which sport i like best? yes,it's swimming !that's why i look so handsome. when i was only 5 years old, i began to learn how to swim and i like it. i think swimming is an interesting sport and it's exciting. it's also a good way to keep fit . every summer, i go to swimming school . my parents say i’ll have a good health if i insist swimming in a right way. and i can grow taller if i go to swim often. so i always ask my coach some questions on swimming and he always helps me a lot. i can swim so well and i can swim breaststroke, backstroke and freestyle. if you also like swimming, join me! thank you! 我最喜欢的运动

成立分公司合同协议书范本 详细版

编号：_____________成立分公司合同 甲方：___________________________ 乙方：___________________________ 签订日期：_______年______月______日

甲方： 乙方： 甲、乙双方经过友好协商，本着公平公正、合作共赢的原则，就甲方委托乙方在设立和运营分支公司（以下简称分公司），特签订以下协议： 一、甲方的权益与义务： 1.甲方应向乙方提供在工商部门代为设立分公司的必要文件，并授权乙方代为办理设立手续； 2.在分公司设立后，甲方应将有关分公司的工商手续提供给乙方，并授权乙方进行运营； 3.乙方在分公司设立和管理工作中遇到困难需要甲方协助时，甲方应在第一时间给予乙方协助； 4.甲方有权对乙方提供的有关的身份凭证进行资格审查认定； 5.甲方有权对乙方设立和运营分公司的一切工作进行监督和领导； 6.甲方认为乙方工作不力或乙方行为有损甲方利益或乙方未按本协议书履行其义务时，甲方有权收回提供给乙方的手续，并撤销对乙方的授权； 7.甲方有义务向分公司提供经营范围内项目的技术支持（具体按项目规定执行）； 8.甲方有权监督分支公司的各项经营行为，以及财务状况； 9.甲方对分支公司的一切经营活动及员工聘用有监督权、知情权和管理权。 二、乙方的权益与义务： 1.乙方运营分公司的一切工作，只限于在分公司所在地，从事甲方要求的的销售、市场管理、信息搜集等工作，经营项目不能超出甲方经营范围。 2.乙方不得利用分公司，从事任何与甲方利益和要求不一致的行为。否则应赔偿由此给甲方带来的一切经济损失，并独自承担相应法律责任。 3.分公司经营场地的位置、规模、环境等应达到甲方要求，需经甲方审核确认同意后，方可使用。 4.乙方必须每月按时给总公司上报分支公司的经营报表和财务报表；不得偷税漏税，一经发现，

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合作设立分公司合同 甲方：___________________________ 乙方：___________________________ 签订日期：_____ 年_____ 月_____ 日甲方： 住所： 法定代表人：

乙方： 身份证号码： 经甲、乙双方友好协商，甲方同意乙方在_________ 省_____ 市______ 区，设立甲方分公司，订立如下协议条款： 一、甲、乙双方出资情况 1、分公司由甲、乙双方共同投资设立，总投资额为________ 万元，甲方出资_____ 万元占出资总额的_____ %。乙方出资___ 万元占出资总额的_______ %。 2、甲、乙双方承诺出资须于________ 年 ______ 月 _____ 日前缴纳完毕，并在合作期间内不得 随意抽回。 二、分公司的管理和分工 1、由乙方任甲方分公司负责人，负责公司的日常运营与管理，具体职责包括： （1）办理分公司设立登记手续。 （2）根据分公司运营需要招聘员工（财务会计人员须由甲、乙双方共同聘任）。 （3）审批日常事项。 （4）公司日常经营需要的其他职责。 2、甲方派_____ 到分公司与乙方共同参与管理分公司，辅助乙方对公司的日常运营与管理， 与乙方有同等的决策权。 三、甲方的权利和义务 1、甲方有权对乙方提供的有关证明自己具有履行本协议书规定义务的身份凭证进行资格审查认定。 2、派甲方人员______ 到分公司与乙方共同参与管理。 3、负责提供甲方的委托书、任职文件、公司章程、验资报告、股东会决议等文件，以便乙 方办理工商、税务等经营执照和有关手续。

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