digitaltwin-basedsafetyevaluationofprestressed steelstructure

Research ArticleDigital Twin-based Safety Evaluation of PrestressedSteel Structure

Zhansheng Liu Wenyan Bai Xiuli Du Anshan Zhang Zezhong Xing and Antong Jiang

e Key Laboratory of Urban Security and Disaster Engineering of Ministry of Education Beijing University of TechnologyBeijing 100124 China

Correspondence should be addressed to Zhansheng Liu lzs4216163com

Received 22 June 2020 Revised 18 August 2020 Accepted 21 August 2020 Published 4 September 2020

Academic Editor Jiang Jin

Copyright copy 2020 Zhansheng Liu et al(is is an open access article distributed under the Creative CommonsAttribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

(e safety of prestressed steel structures in service has been studied widely However traditional safety assessment methods forprestressed steel structures involve few sample points do not provide accurate predictions and consume substantial human andmaterial resources (e digital twin technology can be used to monitor the structural behavior state and activity of a steelstructure throughout its life cycle which is equivalent to performing a safety assessment of the structure(e purpose of this studyis to establish a digital twin multidimensional model of prestressed steel structures Based on this model the support vectormachine and predictionmodel are trained using the relevant structural history data and the safety risk level of the structure is thenpredicted based on the measured data Finally a proportional reduction model of the wheel-spoke cable truss structure is used toverify the feasibility of the proposed method (e results show that digital twin technology can achieve real-time monitoring ofprestressed steel structures in use and can provide timely predictions of the safety level(is represents a newmethod for the safetyrisk assessment of prestressed steel structures

1 Introduction

(e prestressed space steel structure has a unique shape andsimple appearance and can improve the structural forcestiffness and stability of engineering structures this ensuresstructural safety as well as a visually pleasing design(erefore prestressed space structures have been widelyused in large stadiums in recent years However the longer astructure is in service the more it is affected by variousuncertain factors such as component failure temperatureeffects loss of prestress and bar construction error thisadversely affects the structural safety therefore the me-chanical performance and reliability of prestressed struc-tures are very important

In general the safety assessment process of prestressedsteel structures in service is to determine the change rule ofthe structure under various damage conditions accordingto the actual principle of the structure provide the degreeof influence of each damage condition on each member ofthe prestressed steel structure and finally determine thesensitivity and curve change of variables such as prestress

loss to the structure and the structural material Scholarsworldwide have studied the methods to realize structuralsafety assessment [1] Qiang et al studied the reliability ofsteel structural roof members under snow loads based onground snow load samples simulated using the snowmelting model [2] A semianalytical simulation methodcombined with numerical simulation was proposed by Liand Li to study the reliability of the structural assessmentsystem [3] Jiang et al analyzed the reliability of highstrength steel (HSS) built-up box column under differentwelding processes [4 5] Deng et al used the secondarydevelopment of Revit to build a construction hazard sourcesafety management system [6] In conclusion although theexisting research methods can achieve safety assessment toa certain extent analysis software such as ABAQUS andANSYS are usually used for analysis and calculation but itis difficult to determine the boundary conditions of thestudy and in service it is even more complicated withfewer sample points and lack of prediction hence it maynot be ideal for safety assessment of the prestressed steelstructures in service

HindawiAdvances in Civil EngineeringVolume 2020 Article ID 8888876 10 pageshttpsdoiorg10115520208888876

With the advent of the ldquoIndustry 40rdquo era digital twintechnology has become a feasible and effective method toachieve intelligent construction (e concept of digital twinwas first proposed by Professor Grieves (e digital twinsystem includes physical space virtual space and mutualdata interaction In 2012 NASA applied digital twins toaircraft maintenance and quality monitoring Concurrentlyit provided a clear definition of digital twin ldquodigital twinrdquo is amultidisciplinary multiphysical multiscale and multidi-mensional simulation process that makes full use of theentity model perceptron and real-time data It mirrors thephysical entity in virtual digital space reflecting the entire lifecycle process of the physical entityrsquos behavior state oractivity [7 8] In recent years digital twin technology hasdeveloped rapidly and has been increasingly used in thedesign construction operation and maintenance ofbuildings Lu and Brilakis realized digital twin automation ofreinforced concrete bridges using a slicing-based objectfitting method [9] Shim et al proposed a new generationbridge maintenance system based on the concept of thedigital twin model [10] In general digital twin technologycan realize the interactive fusion of the physical space andinformation space of the prestressed steel structure in ser-vice Users can collect real-time information of the physicalstructure through the virtual structure and use neuralnetworks to extract the correlation features of the data andthe safety assessment is then conducted on this basisCorresponding maintenance measures can be applied to thephysical structure based on the assessment results of thevirtual structure to ensure the safe service of the buildingwhich is in line with the basic concept of analysis evaluationand application of the data during the safety assessmentprocess of the prestressed steel structure during service(erefore digital twin technology provides new method forthe safety assessment of prestressed steel structures

2 Safety Assessment Method Based onDigital Twins

21 Framework Structural Safety Assessment According tothe structural characteristics and safety requirements ofprestressed steel structures guidelines for the structuralsafety assessment are proposed as shown in Figure 1 Firstlya virtual model of the prestressed steel structure is builtaccording to the physical building and sensors are arrangedon the physical building to collect the cable force dis-placement and other data during the service of the structure(e data are then transmitted to the master computerthrough the serial communications in order to realize thecollection and interaction of the data related to the serviceprocess of the prestressed steel structure Before using thestructure a sensor is installed on it to collect and store thestructural data (e data of structural displacement or cableforce change in the physical layer can be mapped to thevirtual layer in real time Structure-related data are stored inthe twin data service system Based on these data thestructure is evaluated for safety In addition the measureddata digital model and evaluation results of the structurecan be viewed in real time on the web page thus visual

management is achieved(is study focuses on the structuralsafety risk assessment method based on the digital twinmultidimensional model under the above framework

