Bridge s play an important role in transportation infrastructure system s. Intelligent and digital management of bridge s group is an essential part of the future intelligent transportation infrastructure system. This paper proposes a digital twin system for bridges group in the regional transportation infrastructure network, which is interconnected by measured traffic loads. In physical space, a full-bridge traffic load monitoring system based on information fusion of weigh -in-motion (WIM) and multi -source heterogeneous machine vision is set up on the target bridge to measure traffic loads, also lightweight sensors are employed on the bridges group for structural response information . Furthermore, by establishing mechanical analysis model s in the corresponding digital space and using the measured traffic loads as links, the working condition percepti on and safety warning of all bridges in the regional transportation network is achieved, forming an important support for further intelligent trans portation infrastructure system . The proposed digital twin system has been preliminarily implemented in a bridges group around Shanghai, China, demonstrating the feasibility of the technical framework proposed in this paper and the bright prospects. Index Terms —AI-driven machine vision , bridge digital twin system, multi -source information fusion , traffic load mo nitoring, structural health monitoring I. INTRODUCTION1 With the rapid development of Internet of Things, big data, and cyber physical system, the concept of digitalization of physical world has been proposed gradually and attracted much attention. Among them, the most representative one is the "digital twin ” (DT) , which was defined as digital representation of a physical entity dynamically through all-round real -time monitoring and control [1]. The concept of DT originated from NASA's Apollo project in 1969 [2], but was not defined until 2011 by Michael Grieves [3]. In 2012, NASA officially announced the development roadmap Manuscript submitted 9 April 2021 . This study is funded by the National Natural Science Foundation of China (51878490); the National Key R&D Progr am of China (2017YFF0205605); Shanghai Urban Construction Design Research Institute Project ‘Bridge Safe Operation Big Data Acquisition Technology and Structure Monitoring System Research’; and the Opening Project of National Key Laboratory for Bridge Stru ctural Health and Safety (BHSKL18 -05-GF). (* Corresponding author: Danhui Dan ) Danhui Dan is with the 2 Key Laboratory of Performance Evolution and Control for Engineering Structures of Ministry of Education, Tongji University, Shanghai, 200092, China, and also with the 1 school of Civil Engineering, Tongji University, Shanghai, 200092, China (e -mail: dandanhui@tongji.edu.cn) Yufeng Ying, is with the 1 school of Civil Engineering, Tongji University, Shanghai, 200092, China (e -mail: 1832282@tongji.edu.cn) Liangfu Ge, is with the 1 school of Civil Engineering, Tongji University, Shanghai, 200092, China (e -mail: liangfu@tongji.edu.cn ) & These authors contributed equally to this work. of DT, providing a series of new methods, which gr eatly promoted its development [4]. Since then, DT has been accepted by industry gradually. The concept of DT includes both physical structure in physical space and digital model in cyber space. The most significant thing is to establish an information exchange channel and interaction task s between the structure and model [5]. In the field of c ivil engineering, digital models corresponding to physical structure are easily obtained. These digital models include 2D and 3D drawings for design, and mechanical models for structural analysis. These models are mostly used in the design stage of structures, but rarely in subsequent construction and service stages. Although the BIM technology in recent years cla ims to serve the life-cycle of structure [6], [7], the BIM -based DT for service stage is still in preliminary exploration stage, as there is no information exchange channel established, and also no interaction task s formed [8]. Structural health monitoring system collects data from the sensors which are set up on structure, and then evaluates the health condition and safety risk of the structure [9]. It can not only provid es the information exchange channel between structure and model , but also provides various interaction tasks. So the structural health monitoring is the best field to realize DT in civil engineering . In recent years, with the rapi d develop ment of wireless sensor network , cloud co mputing, communication and other basic technologies, the idea and technology of DT has been initially applied in structural health monitoring. Davila et al. [10] proposed an auto matic Digital Twin System of Bridge s Group based on Machine Vision Fusion Monitoring of Bridge Traffic Load Danhui Dan1,2,*,&, Yufeng Ying1,&, Liangfu Ge1,& modeling met hod of BIM model including time -domain data from response sensors to describe the structure and its health monitoring system. This model supports dynamic visualization of key structural performance parameters, allowing seamless data updat ing and long -term management. On the basis of wireless sensor monitoring network, Bhuiyan et al. [11] designed a cyber -physical system (CPS) of structural event monitoring with WSNs and proposed a novel model -based in -network de cision making method, named MODEM. Yuan et al. [12] proposed a CPS -based temporary structure monitoring system to prevent potential structural failures . This system integrated a virtual model of corresponding temporary structure and a physical struct ure on the construction jobsite. Ozer et al. [13] established a CPS framework including bridge finite element model, smart phone, centralized and distributed computing facilities. Vibration information of s tructure was obtained by smart phone, and data is transmitted to server wirelessly. Then modal frequency of bridge was identified and finite element model was updated to evaluate the reliability of structure. Kang et al. [14] proposed multimedia knowledge -based bridge health monitoring method using digital twins . In order to synchronize physical and virtual space, data collected by sensors was used to update digital simulation models. Then several extreme situations were simulate d to ensure bridge health. However, due to the lack of specific and in -depth understanding of the way information interacts between the physical structure and the digital model . Above work only establishes relationship between them in form, and does not realize the application of DT in civil engineering deeply. At present, bridge health monitoring system mostly measure and analyze data from the bridge itself [15], [16]. Therefore, information exchan ge channel and interaction tasks between physical bridge and digital model naturally revolves around the monitored structural response s. Model -based structural health monitoring and diagnosis involves updating the digital model by using the differences betwee n the measured data and the initial digital model information, and then comparing the updated digital model with the initial digital model or the bridge itself to detect damage of the bridge, or to determine and assess its working performance [17], [18]. As the model updating is an ill -posed mechanics inverse problem based on limited measurement points, it is difficult to solve and the result is unsatisfactory [19], this technology is still far from engineering application, and