Structural health monitoring (SHM) has proven effective in early damage detection and in facilitating structural condition assessment. Conventional SHM systems feature comprehensive sensing for large infrastructures, such as long-span bridges and high-rise buildings. However, the high cost of these systems limits their feasibility for small to medium-sized structures. Inspired by lightweight design principle from the automotive industry, lightweight monitoring offers a promising complementary approach to conventional SHM. This paper provides an overview of the concepts, techniques, methodologies, and applications of lightweight monitoring for SHM. For sensing devices, non-contact, wireless, and robotic sensing are explored for their potential to enable fast and convenient data acquisition. For monitoring scheme, the concept of targeted sensing is introduced as an alternative to comprehensive sensing, focusing on the monitoring of critical structural behaviors using purpose-built devices. Additionally, the development of cloud-edge-end collaborative framework enables optimized deployment of computing resources for SHM. With regard to data analysis, the paper examines methods for data preprocessing, data fusion, and structural evaluation, with particular emphasis on artificial intelligence-driven approaches that enable fast and automatic analysis. Finally, this paper offers insights into the future of lightweight monitoring in SHM. By organizing and clearly identifying SHM techniques suitable for lightweight monitoring practices, this review serves as a practical guide for advancing the adoption of lightweight SHM and provides a foundation for further innovation in this field.
AbnerMWongPK-YChengJC (2022) Battery lifespan enhancement strategies for edge computing-enabled wireless Bluetooth mesh sensor network for structural health monitoring. Automation in Construction140: 104355.
2.
Amezquita-SanchezJPAdeliH (2016) Signal processing techniques for vibration-based health monitoring of smart structures. Archives of computational methods in engineering23: 1–15.
3.
BaoYLiHOuJ (2014) Emerging data technology in structural health monitoring: compressive sensing technology. Journal of Civil Structural Health Monitoring4: 77–90.
4.
BayHEssATuytelaarsT, et al. (2008) Speeded-up robust features (SURF). Computer Vision and Image Understanding110(3): 346–359.
5.
BeaucheminSSBarronJL (1995) The computation of optical flow. ACM Computing Surveys27(3): 433–466.
6.
BuscaGCigadaAMazzoleniP, et al. (2014) Vibration monitoring of multiple bridge points by means of a unique vision-based measuring system. Experimental Mechanics54: 255–271.
7.
CaoSLiJ (2017) A survey on ambient energy sources and harvesting methods for structural health monitoring applications. Advances in Mechanical Engineering9(4): 1687814017696210.
8.
ÇelebiM (2002) Seismic Instrumentation of Buildings (with Emphasis on Federal Buildings). Reportno. Report Number|, Date. Place Published|: Institution|.
9.
ChanBGuanHJoJ, et al. (2015) Towards UAV-based bridge inspection systems: a review and an application perspective. Structural Monitoring and Maintenance2(3): 283–300.
10.
ChangCXiaoX (2010) Three-dimensional structural translation and rotation measurement using monocular videogrammetry. Journal of Engineering Mechanics136(7): 840–848.
11.
ChenS-ERiceCBoyleC, et al. (2011) Small-format aerial photography for highway-bridge monitoring. Journal of Performance of Constructed Facilities25(2): 105–112.
12.
ChenJGWadhwaNChaY-J, et al. (2015) Modal identification of simple structures with high-speed video using motion magnification. Journal of Sound and Vibration345: 58–71.
13.
ChenZLiHBaoY, et al. (2016) Identification of spatio-temporal distribution of vehicle loads on long-span bridges using computer vision technology. Structural Control and Health Monitoring23(3): 517–534.
14.
ChenJGAdamsTMSunH, et al. (2018) Camera-based vibration measurement of the world war I memorial bridge in Portsmouth, New Hampshire. Journal of Structural Engineering144(11): 04018207.
15.
ChenZ-WRuanX-ZLiuK-M, et al. (2022) Fully automated natural frequency identification based on deep-learning-enhanced computer vision and power spectral density transmissibility. Advances in Structural Engineering25(13): 2722–2737.
16.
ChoKHJinYHKimHM, et al. (2016) Multifunctional robotic crawler for inspection of suspension bridge hanger cables: mechanism design and performance validation. IEEE/ASME transactions on Mechatronics22(1): 236–246.