22 Digital Twin Multidimensional Evaluation Model(e definition of safety risk assessment is from a riskmanagement perspective using scientific methods to sys-tematically analyze the threats and vulnerabilities thenassess the degree of damage if a damage should occur andfinally propose targeted protective countermeasures andcorrective measures against threats [11ndash13] Subsequentlysafety risk assessment is conducted based on digital twins Incombination with the safety risk assessment framework andthe five-dimensional model of digital twins [14 15] amultidimensional model for the safety risk assessment isproposed in this study as shown in the following equation

MSDT BPEBVE SSDDOACN( 1113857 (1)

where MSDT represents the multidimensional model ofsecurity risk assessment BPE represents the building physicalstructure BVE represents the building virtual structure SS

represents the security risk assessment service for structuresDD represents the data related to security risk assessmentOA represents a risk assessment algorithm for processingdata and CN represents the connection between the variouscomponents (e security risk assessment process based ondigital twins is shown in Figure 2

In the aforementioned evaluation model BVE is com-posed of the geometric model (BGVE) physical model (BPVE)behavior model (BBVE) and rule model (BRVE) BGVE isestablished using three-dimensional (3D) laser scanningtechnology for 3D modelingBGVE needs to be characterizedby the dimensions installation position relationship andother information for each member of BPE Finite elementanalysis software is used to simulate and analyze the 3Dmodel and build BPVE BBVE describes the real-time variationof the structure under the action of different factors BRVErefers to the classification of the safety level of the structurebased on relevant norms and expert experience DD includesthe measured data (DD1) obtained by arranging the sensorsand historical related data (DD2) OA represents the pro-cessing of DD from which an algorithm to predict thestructure security level is obtained

23 Support Vector Machine Model A support vector ma-chine (SVM) is a small-sample training method which canproduce better training results with fewer samples of dataand can thus eliminate the requirement of collecting datasamples of prestressed steel structures [16] (e SVM modelconsists of training and recognition stages In the first stageinput and output samples are selected according to the actualproblem and the SVM is trained using existing data toobtain the maximum margin hyperplane In the secondstage the input data are from unknown samples and outputdata are those recognized by the SVM [17]

(e SVM model is built using the LIBSVM softwarepackage [18] developed by Professor Lin Chin-Jen of Taiwan

2 Advances in Civil Engineering

University LIBSVM not only contains the source code butalso provides the executable file which is user-friendly andeasy to operate LIBSVM is widely used for classificationregression and distribution estimation [19] (e generalsteps for using LIBSVM are as follows

(1) Select the sample set according to the format re-quirements of the LIBSVM software package

(2) Process the sample set(3) Choose an appropriate kernel function(4) Choose the optimal combination of parameters

penalty coefficient C and kernel function parameter g

(5) Train the entire training set with the optimal pa-rameter combination to obtain the SVM model

(6) Use the obtained training model to predict the testsample

24 Security Evaluation Method Based on Digital Twin andSVM Combining the multidimensional model based ondigital twin proposed in 12 and the SVMmodel proposed in21 the safety risk assessment method of the prestressed steelstructure based on digital twins and SVM is proposed (erequired steps include establishing the digital twin

multidimensional model and training the SVM using thecollected data to obtain the structural safety risk assessmentmodel

(e specific steps of the SVM method are as follows

(1) Select and process the data the sample data arederived from the monitoring data during service(DD1) and the related historical data (DD2) of BVETo avoid overfitting at least 80 of the data areselected as input for SVM model training (e re-mainder is used to test the accuracy of themodel andthe sample data need to be randomly scrambled (etraining sample set is xi yi1113864 1113865 i 1 2 n wherefeature vectors of xi are different factors affecting theBPE structure safety

(2) Choose the kernel function in the safety evaluationalgorithm (OA) selecting the appropriate kernelfunction is an important step Polynomial and radialbasis kernel functions are relatively common types ofkernel function the latter is superior in terms ofcalculation accuracy and other performance criteria(erefore this study selects radial basis as the pre-ferred kernel function

(3) Select the optimal parameter combination of Cand g in this study a cross-validation method is

Security assessment service

Visual risk assessment Visual data management

Datadriving

Twin data service system

Servicedata

Inte

ract

ive

feed

back

Inte

ract

ive

feed

back

Buildingshapedata

Buildingmonitoring

data

Historicaldata

Serialcommuni-

cation

Datamapping

Phys

ical

bui

ldin

g

Virt

ual b

uild

ing

Sensor

Figure 1 Framework of structural safety assessment

Advances in Civil Engineering 3

used to search for the optimal combination of Cand g

(4) Train and test the model in the entire security as-sessment service process (SS) model training andtesting are the core elements Input the trainingsamples selected in (1) and output the structuralsecurity level to train the network (e remainingsamples are used for testing thereby a security riskassessment SVM model of BPE is established (estructural safety risk level obtained by the trainingtest sample is compared with the actual risk level(ehigher the accuracy of the model the stronger itspredictive ability [20]

(5) Predict the security level the test samples are selectedfrom DD1 Input the test sample and output thestructural security level (e security risk level of BPEis predicted thus the security risk assessment serviceof BPE (SS) is implemented (e process is shown inFigure 3