is difficult to be further applied to the technical framework of DT. Another more direct interaction task is, evaluating the status of bridge by comparing measured response s with calculated response s from digital model, then achieve the goal of life prediction, safety early warning and so on [20]. In fact, due to the lack of load information, especially the traffic load information, it is impossible to use digital model to calc ulate structural responses and make further comparative analysis, and interaction task can not be established. Fig. 1. Connection and interaction task between physical bridge and digital model In fact, traffic load is the main live load bridge bears . When the traffic load is known, the calculat ion of corresponding structural responses using digital model becomes a forward problem, which is mature to solve with low difficulty, high efficiency and reliable results. Based on this consideration, measured tra ffic load s can be used as the information exchange channel between physical bridge and its digital model, and the interaction task can be formed around measured traffic load and calculated response s, which is an ideal way to realize bridge DT. In order t o correlate traffic loads to bridge responses, Rui Hou et al. [21] proposed a CPS framework in a highway corridor, to trigger SHM systems to record bridge responses, automat ically link the bridge response s with truck weights colle cted by weigh -in-motion (WIM) stations installed along the corridor. This work shows the possibility of using measured traffic loads as interaction and realizing DT application in the field of health monitoring. The auth or of this paper has proposed identi fication methods of full-bridge traffic load distribution based on computer vision in 2019 and 2020 [22], [23], which have preliminarily solved the problem of bridge traffic load measurement. On this basis, anti -overt urning monitoring method of box girder bridges based on measured traffic load is established [24], [27], as well as simulation method of traffic load model based on machine vision [28]. These studies have shown a good prospect of applying DT to bridge s group health monitoring . For this reason, with the foundation laid by previous research, this paper propose s a bridge DT system connected by measured traffic load, and apply information fusion of machine vision and WIM System to monitor full -bridge traffic load s on the target bridge in a bridge s group . Then according to the different purpose of analysis, corresponding finite element models of all bridges in the bridge s group are established to run independently on the cloud server. By applying measured traffic load from physical bridge to the corresponding digital model, the model can analyze mechanical responses on-line. And according to the calculation results , evaluation and prediction of the physical bridge working status and safety early warning are realized. II. PROTOTYPE FRAMEW ORK OF BRIDGE DIGITAL TWIN SYSTEM A. Subsystems of Bridge Digital Twin System As mentioned above, bridge traffic load monitoring is the natural link between bridge in physical space and its DT > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE -CLICK HERE TO EDIT) < 3 model in cyber space. In order to realize monitoring and prediction of working status and safety early warning of the bridges group, this paper intends to establish bridge s group DT system based on mea sured traffic load s, and applies the measured traffic load s to the DT model s of bridge s group to calculate mechanical response s. This system can be divided into three subsystems: traffic load monitoring subsystem based on machine vision and Weigh -In-Motion information fusion, working condition real -time monitoring subsystem of target bridge, and working condition early warning subsystem of adjacent bridges. The specific geographical location of target bridge and adjacent bridge in bridge s group are shown in following Fig. 2. Fig. 2. Composition of bridge digital twin system of bridge network To arrange traffic load monitoring subsystem, a single bridge on main line in the bridge s group is selected as monitoring target bridge. Usually, taking the full b ridge deck as monitoring range, high -definition cameras and Lidar arrays are arranged longitudinally. Weigh -In-Motion System s are embedded in pavement at the beginning and end of main bridge on both sides, and ensure that the center line of lateral positio n of WIM is within the field of first camera’s view or within the scanning range of Lidar. The field of view of multiple cameras overlap longitudinally along bridge deck to ensure that full bridge deck is c overed with video image signals [22]. Video image obtained by high -definition camera and 3D point clouds scanned by Lidar are used to monitor real-time position of all vehicles passing on th is bridge, at the same time Weigh -In-Motion System is used to obtain the vehicle weight and axle load information. Three kinds of information need to be uploaded to cloud server synchronously for centralized processing. The whole layout is shown in following Fig. 3. Fig. 3. Sensor layout of traffic load monitoring subsystem As the traf fic load is monitored from target bridge, so it can be used on target bridge DT model directly . In fact, a bridge can have any number of digital models. The difference of each digital model lies in the purpose of analysis, method of mo deling and precision of model . Therefore, according to the specific calculation purpose, measured traffic load s can be loaded on different digital model s, and mechanical response s at specific position on digital model can be calculated , aiming to estimate physical bridge respo nses at the same position, then structural working condition monitoring and early warning of safety accidents are realized. In order to enhance the monitoring effect, a lightweight response monitoring system can be installed at designated location of physi cal bridge, then obtain measured response s which corresponding to calculated results. By comparing them, more effective bridge working condition monitoring and safety early warning methodologies can be developed. This is the main function of working condit ion real-time monitoring subsystem of target Bridge, which shows a complete DT system for target bridge, as information is transmitted two -way between target bridge and its digital model, and information processing process also reflects the interaction bet ween them. The relationship can be shown in Fig. 4. Fig. 4. Digital twin system on the target bridge All bridges in the area share the traffic loads running in transportation network. Therefore, traffic loads on different bridges at different locations s hould be correlated with each other. The traffic load s monitored on target bridge can moved to other adjacent bridges in bridge s group partly. Based on this consideration, DT system for other adjacent bridges in the bridge s group can be established. The tr affic load s measured on target bridge are still used as the link between other adjacent physical bridges and their corresponding digital models. Moving measured loads to DT model s of adjacent bridges, then these bridges’ mechanical responses can be calcula ted. By comparing calculated results with threshold, working status evaluation of these bridges are realized. Since the moving process of measured traffic load s in cyber space takes almost no time, for the DT model s of adjacent bridge s which located in the forward direction of