17.
CuiSHoangTMechitovK, et al. (2025) Adaptive edge intelligence for rapid structural condition assessment using a wireless smart sensor network. Engineering Structures326: 119520.
18.
DanDYuXYanX, et al. (2020) Monitoring and evaluation of overturning resistance of box girder bridges based on time-varying reliability analysis. Journal of Performance of Constructed Facilities34(1): 04019101.
19.
DessiDCamerlengoG (2015) Damage identification techniques via modal curvature analysis: overview and comparison. Mechanical Systems and Signal Processing52: 181–205.
20.
Di NuzzoFBrunelliDPolonelliT, et al. (2021) Structural health monitoring system with narrowband IoT and MEMS sensors. IEEE Sensors Journal21(14): 16371–16380.
21.
DongC (2019) Investigation of Computer Vision Concepts and Methods for Structural Health Monitoring and Identification Applications. University of Central Florida.
22.
DongCZCatbasFN (2019) A non-target structural displacement measurement method using advanced feature matching strategy. Advances in Structural Engineering22(16): 3461–3472.
23.
DongC-ZCatbasFN (2021) A review of computer vision–based structural health monitoring at local and global levels. Structural Health Monitoring20(2): 692–743.
24.
DongXChenSZhuD, et al. (2014) High-speed heterogeneous data acquisition using Martlet-A next-generation wireless sensing node. In: In: The 6th World Conference on Structural Control and Monitoring (6WCSCM). Spain: Barcelona.
25.
DongC-ZCelikOCatbasFN (2019) Marker-free monitoring of the grandstand structures and modal identification using computer vision methods. Structural Health Monitoring18(5-6): 1491–1509.
26.
DongCBasSDebeesM, et al. (2020) Bridge load testing for identifying live load distribution, load rating, serviceability and dynamic response. Frontiers in Built Environment6: 46.
27.
ErdenebatDWaldmannD (2020) Application of the DAD method for damage localisation on an existing bridge structure using close-range UAV photogrammetry. Engineering Structures218: 110727.
28.
EreizSDuvnjakIJiménez-AlonsoJF (2022) Review of finite element model updating methods for structural applications. In: Structures. Elsevier, 684–723.
29.
EschmannCWundsamT (2017) Web-based georeferenced 3D inspection and monitoring of bridges with unmanned aircraft systems. Journal of Surveying Engineering143(3): 04017003.
30.
EsfandiariASanayeiMBakhtiari-NejadF, et al. (2010) Finite element model updating using frequency response function of incomplete strain data. AIAA Journal48(7): 1420–1433.
31.
FarahaniRVPenumaduD (2016) Damage identification of a full-scale five-girder bridge using time-series analysis of vibration data. Engineering Structures115: 129–139.
32.
FarrarCRWordenK (2007) An introduction to structural health monitoring. Philosophical Transactions of the Royal Society A: Mathematical, Physical & Engineering Sciences365(1851): 303–315.
33.
FarrarCRDoeblingSWNixDA (2001) Vibration–based structural damage identification. Philosophical Transactions of the Royal Society of London,Series A: Mathematical, Physical and Engineering Sciences359(1778): 131–149.
34.
FayyadTMTaylorSFengK, et al. (2025) A scientometric analysis of drone-based structural health monitoring and new technologies. Advances in Structural Engineering28(1): 122–144.
35.
FengDFengMQ (2018) Computer vision for SHM of civil infrastructure: from dynamic response measurement to damage detection–A review. Engineering Structures156: 105–117.
36.
FengDFengMQOzerE, et al. (2015a) A vision-based sensor for noncontact structural displacement measurement. Sensors15(7): 16557–16575.
37.
FengDMFengMQOzerE, et al. (2015b) A vision-based sensor for noncontact structural displacement measurement. Sensors15(7): 16557–16575.
38.
FischlerMABollesRC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM24(6): 381–395.
39.
FleetDJJepsonAD (1990) Computation of component image velocity from local phase information. International Journal of Computer Vision5: 77–104.
40.
FuHKhodaeiZSAliabadiMF (2018) An event-triggered energy-efficient wireless structural health monitoring system for impact detection in composite airframes. IEEE Internet of Things Journal6(1): 1183–1192.
41.