3 Case Study

31 Model Description (e wheel-spoke cable truss is animportant form of the prestressed steel structure Its shape issimilar to that of a bicycle wheel It is commonly used in

large exhibition halls and Ferris wheels [21] In the process ofusing the wheel-spoke cable truss due to influences such astemperature changes [22] material corrosion complexloads and prestress loss the overall structure or localcomponents show different degrees of damage resulting inthe shortening of its life cycle and adverse impact on humanlife and property safety (erefore it is particularly im-portant to evaluate the safety of wheel-spoke cable trusses inservice In this study a proportional reduction model of thewheel-spoke cable truss structure is taken as an example

(e experimental model built in this study is a reducedscale test model based on a certain wheel-spoke cable trussproject Compared with the actual project the scale ratio ofthe test model is 1 10 the cross-sectional area ratio of thecable is 1 100 and the materials are identical (e structurespan of the test model is 6m and consists of 10 radial cablesring cables braces nodes outer ring beams and steel col-umns(e radial cables include upper radial and lower radialcables and the ring cables include upper ring and lower ringcables (e struts include outer middle and inner struts asshown in Figure 4 (e installation and construction processof this test model are as follows

(1) Installation of the steel columns and outer ring beamthe steel columns in the lower part are positionedand installed for the complete unit and then the

Security riskassessment servie

(Ss)

Security riskassessment

algorithm (OA)

Secu

rity r

isk as

sessm

ent (

CN5)

Building twin data (DD)

Iterative optimization

Physical real world Virtual digital world

Sensor (CN 2)

Serial communication (CN3 )

Simulation analysis (CN4 )

3D visualization (CN6 )

3D modeling (CN1)Building physical structure

(BPE)Building virtual structure

(BVE)

Figure 2 Digital twin-based security risk assessment process


outer ring beam and steel columns are connected bybolts

(2) Installation of the upper ring cable and the upperradial cable assemble the upper radial cable and theupper ring cable on the ground Connect the 10upper radial cables and upper ring cable in radialform according to the coordinates Install the upperring cable and upper radial cable clip after com-pletion of the connection

(3) Tensioning of the upper radial cable use the guidechain to tension the upper radial cable When theupper radial cable is sufficiently high above theground to install the struts install the inner middleand outer struts and the lower ring and lower radialcables (e guide chain is used to tension the upperradial cable After the upper radial cable is tensionedin place the upper radial cable head and the ear plateof the outer ring beam are connected using a pin

(4) Tensioning of the lower radial cable adjust the sleeveto adjust the length of the lower radial cable to thestructure forming state and finally install the lowerradial cable

32 Application Framework of Safety Assessment MethodBased on the method proposed above this study proposesthe safety risk assessment theory as shown in Figure 5Firstly a virtual model (BVE) is built according to thestructure entity (BPE) then sensors (CN2) are suitablyarranged on BPE to collect and store the internal force data(ere is a change in the cable force of BPE while operationalwill synchronously reflect on BVE thus realizing the dynamicsynchronization of physical space and virtual space(e data(DD2) collected by the experimental model are used to trainthe model Based on the monitoring data (DD1) the trainedSVMmodel is used to perform the security assessment of thephysical structure in the physical layer thus the risk pre-diction for the structure (SS) is achieved In addition BVEand DD collected by the sensor can be displayed in real timeon the web page where the relevant personnel can also viewthe final structural safety level (is study explored thesefunctions

Based on the method proposed above this study pro-poses a set of safety risk assessment theory as shown inFigure 5 Firstly a virtual model (BVE) is built according tothe structure entity (BPE) and then sensors (CN2) arereasonably arranged on BPE to collect and store the internalforce data (e change of cable force of BPE in the process ofuse will be reflected on BVE in synchronously which realizesthe dynamic synchronization of physical space and virtualspace(e data (DD2) collected by the experimental model isused to train the model Based on the monitoring data(DD1) the trained SVM model is used to carry out securityassessment for the physical structure in the physical layerrealizing the risk prediction service of the structure (SS) Inaddition BVE and DD collected by the sensor can be dis-played in real time on the web page and the relevantpersonnel can also view the final structural safety level on theweb page (is study explored these functions

33 Application Based on Digital Twin Model SecurityAssessment (e BVE of the wheel-spoke cable truss shouldreflect the structure entity in the physical space accuratelyand in real time Digital twin models include geometricmodels (BGVE) physical models (BPVE) behavior models(BBVE) and rule models (BRVE) (e establishment of the 3Dmodel of BGVE is based on 3D laser scanning technology

Start

Sample selectionand processing

Trainingsamples

Testingsample

Select kernelfunction

Choose the optimalparameter

combination

Model trainingand testing

SVM predictionmodel

Ss

Structuralsafety level

End

OA

DD2 DD1

Collectingdata

Figure 3 Digital twin and SVM-based security evaluation model

Figure 4 Test model


ANSYS is used to conduct the static analysis of the 3Dmodeland establish BPVE BBVE depicts the real-time changes of thestructural cable forces under the different factors by que-rying the relevant specifications and soliciting the expertsrsquoexperience the safety level of the cable under different in-fluences is divided to obtain BRVE