measured traffic load s, the time to load and calculate can be much less than the time when measured traffic load s are really moved and loaded on physical bridge according to actual speed, so safety early warning with large allowance can be realized. Of course, for the DT model s of adjacent bridge s located behind the measured traffic load s, also can be loaded in reverse in time, then to replay the past response s history. In addition, if lightweight response monitoring system s are instal led on these bridges, comparison between the prediction response s and measured response s can be realized, which lays a foundation for further interaction task between physical bridge s and digital model s. The DT system for other adjacent bridge s can be show n in Fig. 5. Fig. 5. Digital twin system on the adjacent bridge s B. Measured Traffic Load ，A Link Between Digital Twin System Among the three subsystems, the first subsystem is aimed to measure traffic load s, and the second and the third subsystems take th e measured traffic load s as a link to realize the interaction between bridge in physical space and digital model in cyber space. It can be seen that measured traffic loads are the key feature of the DT system proposed in this paper. In this system framewor k, measured traffic loads include following forms: 1) Traffic load of single veh icle （TL 1） Main load parameters of TL 1 include vehicle weight (axle load), speed, real -time position on target bridge (including horizontal and longitudinal position ), as well as profile size. When traffic load of this form is moved to digital model s of other adjacent bridges, the position information measured on target bridge can be ignored, and the most unfavorable loading position should be considered according to analysis purpose . 2) Traffic load of vehicle q ueue in local single -lane （TL 2） In addition t o the main parameters of single vehicle traffic load, other parameters like formation shape and group speed of the queue should also be included. When this type of > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE -CLICK HERE TO EDIT) < 5 traffic load is loaded on digital model of target bridge, other traffic load s distribution i n the rest part of bridge deck should also be considered. When loaded on digital model s of other adjacent bridges, the most unfavorable position should be considered. Generally speaking, traffic regulations around the world do not support the formation of heavy vehicle queue in local single -lane, but whether the queue is formed intentionally or accidentally, such loading form is unfavorable to structure and deserve attention. 3) Traffic load of full bridge deck （TL 3） Weight (vehicle weight and axle load), pos ition, and profile size of all vehicles within full deck area of single bridge (target bridge) need to be monitored precisely. If this measured load is loaded directly on target bridge digital model, it can accurately analyze real -time mechanical response s of target bridge. It can also be loaded on other adjacent bridge s digital models after proper cutting longitudinally to predict or replay mechanical response s of adjacent bridge. 4) Statistical steady state traffic load （TL 4） Usually, it is necessary to continuously monitor full bridge deck traffic load for a long time to get a traffic load model, which includes selection of description parameters, selection of statistical distribution types of parameters, and estimating and updating statistical model of each parameter regularly using measured information. This type of traffic load can be loaded not only on target bridge digital model, but also on digital model of other adjacent bridges after proper adjustment. 5) Statistica l steady state traffic load spectrum （TL 5） Based on long -term monitoring of the full-bridge traffic load, the statistical value s of traffic load s in time domain and frequency domain can be estimated at each loading point on the bridge deck, and can be used to estimate vehicle -induced dynamic response spectrum s of structure by loading them on digital model of target bridge and other adjacent bridges. 6) Statistical cumulative traffic load （TL 6） Based on long -term monitoring of full bridge deck traffic load, the fatigue load at each loading point on bridge deck over a period is calculated, which c ould be used for fatigue assessment. These six different forms of traffic loads are obtained by traffic load monitoring subsystem, and are used for interaction between physical bridges and digital models in different scenarios. Among them, the measured single vehicle traffic load can be used for early warning of large transport in transportation network and traffic management, which is of great practical value; the measured local single -lane vehicle queue traffic load can be used to predict response s of some special bridges under least favorable distribution , such as overturning risk prediction of box girder bridges; the measured full bridge deck traffic load s can be used to estimate real -time response of any position in bridge . The ab ove three kinds of traffic load can be directly monitored, while statistical steady -state traffic load, statistical steady -state traffic load spectrum and statistical cumulative traffic load are obtained through statistical analysis on the basis of measured full bridge deck traffic loads, therefore, the last three type can also be called indirect measured traffic loads. Limited to the length of this paper, in following examples, this paper only illustrates application of the me asured full bridge deck traffic load and the measured statistical steady -state traffic load for monitoring and evaluation of bridge s group . Before specific application, we need to explain two key technologies in detail. The y are full bridge deck traffic lo ad monitoring technology based on machine vision and WIM information fusion, and modeling technology of bridge mechanical analysis DT model for different purposes. III. KEY TECHNOLOGIES IN BRIDGE DIGITAL TWIN SYSTEM A. Full Bridge Deck Traffic Load Monitoring Tech nology Based on Information Fusion of Machine Vision and WIM As mentioned above, the measured bridge traffic load s includes six basic forms, among which single vehicle traffic load is the most basic form, and other several forms of traffic load can be deri ved from single vehicle traffic load. The essence of single vehicle traffic load monitoring is real -time measurement of moving vertical force on bridge deck, which includes size of the force and immediate position of the force. BWIM technology uses bridge itself as a measuring tool to identify si ze and position of moving force, the principle is using sensors arranged on bridge to measure mechanical response s, and then through mechanical reverse analysis to identify force [29]. Du e to the ill-conditioned equations caused by limited measuring points and nonlinear factors of vehicle -bridge coupling, the identification quality of this technique for bridge single vehicle traffic load is not ideal, and it is difficult to measure the for ce in real time and realize full bridge coverage. For the more complex monitoring task of full bridge deck traffic flow load s, this technology is more difficult to be competent. Another idea is to identify separately, the size of force is measured by WIM s ystem [25], and the position of force is monitored by machine vision system based on video monitoring [26]. Full bridge deck traffic load s monitoring technolog ies proposed by author of this paper in 2 019 