GentileCBernardiniG (2010) An interferometric radar for non-contact measurement of deflections on civil engineering structures: laboratory and full-scale tests. Structure and Infrastructure Engineering6(5): 521–534.
42.
GuGZouJZhaoR, et al. (2018) Soft wall-climbing robots. Science Robotics3(25): eaat2874.
43.
HanY (2021) Reliable template matching for image detection in vision sensor systems. Sensors21(24): 8176.
44.
HanJ-GRenW-XSunZ-S (2005) Wavelet packet based damage identification of beam structures. International Journal of Solids and Structures42(26): 6610–6627.
45.
HoskereVParkJ-WYoonH, et al. (2019) Vision-based modal survey of civil infrastructure using unmanned aerial vehicles. Journal of Structural Engineering145(7): 04019062.
46.
HuHWangJDongC-Z, et al. (2023) A hybrid method for damage detection and condition assessment of hinge joints in hollow slab bridges using physical models and vision-based measurements. Mechanical Systems and Signal Processing183: 109631.
47.
HuangNEShenZLongSR, et al. (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences454(1971): 903-995.
48.
HuangH-BYiT-HLiH-N, et al. (2022) Sparse Bayesian identification of temperature-displacement model for performance assessment and early warning of bridge bearings. Journal of Structural Engineering148(6): 04022052.
49.
IerimontiLCavalagliNVenanziI, et al. (2023) A Bayesian-based inspection-monitoring data fusion approach for historical buildings and its post-earthquake application to a monumental masonry palace. Bulletin of Earthquake Engineering21(2): 1139–1172.
50.
IyerSVelmuruganTGandomiAH, et al. (2021) Structural health monitoring of railway tracks using IoT-based multi-robot system. Neural Computing & Applications33: 5897–5915.
51.
JangSSpencerBFRiceJA, et al. (2011) Full-scale experimental validation of high-fidelity wireless measurement on a Historic truss bridge. Advances in Structural Engineering14(1): 93–101.
52.
JayawardhanaMZhuXQLiyanapathiranaR, et al. (2015) Statistical damage sensitive feature for structural damage detection using AR model coefficients. Advances in Structural Engineering18(10): 1551–1562.
53.
JiaMCaoJLiangW (2015) Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks. IEEE Transactions on Cloud Computing5(4): 725–737.
54.
JiangSZhangJ (2020) Real-time crack assessment using deep neural networks with wall-climbing unmanned aerial system. Computer-Aided Civil and Infrastructure Engineering35(6): 549–564.
55.
KaulLZlotRBosseM (2016) Continuous-time three-dimensional mapping for micro aerial vehicles with a passively actuated rotating laser scanner. Journal of Field Robotics33(1): 103–132.
56.
KimSPakzadSCullerD, et al. (2007) Health monitoring of civil infrastructures using wireless sensor networks. In: Proceedings of the 6th International Conference on Information Processing in Sensor Networks, 254–263.
57.
KocerBBTjahjowidodoTPratamaM, et al. (2019) Inspection-while-flying: an autonomous contact-based nondestructive test using UAV-tools. Automation in Construction106: 102895.
58.
LeeJ-HChungS-HKimW-S (2017) Fog server deployment considering network topology and flow state in local area networks. In: 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN). IEEE, 652–657.
59.
LeiYZhangFWangJ (2025) Real-time estimation of bridge displacements under vehicle loads using physical mechanism-based attention-LSTM network with partial strain measurements. Advances in Structural Engineering28(1): 193–204.
60.
LiDWangY (2020) Thermally stable wireless patch antenna sensor for strain and crack sensing. Sensors20(14): 3835.
61.
LiDZhangGSuZ, et al. (2023) A wireless sensor with data-fusion algorithm for structural tilt measurement. Smart Structures and Systems31(3): 301.
62.
LiJWuQWengS (2024) Efficient calculation of higher order time history response derivatives by substructuring method. Advances in Structural Engineering27(5): 758–774.
63.
LiYZhaoZSunL, et al. (2025) A geometrical morphology-enhanced computer vision approach for structural health assessment. Structural Health Monitoring, 14759217241307575.
64.
LiaoBZangHChenM, et al. (2020) Soft rod-climbing robot inspired by winding locomotion of snake. Soft Robotics7(4): 500–511.