(1) Geometric models (BGVE) the process of establishingthe BGVE is shown in Figure 6 A theoretical buildinginformation model (BIM) model is establishedduring the component design process (e drawingand processing of the components are performedaccording to this model Simultaneously the coor-dinates of key points are extracted from the BIMmodel to establish a theoretical finite elementanalysis model According to the analysis results thewheel-spoke cable truss is tensioned to the testmodel To consider the influence of time on thespatial coordinates of the structure 3D laser scan-ning technology is introduced which is also knownas ldquoreal scene replicationrdquo technology [23] Afterbeing built the test model is scanned using a 3D laserscanner to obtain the actual measured model of thestructure(e point cloud data includemany outliersdue to inherent machine errors human factors andexternal environment It is necessary to furtherdenoise the point cloud data (e processed data arethen imported into the BIM software and the keypoints of the cable truss are extracted (e modifiedBIM model is established by correcting the coordi-nates of the theoretical BIM model as shown inFigure 7 (e key node coordinates are extractedfrom the modified BIM model to modify the theo-retical finite element analysis model Finally themodified finite element analysis model consideringthe time dimension is obtained to ensure the ac-curacy of the simulation analysis

(2) Physical models (BPVE) the process of establishingthe BPVE of the wheel-spoke cable truss is shown inFigure 8 (e multicondition static test is performed

to study the static performance of the wheel-spokecable truss In this study the static load of thestructure is investigated by gravity loading (especific method of force application is loadingsandbags According to the ldquoCode for Design ofBuilding Structural Loads (GB50009-2012)rdquo the liveload is 05 kNm2 (e area load is converted into theequivalent node load in which the node load on theinner strut is 154N while that on the middle andouter struts is 476N and 714N respectively (e

Structuralentity(BPE)

Relateddata

(DD2)

SVM model(OA)

Training test

Structuralsafety

assessment(Ss)

Visu

aliz

atio

n of

resu

lts

Webpage

Predict

secu

rity l

evel (O

A)

Datavisualization

Real-timedata

(DD1)

Upd

ate

exte

nsio

n

Test

calib

ratio

n

Virtualmodel(BVE)

Data co

llecti

on (C

N2)

Dyn

amic

sync

hron

izat

ion

(CN

)

Figure 5 Safety evaluation system framework

TheoreticalBIM model

Coordinatedof key points Theoretical

finite elementanalysis model

Wheel-spoke cable trusstest model

3D la

ser

scan

ner

Actual measuredmodel of the

structure

Import p

oint cloud

data in

to the B

IM so

ftware

Modified BIMmodel

Modified finiteelement

analysis model

Figure 6 Geometric model building process


simulated and experimental values of the cable forceunder self-weight load are shown in Table 1 Fromthe data in the table the error between the simulatedand experimental values is small therefore BPVE canbe used effectively for the safety assessment of thestructure

(3) Behavior models (BBVE) when the wheel-spokecable truss structure is in use the cable force variesowing to temperature change material corrosioncomplex loads and loss of prestress BBVE depicts

the real-time changes in the cable force of thestructure under the action of different factors andprovides dynamic synchronization with thephysical structure Tables 2ndash4 show the changes ofthe upper radial cable force when the structuretemperature changes and the lower ring cablerelaxes and has a length error Furthermore thethree influencing factorsmdashtemperature changecable relaxation and rod errormdashare used as themain evaluation indexes for the subsequenttraining of the SVM model

(4) Rule models (BRVE) BRVE refers to the classificationof the safety level of the structure based on relevantcodes and expert experience According to the

Outer ringbeamsUpper ring

cablesOuter struts

Middle struts

Upper radialcables

Lower ringcablesLower radial

cables

Inner struts

12

3

4

5

67

8

9

10

Figure 7 (e BIM model of structure

Experimentalmodel

3D model

AN

SYS

Static analysis

Dead load12 Dead load + quarter span 14live load12 Dead load + half span 14live load12 Dead load + three-quarter span14 live load12 Dead load + full-span 14 liveload

(i)(ii)

(iii)

(iv)

(v)

Figure 8 Physical model building process

Table 1 Comparison of cable force value

Component number Simulated value Test value Error ()Upper radial cable 1 4695 4726 minus1Upper radial cable 3 5629 5821 minus3Upper radial cable 5 5017 5126 minus2Upper radial cable 7 5699 5592 2Upper radial cable 9 5074 5034 1Lower radial cable 1 5447 5545 minus2Lower radial cable 3 6353 6375 0Lower radial cable 5 5283 5170 2Lower radial cable 7 5966 5722 4Lower radial cable 9 5623 5463 3Upper ring cable 8580 8537 1Lower ring cable 9037 8846 2


content involved in the safety assessment indexsystem of the wheel-spoke cable truss the assessmentlevels are divided into four categories (e safetylevels from high to low are a b c and d In this studya cable is used as an example and the quantitativeevaluation criteria of the cable force are shown inTable 5

34 TwinData (DD)Collection An important way to use thedigital twin model to evaluate the safety risk of the in-servicewheel-spoke cable truss is to store the collected data analyzethe reliability of the virtual structure and then implementthe corresponding remedial measures In this study thecable force of the structure is recorded using a pressuretransducer after applying a column tension (e differentrods of the wheel-spoke cable truss perform differentfunctions therefore the collection methods of DD shouldnot be identical For the cable force a total of 12 monitoringpoints are present 2 monitoring points are arranged for eachof the upper and lower ring cables and 10 monitoring pointsfor 1 3 5 7 and 9 of the upper and lower radial cables (elocation of the monitoring points is shown in Figure 9

35 Application Based on SVM Algorithm Security LevelPrediction In this study the change in the cable force of thewheel-spoke cable truss is used as an example First thedigital twin multidimensional model of the structure is built(e SVM evaluation model is then established using thesafety evaluation method based on the digital twin and

support vector machines described in Section 2 Finally theaccuracy of the model is tested