and 2020 belong to the latter technical route [22], [23]. On the basis of this technology, an improved prototype architecture of full bridge deck traffic load monitoring system based on multi -source information fusion is proposed in this paper, as shown in Fig. 6. Fig. 6. Technical route of bridge traffic load spatio -temporal distribution perception system Notes: SMWI : Synchro Matching with Vehicle Weight and First Image Frame ; TD: Multi -Moving-Target Detection Module Based on Yolo -v3 CNN ;OM: Optical Measurement Module for Multi -Moving -Vehicle ; TT: Track Tracking Module; MFVC：Multi Field of View Connection Module ; IMTL : Integrated Monitoring Module for Full Bridge Deck Traffic Load The proposed prototype system in Fig. 6 consists of hardware subsystem and software subsystem. Hardware subsystem is the acquisition equipment of vehicle weight, vehicle driving video and vehicle 3D point cloud information. It is composed of multi -source vision sensor network (including high -definition camera and Lidar ), WIM system, data field collection and processing system and data communication system. All these devices have mature products in current market, and their technical parameters can meet the needs of this system. multi -source vision sensor network is installed on bridge deck of target bridge, cameras and Lidar are arranged into sensor -array in bridge area according to the span of bridge, and piezoelectric quartz WIM sensors are buried in approach bridge or main bridge pavement layer near the expansion joint. Software subsystem is composed of two kinds of cloud databases, remote data com munication control module, full bridge deck traffic load s perception cloud software platform and various functional modules. It is used for real -time perception of full bridge deck traffic load s, and still needs to be developed. Main functional modules of the software subsystem include: Synchro Matching with Vehicle Weight and First Image Frame module (SMWI), Multi -Moving -Target Detection Module Based on Y OLO -v3 CNN (TD), Optical Measurement Module for Multi -Moving -Vehicle (OM), multi -vehicle track tracking module (TT), multi -field-of-view connection module (MFVC), and Integrated Monitoring Module for Full Bridge Deck Traffic Load (IMTL). All the above software modules work together to complete real -time identification of full bridge deck traffic load s under unified software platform scheduling. Processing sequence of multi -source vi sion information and WIM information in each module is related to the spatial layout of sensor network. For long -span bridges, in order to realize full coverage of visual images o n bridge deck, there are many layout forms. One of the most commonly used layout is that multiple cameras are arranged on deck in turn with the same orientation. Within this arrangement, full bridge deck monitoring area can be divided into three different areas along longitudinal direction of bridge. The first is starting area of bridge, which is limited to WIM sensor s layout line (i.e. starting line of first camera’s field of view). In this range, SMWI module completes information fusion of WIM and video i mages. The second is track tracking area on bridge deck, whose range starts from starting line of first camera’s field of view to starting line of second visual camera’s field of view. In this area, TD, OM and TT modules cooperate with each other to comple te location and tracking of all moving vehicles within this range. The third is view handover area. In addition to the TD, OM and TT modules, MFVC module is also needed to complete precise docking of same vehicle in two different fields of view. Entire bri dge deck is fully covered by the latter two are as in turn, partially over lapping, as shown in Fig. 7. > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE -CLICK HERE TO EDIT) < 7 Fig. 7. Longitudinal distribution of each software module along bridge deck （Notes: W -Weight; L -Location） In order to realize intelligent perception of full bridge deck traffic load s, the primary task is to identify vehicles from video images or point cloud, which will be completed by Multi -Moving -Target Detection Module (TD). In earlier work, the author of this paper propose an intelligent target detection scheme for multiple moving vehicle on bridge deck . This scheme designed a YOLO - v3 network structure (as shown in Fig. 8) . Using the advantages of this CNN in illumination rob ustness and recognition accuracy, synchronous high quality optical detection of overall and rear of vehicle is successfully realized. At the same time, a method of accurately identifying vehicle position and estimating width and length of vehicle is propos ed, so as to finally realize real -time accurate identification of traffic flow loads on full bridge deck. Section 4.1 illustrates the recognition quality of this method. Fig. 8 . Network structure of yolov3 overall and rear of vehicle dual target detect ion model For t he vehicles far from the camera, the image may be blurred , in order to further improve the recognition accuracy of real time position , besides the high -definition camera , fixed -base Lidar can also be used at the same time . Using Lidar to ob tain 3D point cloud s of bridge deck, and through information fusion , the longitudinal coordinate of vehicle can be corrected , more accurate perception of position can be realized . Dual YOLO -v3 network structure can be used to realize moving vehicle target detection for two different signals, and then feature -level information fusion method can be used to obtain high -precision estimation of vehicle coordinate on bridge deck, as shown in Fig. 9. Fig. 9. Dual Yolo v3 network fusion target detection model o f high -definition camera and lidar video signal B. Modeling Technology of Bridge Digital Twin for Different Mechanical Analysis Purposes Traffic load is the most important live load of bridge, vehicle -induced response is important for bridge safety and servi ceability. The primary task of DT model for target bridge and other adjacent bridges in transportation network is to analyze and calculate mechanical response s under above six measured traffic loads. And the process should be continuous, on -line and real -time. Of course, in addition to vehicle -induced response s calculation, DT models can also be used for other mechanical analysis purposes, such as system identification, performance evaluation, fatigue analysis and so on. According to the different purpose s, bridge DT model can be divided into following categories: 1) Transient static response analysis DT model Transient static response analysis DT model, is used to analyze transient static response s of bridge under traffic loads, especially the maximum transien t static response s at key positions. It should ignore dynamic impact effect of traffic load s, and then establish static analysis model of structure under static equilibrium condition. In general, this kind of analysis is linear elastic, because in the desi gn principle of modern engineering structure, bridge is within the linear elastic working range under normal traffic load s. Of course, in some special cases, such as bridge in danger or super -large transportation, nonlinear analysis should be taken into co nsideration. 