65.
LinYHyyppäJRosnellT, et al. (2013) Development of a UAV-MMS-collaborative aerial-to-ground remote sensing system–a preparatory field validation. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing6(4): 1893–1898.
66.
LiuXTongXDingK, et al. (2015) Measurement of long-term periodic and dynamic deflection of the long-span railway bridge using microwave interferometry. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing8(9): 4531–4538.
67.
LiuLZhuJSuY, et al. (2016) Improved Kalman Filter with Unknown Inputs based on Data Fusion of Partial Acceleration and Displacement Measurements. Smart Structures and Systems17(6): 903–915.
68.
LoweDG (2004) Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision60: 91–110.
69.
LuYTangJ (2018) On time-frequency domain feature extraction of wave signals for structural health monitoring. Measurement114: 51–59.
70.
LydonDLydonMTaylorS, et al. (2019) Development and field testing of a vision-based displacement system using a low cost wireless action camera. Mechanical Systems and Signal Processing121: 343–358.
71.
LynchJP (2002) Decentralization of Wireless Monitoring and Control Technologies for Smart Civil Structures. Stanford University.
72.
LynchJPLawKHKiremidjianAS, et al. (2002) Validation of a wireless modular monitoring system for structures. In: Smart Structures and Materials 2002: Smart Systems for Bridges, Structures, and Highways. SPIE, 124–135.
73.
MartínCGarridoDLlopisL, et al. (2022) Facilitating the monitoring and management of structural health in civil infrastructures with an Edge/Fog/Cloud architecture. Computer Standards & Interfaces81: 103600.
MorgenthalGHallermannN (2014) Quality assessment of unmanned aerial vehicle (UAV) based visual inspection of structures. Advances in Structural Engineering17(3): 289–302.
MurphyMPSittiM (2007) Waalbot: an agile small-scale wall-climbing robot utilizing dry elastomer adhesives. IEEE/ASME transactions on Mechatronics12(3): 330–338.
78.
NguyenKVPhamNVBDaoTTB (2022) Damage detection of cables in cable-stayed bridges using vibration data measured from climbing robot. Advances in Structural Engineering25(13): 2705–2721.
79.
NiY-QWangBKoJM (2000) Simulation Studies of Damage Location in Tsing Ma Bridge Deck. SPIE.
80.
NiFZhangJNooriMN (2020) Deep learning for data anomaly detection and data compression of a long-span suspension bridge. Computer-Aided Civil and Infrastructure Engineering35(7): 685–700.
81.
NisharARichardsSBreenD, et al. (2016) Thermal infrared imaging of geothermal environments and by an unmanned aerial vehicle (UAV): a case study of the Wairakei–Tauhara geothermal field, Taupo, New Zealand. Renewable Energy86: 1256–1264.
82.
OtsukiYNguyenSTLaHM, et al. (2023) Autonomous ultrasonic thickness measurement of steel bridge members using a climbing bicycle robot. Journal of Engineering Mechanics149(8): 04023051.
83.
O’HigginsCHesterDMcGetrickP, et al. (2023) Minimal information data-modelling (mid) and an easily implementable low-cost shm system for use on a short-span bridge. Sensors23(14): 6328.
84.
PanBTianLSongX (2016) Real-time, non-contact and targetless measurement of vertical deflection of bridges using off-axis digital image correlation. Ndt & E International79: 73–80.
85.
PaolucciRMuttilloMDi LuzioM, et al. (2020) Electronic sensory system for structural health monitoring applications. In: 2020 5th International Conference on Smart and Sustainable Technologies (SpliTech). IEEE, 1–5.
86.
PengZLiJHaoH (2023) Development and experimental verification of an IoT sensing system for drive-by bridge health monitoring. Engineering Structures293: 116705.
87.
Peter CardenEBrownjohnJMW (2008) ARMA modelled time-series classification for structural health monitoring of civil infrastructure. Mechanical Systems and Signal Processing22(2): 295–314.
88.
PfändlerPBodieKCrottaG, et al. (2024) Non-destructive corrosion inspection of reinforced concrete structures using an autonomous flying robot. Automation in Construction158: 105241.
89.