(e main factors that affect the change in the structuralcable force include temperature change prestress losses androd length errors In this study as SVM input 200 sets ofsample data of the three aforementioned factors are selectedfrom the experimental data (e cable force safety assess-ment level described in Section 32 is used as the expectedoutput to train the model A total of 20 groups of evaluationvalues and cable force evaluation levels are selected as thetest samples to establish the SVM safety evaluation model(e test results are shown in Table 6 It shows that comparedwith the actual safety level the accuracy of the SVMmodel is85 In the sample with error the corresponding safety levelinterval is no more than one safety level (erefore the SVMevaluation model can accurately predict the structural safetylevel of the wheel-spoke cable in service (e input and

Table 2 Upper radial cable force (N)

Temperature change 1 3 5 7 9minus55 6118 6102 6081 6125 6135minus25 4956 4943 4930 4961 49680 3996 3986 3978 3999 400525 3052 3046 3041 3054 305855 1962 1958 1956 1963 1965

Table 5 Forced evaluation standards

Structuretype Component category

Ratio of measured value todesign

a b c d

CableImportant secondary

components ge100 ge093 ge090 lt085

General components ge100 ge091 ge086 lt081


Degree of relaxation 1 3 5 7 90 3996 3986 3978 3999 400510 3965 3956 3948 3969 397440 3875 3866 3858 3878 388370 3784 3776 3768 3787 3792


Length error 1 3 5 7 91400 3387 3368 3351 3385 33961700 3630 3616 3602 3630 363911000 3730 3717 3705 3731 37380 3996 3986 3978 3999 4005minus11000 4210 4200 4195 4212 4215minus1700 4310 4306 4303 4317 4319minus1400 4570 4573 4574 4583 4583

1

2

3

4

56

7

8

9

10

Figure 9 Cable monitoring point

Table 6 Comparison of actual and predicted structural risk levels

Inspectionsamplenumber

Actualrisklevel

Predictedrisk level


Actualrisklevel

Predictedrisk level

1 a a 11 c c2 alowast blowast 12 b b3 b b 13 a a4 b b 14 a a5 c c 15 blowast alowast6 blowast clowast 16 a a7 a a 17 b b8 a a 18 c c9 b b 19 b b10 a a 20 a a


output values of the trained SVM evaluation model areavailable for users to view in real time on the website

4 Conclusion

(is study investigates the safety assessment method ofprestressed steel structures in service based on digital twinsBased on the structural safety assessment framework adigital twin multidimensional model of the structure isestablished (e SVM algorithm is used to predict thestructural safety level Finally the risk assessment of thestructure is performed Taking a wheel-spoke cable truss asan example the geometric physical behavior and rulemodels are built A structural safety risk prediction andevaluation system is constructed based on the SVM theoryand related data (e advantages of using digital twintechnology in the safety risk assessment of prestressed steelstructures can be summarized as follows

(1) (e digital twin can realize the self-awareness self-prediction and self-evaluation of the structure thuscreating a more intelligent and safer structure whileoperational

(2) (e virtual model of the structure can be dynamicallysynchronized with the physical structure on the webpage and the relevant data and security levels of thestructure can also be viewed this makes the entireevaluation process more intuitive and concise

(3) (e digital twin-dimensional model integrates theentity structure physical structure relevant dataevaluation algorithm and evaluation service whichis conducive to the rapid evaluation prediction andmaintenance of the structure and provides a newmethod for the safety evaluation of the structure

5 Discussion

Digital twin technology has good research value in the safetyassessment of prestressed steel structures however the re-search in this study is only a preliminary exploration ofdigital twin technology in structural risk assessment (eresearch object is the prestressed steel structure during theservice period only and the study omitted to consider thesafety risk in the construction process(e proposed methodhas not yet considered the subsequent maintenance of thestructure

Data Availability

(e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

(e authors declare that they have no conflicts of interest

Acknowledgments

(e financial support for this study was provided by theBeijing Municipal Natural Science Foundation (8202001)

and the National Key RampD Program for the 13th-Five-YearPlan of China (2018YFF0300300)

References

[1] F Biondini and D M Frangopol ldquoLife-cycle performance ofdeteriorating structural systems under uncertainty reviewrdquoJournal of Structural Engineering vol 142 no 9 2016

[2] S Qiang X Zhou and M Gu ldquoResearch on reliability of steelroof structures subjected to snow loads at representative sitesin Chinardquo Cold Regions Science and Technology vol 150pp 62ndash69 2018

[3] G-Q Li and J-J Li ldquoA semi-analytical simulation method forreliability assessments of structural systemsrdquo Reliability En-gineering amp System Safety vol 78 no 3 pp 275ndash281 2002

[4] J Jiang S P Chiew C K Lee and P L Y Tiong ldquoA nu-merical study on residual stress of high strength steel boxcolumnrdquo Journal of Constructional Steel Research vol 128pp 440ndash450 2017

[5] J Jiang S P Chiew C K Lee and P L Y Tiong ldquoAn ex-perimental study on residual stresses of high strength steel boxcolumnsrdquo Journal of Constructional Steel Research vol 130pp 12ndash21 2017

[6] L Deng M Zhong L Liao L Peng and S Lai ldquoResearch onsafety management application of dangerous sources in en-gineering construction based on BIM technologyrdquo Advancesin Civil Engineering vol 2019 pp 1ndash10 2019