2) Transient dynamic response analysis DT model Transient dynamic response analysis DT model, is used to analyze dynamic response s of bridge under transient or short -time traffic flow loads. This model is based on dynamic balance equation, which needs to consider dynamic impact effect of traffic load s and influence of pavement roughness. This model is usually used to evaluate dynamic stability and driving comfort of entire bridge or local members under traffic load s. 3) Statistical analysis of steady -state static response DT model Statistical analysis of steady -state static response DT model, is used to analyze statistical properties of various static response s of bridge under steady -state traffic flow loads. Traffic load is a special random load, it is statistically stable when the investigation time is long enough. However, due to the correlation of its temporal and spatial distribution, and the partially nonlinearity of the structure, the independent statistical analysis method at each time can not be used. So Monte Carlo numerical simulation method should be used on this model usually. 4) Steady -state dynamic response spectrum analysis DT model Steady -state dynamic response spectrum analysis DT model, is used to analyze various steady -state dynamic response spectrum properties of bridge under steady -state traffic flow loads. Similar to type 2, dynamic effect of traffic loads and road roughness need to be considered in modelin g. Also due to the correlation of spatial and temporal distribution , as well as nonlinear influence, response spectrum analysis caused by steady -state traffic load s will be difficult according to classical random structure vibration theory, so numerical simulation is needed with this model. 5) Steady -state performance indicator or damag e indicator analysis DT model Steady -state performance indicator or damage indicator analysis DT model, is used to analyze various performance indicators or damage indicators of bridge under steady -state traffic flow loads for structur al health monitor ing and damage diagnosis. These indicators can be defined from the steady -state static response s in type 3, or from steady -state dynamic response spectrum s in type 4. 6) Model updating DT based on steady -state response Model updating DT based on steady -state resp onse, is used to update structur e model under steady -state traffic loads, and realize perception of structural properties based on monitoring steady -state big data. This requires a lightweight health monitoring system installed on structure to monitor the difference between theoretical and measured steady -state response s in target position, then to continuously update DT model online, so as to realize comprehensive health condition perception of bridge. > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE -CLICK HERE TO EDIT) < 9 7) The online fatigue analysis DT The online fatigue anal ysis DT model, is used to analyze fatigue state of structure under measured cumulative traffic loads and to predict fatigue life on -line. In essence, this kind of fatigue analysis is a forward analysis of fatigue stress spectrum or damage cumulative variab les under actual traffic loads . Therefore, we do not need to predict hot spots in advance, and can realize fatigue analysis at any position of structure. According to the purpose and scale of mechanical analysis, different modeling methods of DT model can be selected, range from simple force and displacement method in structural mechanics to multi -scale finite element method. It represents two different modeling style, namely portrait and freehand. Generally speaking, when analyzing entire bridge structure, or evaluating overall performance, we can establish DT model according to the structure mechanics equation sketchily, or choose simple finite element model; when local force analysis is needed, we can choose the more complex beam grid finite element , she ll finite element, or even solid finite element. The former has clear physical meanings and are easy to grasp main features, while the latter can obtain more trivial details or precise results. In order to balance the advantages of two modeling styles and obtain higher computational efficiency, bridge digital model can be multi -scale. There are many foci about bridge in physical space . Under the same traffic load s, there are many overall or local analytical method, and purpose of analysis includes safety, serviceability and durability . So one bridge can have several different DT models. These DT models are deployed in cloud application server in the form of standalone programs. Each model includes mechanical calculation and ana lysis module and matched traffi c load loader , on standby in cloud. Once a certain form of measured traffic load s loading condition is formed, online mechanical calculation is started. Table 1 shows some bridge DT models formed in author's previous research: TABLE Ⅰ SOME EXAMPLES OF DIGITAL TWIN MODEL DT model Prototype in physical space Model in cyber space Type of measured traffic l oad used Model type Description Strain s Correlation Coefficient of assembled bridge [30] TL 1-4 Type 5 Used to analyze mid - span strain correlation coefficient of each beam under steady -state traffic load, and to monitor lateral cooperative working performance of assembled bridge . Modal amplitude ratio index of assembled bridge 1P 1P2号梁 2P 2P1号梁 1P 1P1号梁 2号梁 2P 2PS-S C-C TL 1 -5 Type 5 Used to analyze transverse mod al amplitude ratio of assembled bridge under steady -state traffic load spectrum, and to diagnose the transverse cooperative working performance of assembled bridge or widened bridge . Measured displacement spectrum similarity of assembled bridge [31] TL 1 -5 Type 5 Used to analyze similarity of measured displacement spectrum of assembled beam bridge under steady -state traffic load spectrum, and to diagnose lateral co operative working performance . Strains correlation coefficien t of adjacent spans TL 1 -4 Type 5 Used to analyze strain correlation coefficient of adjacent spans of simple -beam -to-continuous -beam brid ge under steady -state traffic load, and to diagnose the degradation degree from continuous -beam to simpl e-beam . Transverse modal amplitude ratio of multi -beam u1 (x, t)P1 ......... ...... ..................... ... u2 (x, t) un -1(x, t) un (x, t)P1 P2 Pn-1 Pn n-1 nk1k1k1k1 k2k2k2k2 kn-1kn-1kn-1kn-1 ......... P2 Pn-1 Pn TL 1 -5 Type 5 Used to analyze transverse modal amplitude ratio of assembled bridge under steady -state traffic load spectrum, and to diagnose the lateral cooperative working performance . Transverse strain mo dal shape of assembled bridge [32] 分布式弹簧 虚拟刚臂 TL 1 -5 Type 5 Used to analyze transverse strain modal shape and derived index of assembled bridge under steady -state traffic load spectrum . Overturning resistance of box girder bridges [27] TL 1 -4 Type 1 Type 3 Used to analyze time -varying overturning reliability and real -time overturning warning under measured traffic load s. Anti-overturnin g performance of box girder bridges [24] k0 k1 k2 kn-2 kn-1 kn kn+1 kn+2 km km+1 l1 l2 ln-1 ln ln+1 ln+2 lm+1P1 Pi PN-1 PN TL 1 -3 Type 1 Used to analyze real -time bearing reaction under measured traffic load s and predict overturning risk . Response analysis DTM of cable -stayed bridge TL 1 -7 Type 1 Type 2 Type 3 Used to analyze response of cable -stayed bridges under measur ed traffic load s. Taking the response analysis DT model of cable -stayed bridge under measured traffic loads T able 1 as an example, modeling method and basic process are illustrated. Corresponding prototype of this cable -stayed bridge response analysis DT model in physical world is a cable -stayed bridge built in May 1992, which is located in the