RibeiroDCalçadaRDelgadoR, et al. (2012) Finite element model updating of a bowstring-arch railway bridge based on experimental modal parameters. Engineering Structures40: 413–435.
90.
RibeiroDCalçadaRFerreiraJ, et al. (2014) Non-contact measurement of the dynamic displacement of railway bridges using an advanced video-based system. Engineering Structures75: 164–180.
91.
ShanYLiLXiaQ, et al. (2023) Temperature behavior of cable-stayed bridges. Part I—global 3D temperature distribution by integrating heat-transfer analysis and field monitoring data. Advances in Structural Engineering26(9): 1579–1599.
92.
ShresthaADangJNakajimaK, et al. (2020) Image processing–based real-time displacement monitoring methods using smart devices. Structural Control and Health Monitoring27(2): e2473.
93.
SigurdardottirDGlisicB (2013) Neutral axis as damage sensitive feature. Smart Materials and Structures22(7): 075030.
94.
SimS-HSpencerJBZhangM, et al. (2010) Automated decentralized modal analysis using smart sensors. Structural Control and Health Monitoring17(8): 872–894.
95.
SohnH (2007) Effects of environmental and operational variability on structural health monitoring. Philosophical Transactions of the Royal Society A: Mathematical, Physical & Engineering Sciences365(1851): 539–560.
96.
StraserEKiremidjianAMengT, et al. (1998) A modular, wireless network platform for monitoring structures. In: Proceedings-SPIE the International Society for Optical Engineering. Citeseer, 450–456.
97.
SuZWeiBZhangJ (2023) Feature-constrained real-time simultaneous monitoring of monocular vision odometry for bridge bearing displacement and rotation. Automation in Construction154: 105008.
98.
SunLShangZXiaY, et al. (2020) Review of bridge structural health monitoring aided by big data and artificial intelligence: from condition assessment to damage detection. Journal of Structural Engineering146(5): 04020073.
99.
TangZChenZBaoY, et al. (2019) Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring. Structural Control and Health Monitoring26(1): e2296.
100.
TianYZhangJYuS (2019) Vision-based structural scaling factor and flexibility identification through mobile impact testing. Mechanical Systems and Signal Processing122: 387–402.
101.
TianYZhangCJiangS, et al. (2021) Noncontact cable force estimation with unmanned aerial vehicle and computer vision. Computer-Aided Civil and Infrastructure Engineering36(1): 73–88.
102.
WadhwaNChenJGSellonJB, et al. (2017) Motion microscopy for visualizing and quantifying small motions. Proceedings of the National Academy of Sciences114(44): 11639–11644.
103.
WaldnerJFSadhuA (2024) A systematic literature review of unmanned underwater vehicle-based structural health monitoring technologies. Journal of Infrastructure Intelligence and Resilience: 100112.
104.
WangYLynchJPLawKH (2007a) Information driven wireless sensing and control for civil structures. In: Proceedings of the 1st World Forum on Smart Materials and Smart Structures Technology (SMSST’07). Citeseer.
105.
WangYLynchJPLawKH (2007b) A wireless structural health monitoring system with multithreaded sensing devices: design and validation. Structure and Infrastructure Engineering3(2): 103–120.
106.
WangYLanderPMyungH, et al. (2022) Robotic Sensing for Assessing and Monitoring Civil Infrastructures. Sensor Technologies for Civil Infrastructures. Elsevier, 587–615.
107.
WangZYiT-HYangD-H, et al. (2023) Early warning method of structural damage using localized frequency cointegration under changing environments. Journal of Structural Engineering149(2): 04022230.
108.
WangchukSSiringoringoDMFujinoY (2022) Modal analysis and tension estimation of stay cables using noncontact vision-based motion magnification method. Structural Control and Health Monitoring29(7): e2957.
109.
WengSZhuHXiaY, et al. (2020) A review on dynamic substructuring methods for model updating and damage detection of large-scale structures. Advances in Structural Engineering23(3): 584–600.
110.
WengYQuekS-TYeohJK-W (2025) Robust vision-based sub-pixel level displacement measurement using a complementary strategy. Mechanical Systems and Signal Processing223: 111898.
111.
WuJ-JShihS-FLiuP, et al. (2011) Optimizing server placement in distributed systems in the presence of competition. Journal of Parallel and Distributed Computing71(1): 62–76.