[7] E J Tuegel A R Ingraffea T G Eason andS M Spottswood ldquoReengineering aircraft structural lifeprediction using a digital twinrdquo International Journal ofAerospace Engineering vol 2011 pp 1ndash14 2011

[8] E M Kraft ldquo(e US air force digital threaddigital twinndashlifecycle integration and use of computational and experimentalknowledgerdquo Procedia Computer Science vol 114 pp 47ndash562017

[9] R D Lu and I Brilakis ldquoDigital twinning of existing rein-forced concrete bridges from labelled point clustersrdquo Auto-mation in Construction vol 105 Article ID 102837 2019

[10] C-S Shim N-S Dang S Lon and C-H Jeon ldquoDevelopmentof a bridge maintenance system for prestressed concretebridges using 3D digital twin modelrdquo Structure and Infra-structure Engineering vol 15 no 10 pp 1319ndash1332 2019

[11] W Tian H M Li R Q Yan and Y X Hu ldquoSafety riskassessment of highway special maintenance project based onBP neural networkrdquo Advanced Materials Research vol 373pp 3175ndash3179 2011

[12] G Macdonald ldquoRisk perception and construction safetyrdquoCivil Engineering vol 6 no 159 pp 51ndash56 2006

[13] H-S Lee H Kim M Park E Ai Lin Teo and K-P LeeldquoConstruction risk assessment using site influence factorsrdquoJournal of Computing in Civil Engineering vol 26 no 3pp 319ndash330 2012

[14] F Tao W R Liu and M Zhang ldquoFive-dimension digital twinmodel and its ten applicationsrdquo Computer integratedmanufacturing systems vol 25 no 1 pp 1ndash18 2019

[15] Z Liu A S Zhang and W S Wang ldquoDynamic fire evacu-ation guidance method for winter olympic venues based ondigital twin-driven modelrdquo Journal of Tongji Uni-versity(Natural Science) vol 48 no 7 pp 962ndash971 2020

[16] Q Yang Y X Jiang and A D Xu ldquoA model divides themobile security level based on SVMrdquo in Proceedings of the2017 IEEE Conference on Communications and Network Se-curity (CNS) pp 370-371 Las Vegas NV USA October 2017


[17] H F Wang and D J Hu ldquoComparison of SVM and LS-SVMfor regressionrdquo in Proceedings of the 2005 InternationalConference on Neural Networks and Brain vol 1 pp 279ndash283Beijing China October 2005

[18] C-C Chang and C-J Lin ldquoLIBSVM A library for supportvacteo machinesrdquo ACM Transactions on Intelligent Systemsand Technology vol 2 no 3 pp 1ndash27 2011

[19] S Soleimani O Bozorg-Haddad and M Saadatpour ldquoOp-timal selective withdrawal rules using a coupled data miningmodel and genetic algorithmrdquo Journal of Water ResourcesPlanning and Management vol 142 no 12 Article ID4016064 2016

[20] C-L Huang and C-J Wang ldquoA GA-based feature selectionand parameters optimization for support vector machinesrdquoExpert Systems with Applications vol 31 no 2 pp 231ndash2402006

[21] Z S Liu Z B Han and J He ldquoSensitive test on relaxation ofcable and reliability assessment of spoke cable-truss struc-turerdquo Journal of Tongji University (Natural Science) vol 47no 7 pp 946ndash956 2019

[22] J Jiang W Bao and Z Y Peng ldquoCreep property of TMCPhigh strength steel Q690CFD at elevated temperaturesrdquoJournal of Materials in Civil Engineering vol 32 no 2 ArticleID 4019364 2020

[23] D Zhang T Huang and J C Song ldquoCAD model recon-struction using 3D laser scanningrdquo Applied Mechanics andMaterials vol 78 pp 3485ndash3488 2011


With the advent of the ldquoIndustry 40rdquo era digital twintechnology has become a feasible and effective method toachieve intelligent construction (e concept of digital twinwas first proposed by Professor Grieves (e digital twinsystem includes physical space virtual space and mutualdata interaction In 2012 NASA applied digital twins toaircraft maintenance and quality monitoring Concurrentlyit provided a clear definition of digital twin ldquodigital twinrdquo is amultidisciplinary multiphysical multiscale and multidi-mensional simulation process that makes full use of theentity model perceptron and real-time data It mirrors thephysical entity in virtual digital space reflecting the entire lifecycle process of the physical entityrsquos behavior state oractivity [7 8] In recent years digital twin technology hasdeveloped rapidly and has been increasingly used in thedesign construction operation and maintenance ofbuildings Lu and Brilakis realized digital twin automation ofreinforced concrete bridges using a slicing-based objectfitting method [9] Shim et al proposed a new generationbridge maintenance system based on the concept of thedigital twin model [10] In general digital twin technologycan realize the interactive fusion of the physical space andinformation space of the prestressed steel structure in ser-vice Users can collect real-time information of the physicalstructure through the virtual structure and use neuralnetworks to extract the correlation features of the data andthe safety assessment is then conducted on this basisCorresponding maintenance measures can be applied to thephysical structure based on the assessment results of thevirtual structure to ensure the safe service of the buildingwhich is in line with the basic concept of analysis evaluationand application of the data during the safety assessmentprocess of the prestressed steel structure during service(erefore digital twin technology provides new method forthe safety assessment of prestressed steel structures