central area of Ningbo, China. The main bridge is a pre-stressed concrete cable -stayed bridge with single tower and double cable plane , it has unequal span of 105 (m) + 97 (m), and is a tower -pier-girder fixed system. According to the actual size of bridge, finite element model is established by OpenSees. Main girder, pier and tower adopt elastic beam -column element and cable adopts truss element, other mechanical properties and constraints are assigned by corresponding OpenSees modeling commands. When programing above model, in order to improve the generality of finite element modeling code, structural data is separated from the code and placed in database in a cer tain format. A script written in Python extract the data needed for structural analysis and generate finite element automatically. The final finite element model program and traffic load s loading program are deployed on cloud server, and related basic data are stored in supporting remote database. Ⅳ. PRELIMINARY REALIZATION A. Preliminary Realization of Full Bridge Deck Traffic Load Monitoring According to the prototype framework of DT system for bridge s group constructed above, the author of this paper has successively installed some traffic load monitoring system on bridges. Such as Tongji Road T4 Ramp Bridge (Baoshan District of Shanghai, 2016), Yongjiang Bridge ( Center of Ningbo , 2017), and Qingfeng Road Ramp Bridge (Center of Ningbo , 2017 ). Also several tr affic load monitoring schemes with different scales are designed to be built . The specific information of these installed or desi gned traffic load monitoring systems are given in Table 2 below. TABLE Ⅱ TRAFFIC LOAD MONITORING SYSTEM INSTALLED OR DESIGNED Number Bridge name Length of covered bridge deck Number of HD camera Number of WIM lane Install ation time 1 Tongji Road T4 Ramp Bridge 108m 2 2 2016 2 Yongjiang Cable -stayed Bridge 210m 6 2+2 2017 3 Qingfeng Road On-ramp Bridge 107m 2 1 2017 4 Qingfeng Road Off-ramp Bridge 153m 2 1 2017 5 Zhengzhou -Jinan High Speed 2016m 24 6 2021 -2022 > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE -CLICK HERE TO EDIT) < 11 Railway Yellow River Bridge 6 Kunyang Road Bable stayed Bridge 440m 18 6 2020 -2021 7 Honghe suspension Bridge 700m 4 2 2021 8 Shaoxing Intelligent Expressway Bridge s group 13.8km 12 26 2022 Among them, the traffic load monitoring system of Tongji road T4 ramp bridge in Shanghai was completed in 2016 (shown in Fig. 10). Main purpose of this system is to monit or safety risks of viaducts caused by abnormal traffic loads. This system is equipped with a 4 million -pixel infrared high-definition camera and a high -precision WIM system, which is in normal operation state and can continuously provide on -site data for s cientific research. The health monitoring system of Yongjiang Bridge includes many kinds of sensors, such as WIM sensor, bridge deck video monitoring facility, accelerometer, strain gauge, GPS, displacement gauge and so on. Traffic load monitoring system o f Yongjiang Bridge is formed by reorganization of WIM sensor and bridge deck video monitoring facility. Four Starlight high -definition cameras constitute a traffic flow video monitoring system covering full bridge deck, two WIM systems are installed at eac h end of approach bridge to monitor vehicle weight and speed from two directions on bridge, as shown in Fig. 11. Data of this system is transmitted to remote urban bridge monitoring center through public fiber -optic communication network, together with oth er monitoring data aiming for 24 -hour all -weather full bridge deck traffic load s monitoring. So far, the system has been working for nearly three years and has accumulated a large number of traffic video signals and WIM monitoring data. (a) HD camera on the gantry (b) WIM sensor strip (c) Traffic video signal on ramp bridge Fig. 10. Traffic Flow Load Monitoring System of Tongji Road T4 Ramp Bridge Fig. 11. Traffic flow load monitoring system of Yongjiang Bridge The traffic load identification technology mentioned in Section 3.1 was used to monitor and identify traffic load of Tongji Road T4 Ramp Bridge and Yongjiang Bridge respectively. For the traffic load monito ring system of Tongji Road T4 Ramp Bridge, measured WIM data and monitoring video from 3: 00 p.m. to 4: 00 p.m. on January 14th, 2018 are selected, traffic flow load s of full bridge deck with a length of 10 minutes were successfully identified. Considering the demand of traffic load s real time monitoring and the maximum time -consuming of single -step calculation of identification algorithm, video sampling frequency of traffic load is set to 15Hz. 08:00:15t 59:17:15t 19:28:15t Fig. 12. Identification results of traffic load distribution of N4 ramp bridge on Tongji Road in Shanghai To show the identification effect, traffic load s distribution on full bridge deck at three sampling instant are given in Fig. 12. At each time, position coordinates of each vehicle are accurately measured, the accuracy fully meets the needs of structure analysis of three -span continuous ramp br idge [21]. One rectangle frame represents one vehicle, measured weight values comes directly from WIM system with different color. It is also convenient to divide one rectangular frame into two or three parts, and each part uses a different color to represent the corresponding axle load value, so that more precise traffic loads can be obtained. These loads can be used to analyze local vehicle load effect combined with local analysis DT model. Two a nalysis application of DT models using this measured traffic load s will be introduced later in this paper. For the traffic load monitoring system of YongJiang Bridge, video sensor s array covering full bridge deck and two WIM system s on both sides are used. As the single -tower cable -staye d bridge is located in city center, and the needs of cityscape, we use the least number of poles on bridge deck to install cameras, so field of view of each camera is designed as shown in Fig. 11. For blind area near the bridge tower, two high-definition c ameras covering bridge deck at root of tower are added. For this kind of video sensor s array arrangement, we have designed special multi -vehicle track tracking module (TT), multi -field-of-view connection module (MFVC), these works will be shown in other pa per due to the paper length. By selecting monitoring data from 10:00 a.m. to 11:00 a.m. on November 16th, 2020, full bridge deck traffic loads of cable -stayed bridge of 5 minutes was identified, and traffic load s distribution was given in Fig. 13. This meas ured full bridge deck traffic flow s segment will be applied to the DT model of Yongjiang Bridge for real -time load effect s analysis. Fig. 13. Measured traffic flow load segment on full bridge deck of Yongjiang bridge (November 16, 2020) Statistical steady -state traffic flow load s of existing roads and bridges are actually a model describing a certain cross section of road or bridge deck . In order to obtain measured statistical steady -state traffic flow s of Yongjiang Bridge, the cross section locating in bridge tower is taken as representative. T he process includes , firstly, using a video camera located in middle of tower to o btain video image of each vehicle passing through this cross section, then using a special images target detection algorithm to detect vehicle profile and wheels, at the same time, using a special optical measurement algorithm to realize real -time on -line recognition of vehicle length, front and rear