112.
WuYLiuWLiK (2017) A novel wireless acoustic emission sensor system for distributed wooden structural health monitoring. Int J Innov Comput Inf Control13(4): 1289–1306.
113.
XuFWangXWuH (2012) Inspection method of cable-stayed bridge using magnetic flux leakage detection: principle, sensor design, and signal processing. Journal of Mechanical Science and Technology26: 661–669.
114.
XuYBrownjohnJKongD (2018) A non-contact vision-based system for multipoint displacement monitoring in a cable-stayed footbridge. Structural Control and Health Monitoring25(5): e2155.
115.
XuYBrownjohnJMHuseynovF (2019) Accurate deformation monitoring on bridge structures using a cost-effective sensing system combined with a camera and accelerometers: case study. Journal of Bridge Engineering24(1): 05018014.
116.
YangKCLuoYPPanS, et al. (2024) Non-contact measurement of structural dynamic displacement based on improved edge detection and video interpolation. Advances in Structural Engineering. DOI: 10.1177/13694332241289176.18.
117.
YaoHCaoHLiJ (2016) Design and implementation of a portable wireless system for structural health monitoring. Measurement and Control49(1): 23–32.
118.
YesilyurtIGursoyH (2015) Estimation of elastic and modal parameters in composites using vibration analysis. Journal of Vibration and Control21(3): 509–524.
119.
YingYGarrettJJHOppenheimIJ, et al. (2013) Toward data-driven structural health monitoring: application of machine learning and signal processing to damage detection. Journal of Computing in Civil Engineering27(6): 667–680.
120.
YoonHElanwarHChoiH, et al. (2016) Target-free approach for vision-based structural system identification using consumer-grade cameras. Structural Control and Health Monitoring23(12): 1405–1416.
121.
YuLZhuJHYuLL (2013) Structural damage detection in a truss bridge model using fuzzy clustering and measured FRF data reduced by principal component projection. Advances in Structural Engineering16(1): 207–217.
122.
YuYHanFBaoY, et al. (2015) A study on data loss compensation of WiFi-based wireless sensor networks for structural health monitoring. IEEE Sensors Journal16(10): 3811–3818.
123.
YuHZhuHWengS, et al. (2023) Substructural damage identification using autoregressive moving average with exogenous inputs model and sparse regularization. Advances in Structural Engineering26(9): 1621–1635.
124.
ZhangJTianGYMarindraAM, et al. (2017) A review of passive RFID tag antenna-based sensors and systems for structural health monitoring applications. Sensors17(2): 265.
125.
ZhangJChenBZhaoY, et al. (2018) Data security and privacy-preserving in edge computing paradigm: survey and open issues. IEEE Access6: 18209–18237.
126.
ZhangBZhouLZhangJ (2019) A methodology for obtaining spatiotemporal information of the vehicles on bridges based on computer vision. Computer-Aided Civil and Infrastructure Engineering34(6): 471–487.
127.
ZhangW-ZElgendyIAHammadM, et al. (2020) Secure and optimized load balancing for multitier IoT and edge-cloud computing systems. IEEE Internet of Things Journal8(10): 8119–8132.
128.
ZhangJCaiY-YYangD, et al. (2023) MobileNetV3-BLS: a broad learning approach for automatic concrete surface crack detection. Construction and Building Materials392: 131941.
129.
ZhaoWZhangGZhangJ (2020) Cable force estimation of a long-span cable-stayed bridge with microwave interferometric radar. Computer-Aided Civil and Infrastructure Engineering35(12): 1419–1433.
130.
ZhuDWangYBrownjohnJ (2011) Vibration testing of a steel girder bridge using cabled and wireless sensors. Frontiers of Architecture and Civil Engineering in China5: 249–258.
131.
ZhuDGuoJChoC, et al. (2012) Wireless mobile sensor network for the system identification of a space frame bridge. IEEE/ASME transactions on Mechatronics17(3): 499–507.
132.
ZhuLFuYChowR, et al. (2018) Development of a high-sensitivity wireless accelerometer for structural health monitoring. Sensors18(1): 262.
133.
ZimmermanATLynchJP (2009) A parallel simulated annealing architecture for model updating in wireless sensor networks. IEEE Sensors Journal9(11): 1503–1510.