2 Safety Assessment Method Based onDigital Twins

21 Framework Structural Safety Assessment According tothe structural characteristics and safety requirements ofprestressed steel structures guidelines for the structuralsafety assessment are proposed as shown in Figure 1 Firstlya virtual model of the prestressed steel structure is builtaccording to the physical building and sensors are arrangedon the physical building to collect the cable force dis-placement and other data during the service of the structure(e data are then transmitted to the master computerthrough the serial communications in order to realize thecollection and interaction of the data related to the serviceprocess of the prestressed steel structure Before using thestructure a sensor is installed on it to collect and store thestructural data (e data of structural displacement or cableforce change in the physical layer can be mapped to thevirtual layer in real time Structure-related data are stored inthe twin data service system Based on these data thestructure is evaluated for safety In addition the measureddata digital model and evaluation results of the structurecan be viewed in real time on the web page thus visual

management is achieved(is study focuses on the structuralsafety risk assessment method based on the digital twinmultidimensional model under the above framework

22 Digital Twin Multidimensional Evaluation Model(e definition of safety risk assessment is from a riskmanagement perspective using scientific methods to sys-tematically analyze the threats and vulnerabilities thenassess the degree of damage if a damage should occur andfinally propose targeted protective countermeasures andcorrective measures against threats [11ndash13] Subsequentlysafety risk assessment is conducted based on digital twins Incombination with the safety risk assessment framework andthe five-dimensional model of digital twins [14 15] amultidimensional model for the safety risk assessment isproposed in this study as shown in the following equation

MSDT BPEBVE SSDDOACN( 1113857 (1)

where MSDT represents the multidimensional model ofsecurity risk assessment BPE represents the building physicalstructure BVE represents the building virtual structure SS

represents the security risk assessment service for structuresDD represents the data related to security risk assessmentOA represents a risk assessment algorithm for processingdata and CN represents the connection between the variouscomponents (e security risk assessment process based ondigital twins is shown in Figure 2

In the aforementioned evaluation model BVE is com-posed of the geometric model (BGVE) physical model (BPVE)behavior model (BBVE) and rule model (BRVE) BGVE isestablished using three-dimensional (3D) laser scanningtechnology for 3D modelingBGVE needs to be characterizedby the dimensions installation position relationship andother information for each member of BPE Finite elementanalysis software is used to simulate and analyze the 3Dmodel and build BPVE BBVE describes the real-time variationof the structure under the action of different factors BRVErefers to the classification of the safety level of the structurebased on relevant norms and expert experience DD includesthe measured data (DD1) obtained by arranging the sensorsand historical related data (DD2) OA represents the pro-cessing of DD from which an algorithm to predict thestructure security level is obtained

23 Support Vector Machine Model A support vector ma-chine (SVM) is a small-sample training method which canproduce better training results with fewer samples of dataand can thus eliminate the requirement of collecting datasamples of prestressed steel structures [16] (e SVM modelconsists of training and recognition stages In the first stageinput and output samples are selected according to the actualproblem and the SVM is trained using existing data toobtain the maximum margin hyperplane In the secondstage the input data are from unknown samples and outputdata are those recognized by the SVM [17]

(e SVM model is built using the LIBSVM softwarepackage [18] developed by Professor Lin Chin-Jen of Taiwan
















Datadriving


Servicedata

Inte

ract

ive

feed

back

Inte

ract

ive

feed

back

Buildingshapedata

Buildingmonitoring

data

Historicaldata

Serialcommuni-

cation

Datamapping

Phys

ical

bui

ldin

g

Virt

ual b

uild

ing

Sensor






3 Case Study






(Ss)


algorithm (OA)

Secu

rity r

isk as

sessm

ent (

CN5)




Sensor (CN 2)






(BVE)










Start


Trainingsamples

Testingsample



combination


SVM predictionmodel

Ss


End

OA

DD2 DD1

Collectingdata


Figure 4 Test model







Relateddata

(DD2)

SVM model(OA)

Training test

Structuralsafety

assessment(Ss)

Visu

aliz

atio

n of

resu

lts

Webpage

Predict

secu

rity l

evel (O

A)

Datavisualization

Real-timedata

(DD1)

Upd

ate

exte

nsio

n

Test

calib

ratio

n

Virtualmodel(BVE)

Data co

llecti

on (C

N2)

Dyn

amic

sync

hron

izat

ion

(CN

)






3D la

ser

scan

ner


structure

Import p

oint cloud

data in

to the B

IM so

ftware

Modified BIMmodel


analysis model








cablesOuter struts

Middle struts

Upper radialcables


cables

Inner struts

12

3

4

5

67

8

9

10


Experimentalmodel

3D model

AN

SYS

Static analysis


(i)(ii)

(iii)

(iv)

(v)















a b c d








1

2

3

4

56

7

8

9

10




Actualrisklevel

Predictedrisk level


Actualrisklevel

Predictedrisk level




4 Conclusion





5 Discussion


Data Availability




Acknowledgments



References








































Datadriving


Servicedata

Inte

ract

ive

feed

back

Inte

ract

ive

feed

back

Buildingshapedata

Buildingmonitoring

data

Historicaldata

Serialcommuni-

cation

Datamapping

Phys

ical

bui

ldin

g

Virt

ual b

uild

ing

Sensor






3 Case Study






(Ss)


algorithm (OA)

Secu

rity r

isk as

sessm

ent (

CN5)




Sensor (CN 2)






(BVE)










Start


Trainingsamples

Testingsample



combination


SVM predictionmodel

Ss


End

OA

DD2 DD1

Collectingdata


Figure 4 Test model







Relateddata

(DD2)

SVM model(OA)

Training test

Structuralsafety

assessment(Ss)