suspension length and lateral position. Secondly, combined with vehicle weight monitoring value from WIM system, measured traffic loads in local area near the bridge tower can be obtained. If this kind of monitoring and identification lasts for a longer period, observed parameters will gradually into stable state . At this time, statistical characteristics of each parameters can be obtained by statistical analysis, and then measured statistical steady state traffic flow s can be obtained. Also, with the identification of above key parameters, we can measure a more pr ecise statistical steady state traffic flow loads, and its core feature is a microscopic traffic flow model based on Intelligent Driver Model (IDM), which makes it closer to actual traffic load s. > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE -CLICK HERE TO EDIT) < 13 Fig. 14. Measured statistical steady -state traffic flow load on Yongjiang Bridge Fig. 14 shows a segment of this measured traffic flow load s. It includes four vehicles, and the speed, weight, wheel and center position coordinates of each vehicle are gene by the measured information. Long-term static and dynami c response s of target bridge and adjacent bridge s can be evaluated by applying this measured statistical steady -state traffic flow load s to corresponding DT model. The specific application will be given below. 。 B. Preliminary realization of Bridge Digital Twin system based on measured Traffic load Traffic load is the most important live load for bridge, it is not only main operating load of bridge, but also an important factor affecting bridge safety, serviceabili ty and durability. In fact, design and recheck of bridge during design stage and status evaluation during service stage are all based on traffic load effect. In design stage, design traffic load is based on empirical investigation, and is generally regulat ed as lane load and vehicle load, which are quite different from real traffic load during service period. This design load can only be used for analysis and recheck in design stage. In service stage, traffic load loaded on bridge is usually less than the design level, but in some cases it may exceed the level, which may lead to bridge safety accidents. In addition, some special service stage analysis, such as working status prediction , safety early warning, fatigue assessment, and long -term performance eval uation, all need several measured traffic loads described above. The system proposed above has realized the monitoring of actual traffic load s during service stage of bridge. Next , these measured traffic loads can be loaded on bridge DT models for differen t analysis purposes. In this paper, three analysis examples of bridge DT model s under measured traffic load s are given in order to preliminarily illustrate the usage and effectiveness of the system proposed in this paper. 1) Case1. Measured traffic load and i ts short -term effect: overturning risk assessment The main purpose of traffic load monitoring of Tongji Road T4 Ramp Bridge is to realize real -time monitoring of overloaded vehicles and abnormal queue formed by them. According to the identification results , some possible bridge safety accidents, like overturning and fracture, are predicted, and then to control the traffic of urban viaduct. Due to limitation of urban space, many viaducts and ramp bridges are single -column -pier box girder bridges. The anti-overturning capacity of these bridges is very limited, so overturning accidents may occur under some abnormal traffic loads. In order to predict overturning risk of single -column -pier box girder bridges under measured traffic load s, we establish a DT model f or real -time overturning early warning, shown in Table 1 . A segment of full bridge deck traffic flow from Tongji Road T4 Ra mp Bridge is loaded on this model, aiming at calculating support reaction of middle single pier, and risk of overturning can be judge d by whether the reaction force is zero. The analysis detail can refer to previous work [23]. Here, only the results are given, as shown in Fig. 15. Fig. 15. Analysis results of support reaction s of digital twin model for real-time overturning early warning analysis of box girder under measured traffic flow load Fig. 15 shows the support reactions calculated by real -time overturning early warning DT model of box girder bridge s under measured traffic flow load s (excluding the dead load). Support2 represents the middle bearing of the bridge, and Support 1-1, Support 1-2, Support 3-1 and Support 3-2 represent the four bearings on the side pier. The figure shows that the middle bearing is always under pre ssure, while the other four bearings are in an alternating state between positive and negative. In the whole process, the minimum value of the bearing reaction force is about -5kN, while the calculated side bearings reaction force is about 787kN under the d ead load. So the bearing reaction force caused by vehicle load are negligible, and there is no risk of overturning during the monitoring process in this bridge. This real -time overturning early warning DT model adopts a simple structural mechanics principl e, the analysis speed is fast, the result is accurate, and the process is simple and feasible. In addition , this measured traffic load s segment from target bridge can be used in other box girder bridges on the same route before traffic load s actual arrived , then to predict overturning risk. It can be seen that , interaction task between digital model s in cyber space and box girder bridges in physical space is realized to e nsure safety of bridge s and traffic . 2) Case 2. Measured traffic load and its short -term e ffect: prediction of transient vehicle -induced response of cable -stayed bridge It is interesting to use the DT model to calculate bridge transient response s under measured traffic load s. For two reasons, first, for the target bridge, it can break through t he limit of number of measuring points and realize the real-time calculation of any mechanical response s at any position of bridge, which is equivalent to place sensors at any position. Also, for the key position where sensor has been arranged, the current working statue of structure can be judged by comparing measured values with semi -measured and semi-theoretical values calculated by DT model. Second, for other adjacent bridges, measured traffic load can be moved and loaded into the corresponding digital model in the most unfavorable way to realize predict or playback of accident. To illustrate this function, measured traffic flow load s segments obtained from Tongji Road T4 Ramp Bridge and Yongjiang Bridge in section 4.1 are respectively loaded on the DT model of vehicle -induced response analysis of Yongjiang cable -stayed bridge. Loads segment 1 is loaded with unfavorable position, loads segment 2 is loaded with real measured position. Fig. 16 shows the change of longitudinal displacement of top of tower, d eflection of mid-span in west side and tension of SW.1 cable of Yongjiang cable -stayed bridge in these two cases. (a) longitudinal displacement of top of tower (b) deflection of mid -span in west side (c) tension of SW.1 cable (d) longitudinal displ acement of top of tower (e) deflection of mid -span in west side (f) tension of SW.1 cable Fig.16. Transient vehicle -induced response of cable -stayed bridge under different measured traffic load Fig. 16 (a), (b), (c) show the response s of cable -stayed bridge under the most unfavorable loading with