Visu

aliz

atio

n of

resu

lts

Webpage

Predict

secu

rity l

evel (O

A)

Datavisualization

Real-timedata

(DD1)

Upd

ate

exte

nsio

n

Test

calib

ratio

n

Virtualmodel(BVE)

Data co

llecti

on (C

N2)

Dyn

amic

sync

hron

izat

ion

(CN

)






3D la

ser

scan

ner


structure

Import p

oint cloud

data in

to the B

IM so

ftware

Modified BIMmodel


analysis model








cablesOuter struts

Middle struts

Upper radialcables


cables

Inner struts

12

3

4

5

67

8

9

10


Experimentalmodel

3D model

AN

SYS

Static analysis


(i)(ii)

(iii)

(iv)

(v)















a b c d








1

2

3

4

56

7

8

9

10




Actualrisklevel

Predictedrisk level


Actualrisklevel

Predictedrisk level




4 Conclusion





5 Discussion


Data Availability




Acknowledgments



References





























3 Case Study






(Ss)


algorithm (OA)

Secu

rity r

isk as

sessm

ent (

CN5)




Sensor (CN 2)






(BVE)










Start


Trainingsamples

Testingsample



combination


SVM predictionmodel

Ss


End

OA

DD2 DD1

Collectingdata


Figure 4 Test model







Relateddata

(DD2)

SVM model(OA)

Training test

Structuralsafety

assessment(Ss)

Visu

aliz

atio

n of

resu

lts

Webpage

Predict

secu

rity l

evel (O

A)

Datavisualization

Real-timedata

(DD1)

Upd

ate

exte

nsio

n

Test

calib

ratio

n

Virtualmodel(BVE)

Data co

llecti

on (C

N2)

Dyn

amic

sync

hron

izat

ion

(CN

)






3D la

ser

scan

ner


structure

Import p

oint cloud

data in

to the B

IM so

ftware

Modified BIMmodel


analysis model








cablesOuter struts

Middle struts

Upper radialcables


cables

Inner struts

12

3

4

5

67

8

9

10


Experimentalmodel

3D model

AN

SYS

Static analysis


(i)(ii)

(iii)

(iv)

(v)















a b c d








1

2

3

4

56

7

8

9

10




Actualrisklevel

Predictedrisk level


Actualrisklevel

Predictedrisk level




4 Conclusion





5 Discussion


Data Availability




Acknowledgments



References

































Start


Trainingsamples

Testingsample



combination


SVM predictionmodel

Ss


End

OA

DD2 DD1

Collectingdata


Figure 4 Test model







Relateddata

(DD2)

SVM model(OA)

Training test

Structuralsafety

assessment(Ss)

Visu

aliz

atio

n of

resu

lts

Webpage

Predict

secu

rity l

evel (O

A)

Datavisualization

Real-timedata

(DD1)

Upd

ate

exte

nsio

n

Test

calib

ratio

n

Virtualmodel(BVE)

Data co

llecti

on (C

N2)

Dyn

amic

sync

hron

izat

ion

(CN

)






3D la

ser

scan

ner


structure

Import p

oint cloud

data in

to the B

IM so

ftware

Modified BIMmodel


analysis model








cablesOuter struts

Middle struts

Upper radialcables


cables

Inner struts

12

3

4

5

67

8

9

10


Experimentalmodel

3D model

AN

SYS

Static analysis


(i)(ii)

(iii)

(iv)

(v)















a b c d








1

2

3

4

56

7

8

9

10




Actualrisklevel

Predictedrisk level


Actualrisklevel

Predictedrisk level




4 Conclusion





5 Discussion


Data Availability




Acknowledgments



References































Relateddata

(DD2)

SVM model(OA)

Training test

Structuralsafety

assessment(Ss)

Visu

aliz

atio

n of

resu

lts

Webpage

Predict

secu

rity l

evel (O

A)

Datavisualization

Real-timedata

(DD1)

Upd

ate

exte

nsio

n

Test

calib

ratio

n

Virtualmodel(BVE)

Data co

llecti

on (C

N2)

Dyn

amic

sync

hron

izat

ion

(CN

)






3D la

ser

scan

ner


structure

Import p

oint cloud

data in

to the B

IM so

ftware

Modified BIMmodel


analysis model








cablesOuter struts

Middle struts

Upper radialcables


cables

Inner struts

12

3

4

5

67

8

9

10


Experimentalmodel

3D model

AN

SYS

Static analysis


(i)(ii)

(iii)

(iv)

(v)















a b c d








1

2

3

4

56

7

8

9

10




Actualrisklevel

Predictedrisk level


Actualrisklevel

Predictedrisk level




4 Conclusion





5 Discussion


Data Availability




Acknowledgments



References































cablesOuter struts

Middle struts

Upper radialcables


cables

Inner struts

12

3

4

5

67

8

9

10


Experimentalmodel

3D model

AN

SYS

Static analysis


(i)(ii)

(iii)

(iv)

(v)















a b c d








1

2

3

4

56

7

8

9

10




Actualrisklevel

Predictedrisk level


Actualrisklevel

Predictedrisk level




4 Conclusion





5 Discussion


Data Availability




Acknowledgments



References




































a b c d








1

2

3

4

56

7

8

9

10




Actualrisklevel

Predictedrisk level


Actualrisklevel

Predictedrisk level




4 Conclusion





5 Discussion


Data Availability




Acknowledgments



References



























4 Conclusion





5 Discussion


Data Availability




Acknowledgments



References


























digitaltwin-basedsafetyevaluationofprestressed steelstructure

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