measured traffic load s segment 1 from Tongji Road T4 Ramp Bridge, and Fig. 16 (d), (e), (f) show the response s of cable -stayed bridge under its own measured traffic load s segment 2 . No matter which loads segment is used, the response s calculated are very consistent with immediate distribution of vehicle on bridge deck according to frame -by-frame comparison, result accuracy is related to precision of DT model. In calculation process of vehicle -induced response s, single -step calculation time is within 0.05s, which is less than traffic load video sampling interval. It can be seen that within allowable accuracy range, the relatively simple DT model can reduce time-consuming of analysis and calculation, thus making real-time on -line structural response s analysis, as well as other further online evaluation and safety early warning possible. > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE -CLICK HERE TO EDIT) < 15 3) Case 3. Measured statistical steady -state traffic load and its long -term effect evaluation: Statistical analysis of cable -stayed bri dge response The advantage of establishing a real -time monitoring system of bridge traffic load is not only to monitor short -time traffic flow load s of full bridge deck, but also to obtain a large number of traffic load s data by long -term and equal time interval sampling. Based on statistical analysis and modeling of these large quantities of measured data, several type of statistical steady -state traffic load s models can be obtained. These statistical steady -state measured traffic loads, can be loaded on DT model of target bridge and other adjacent bridges, and to find statistical regularities of long -term static response s and dynamic response spectrum s. It can also be used to evaluate and predict fatigue life. To illustrate, the statistical steady -state tr affic flow load s of Yongjiang Bridge were measured using above technique and loaded on the DT model of response analysis of cable -stayed bridge. Because the random loads loaded on different joints of main girder are correlated, this kind of response s analy sis under steady -state traffic flow load does not follow the statistical characteristic transfer law of structure under independent random loading. So Monte Carlo simulation was used. The simulation is divided into three steps: first ly, random vehicle para meters are obtained based on the measured statistical steady state traffic flow load s model ; secondly, the traffic load s distribution of full bridge deck at a specific time is generated according to the Intelligent Driver Model (IDM) ; thirdly, apply the lo ads distribution into DT model to analyze and calculate structural response s. Repeat these steps until enough vehicle -induced response samples are obtained, and then analyze statistical regularities of response s in any desired positions. Some analysis resu lts are shown in Fig. 17 and 18 below. (a) scatter plot of strain at bottom of mid -span girder (b) probability density distribution of strain at bottom of mid -span girder (c) scatter plot of tension of SW.2 cable (d) probability density distribution of tension of SW.2 cable (e) scatter plot of displacement at top of tower (f) probability density distribution of displacement at top of tower Fig.17. Simulation analysis of digital twin model of Yongjiang bridge under measured steady -state traffic load Fig. 17 sh ows the semi -measured and semi -theoretical response s analysis results of DT model of Yongjiang Bridge. It can be seen that, under the statistical steady -state traffic flow load, whether it is the strain at bottom of mid -span girder, the tension of SW.2 cab le, or the displacement at top of tower, when the sample s are large enough, theirs value s are all statistically stable. Among them, the probability density distribution of strain at bottom of mid -span girder and tension of SW.2 cable are positive -skewed di stributions, the mean value of strain and cable tension is larger than the mode. For the strain, it basically increases under traffic load s, and for the cable tension, in most cases will increase, in a small number of cases will decrease, depending on the influence line function of this cable tension . The probability density distribution for longitudinal displacement of top of tower is normal distribution, and is symmetr ical under traffic load s, indicating the structure is nearly longitudinal symmetrical. These results show that response s of cable -stayed bridge has rich statistical meaning, and reflects regularities of structure under big data loading, which is worthy of further study. Fig.18. Spatial probability density distribu tion of deflection at bottom of main girder Fig.18 shows the spatial probability density distribution curve of deflection at bottom of main girder. On the whole, the spatial distribution surface composed of probability density curve of deflection (blue lin e) is a curved ridge surface, and the projection of the line connecting the highest point of each probability density curve represents initial configuration of main girder (without traffic load s). From the local perspective, the width of each probability d ensity distribution curve indicates distribution range of deflection value at this point, so the connection line of the starting (and ending ) points of each probability density distribution curve represents deflection envelope of main girder in simulated case. These contents indicates, the probability density spatial distribution curve contains rich structural mechanical characteristics, and can be used to excavate structural statistically behaviors regularities under big data loading,. The above two figure s show that, bridge DT model can realize structural long -term effect evaluation and statistical regularities study under measured statistical steady -state traffic load. How to use the result of evaluation to deduce health status of bridge prototype in phys ical space and form more interaction tasks between two spaces will be the future exploration. V. CONCLUSION Traffic load is the main live load bridges bears and affect s function and reliability of bridge in service period. Based on machine vision fusion monit oring of bridge traffic load s, this paper constructs a digital twin system for regional bridge s group , and realizes the interaction between physical bridge and digital model . By monitoring the traffic load s in physical bridge , and loading the actual traffi c load s to the corresponding digital twin model, semi -measured semi-theoretical response s are obtained. By comparing it with measured responses from physica l bridge , safety warning and working status evaluation of physical bridges can be realized. The tech nical framework of traffic load monitoring proposed in this paper has been realized in author’s previous research [22], [23]. The measured traffic loads of different forms from two bridges meet the real-time requirements of the actual monitoring system , and can be applied to various digital twin models. The proposed classification method of measured traffic load s and bridge digital twin model, as well as the modeling ideas are all proved reasonably through real case study . All these show that using measured traffic loads to realize interaction between physical bridge and digital model is the most direct and reasonable way. It can realize the interconnection and collaboration of transportation infrastructures, so as to promote the construction of future intelligent transportation infrastructure systems.