Eigenfrequency analysis of bridges using a smartphone and a novel low-cost accelerometer prototype

doi:10.1007/s11709-024-1055-5

Front. Struct. Civ. Eng.

2024, Vol. 18

Issue (2) : 202-215 https://doi.org/10.1007/s11709-024-1055-5

Eigenfrequency analysis of bridges using a smartphone and a novel low-cost accelerometer prototype

Seyedmilad KOMARIZADEHASL¹, Ye XIA²(

), Mahyad KOMARY¹, Fidel LOZANO³

¹. Department of Civil and Environment Engineering, Universitat Politècnica de Catalunya, Barcelona 08034, Spain
². Department of Bridge Engineering, Tongji University, Shanghai 200092, China
³. Department of Civil Engineering, Universidad de Castilla-La Mancha, Ciudad Real 13071, Spain

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Abstract

Researchers are paying increasing attention to the development of low-cost and microcontroller-based accelerometers, in order to make structural health monitoring feasible for conventional bridges with limited monitoring budget. Parallel with the low-cost sensor development, the use of the embedded accelerometers of smartphones for eigenfrequency analysis of bridges is becoming popular in the civil engineering literature. This paper, for the first time in the literature, studies these two promising technologies by comparing the noise density and eigenfrequency analysis of a self-developed, validated and calibrated low-cost Internet of things based accelerometer LARA (low cost adaptable reliable accelerometer) with those of a state of the art smartphone (iPhone XR). The eigenfrequency analysis of a footbridge in San Sebastian, Spain, showed that the embedded accelerometer of the iPhone XR can measure the natural frequencies of the under study bridge.

Keywords smartphone modal analysis eigenfrequency analysis low-cost accelerometers Arduino Raspberry Internet of things

Corresponding Author(s): Ye XIA

Just Accepted Date: 10 April 2024 Online First Date: 24 May 2024 Issue Date: 07 June 2024

Cite this article:

Seyedmilad KOMARIZADEHASL,Ye XIA,Mahyad KOMARY, et al. Eigenfrequency analysis of bridges using a smartphone and a novel low-cost accelerometer prototype[J]. Front. Struct. Civ. Eng., 2024, 18(2): 202-215.

URL:

https://academic.hep.com.cn/fsce/EN/10.1007/s11709-024-1055-5
https://academic.hep.com.cn/fsce/EN/Y2024/V18/I2/202

Fig.1 Key elements of a typical SHM application.

Fig.2 Acceleration sensing principles and some common accelerometer types.

Fig.3 Building map for manufacturing LARA: (a) sensing part; (b) data acquisition part.

Fig.4 The assembled sensing part of LARA: (a) LARA assembled by hand; (b) redesigned LARA produced using the machine assembly.

Fig.5 The axes of a smartphone and the contributed data to the database of Phyphox software.

Tab.1 Comparing the vibration acquistion of some smartphones from the data source of Phyphox website [68]

Fig.6 Mounting LARA and iPhone XR for noise density analysis.

LARA	Noise density ( $μg / Hz$ )	iPhone XR	Noise density ( $μg / Hz$ )
X,Y	51	X,Y	66
Z	82	Z	110

Tab.2 Noise density comparison of LARA with iPhone XR accelerometer

Fig.7 Time domain acceleration acquisition of the iPhone XR.

Fig.8 Time domain acceleration acquisition of LARA.

Fig.9 Footbridge instrumentation using LARA accelerometers and an iPhone XR: (a) Puente Alondegi with span length of 60 m; (b) bridge instrumentation.

Fig.10 FFT output of the vibration acquisition of the Puente Alondegi bridge: (a) transverse vertical direction of the bridge vibration; (b) transverse horizontal direction of the bridge vibration; (c) longitudinal direction of the bridge vibration.

Tab.3 Eigenfrequency analysis of LARA and iPhone XR

1	J Alam, L A C Neves, H Zhang, D Dias-da-Costa. Assessment of remaining service life of deteriorated concrete bridges under imprecise probabilistic information. Mechanical Systems and Signal Processing, 2022, 167: 108565 https://doi.org/10.1016/j.ymssp.2021.108565
2	R Javanmardi, B Ahmadi-Nedushan. Optimal design of double-layer barrel vaults using genetic and pattern search algorithms and optimized neural network as surrogate model. Frontiers of Structural and Civil Engineering, 2023, 17: 378–395 https://doi.org/10.1007/s11709-022-0899-9
3	G Tzortzinis, S F Breña, S Gerasimidis. Experimental testing, computational analysis and analytical formulation for the remaining capacity assessment of bridge plate girders with naturally corroded ends. Engineering Structures, 2022, 252: 113488 https://doi.org/10.1016/j.engstruct.2021.113488
4	ASCE. 2021 Report Card for America’s Infrastructure. 2021
5	A Miyamoto, R Kiviluoma, A Yabe. Frontier of continuous structural health monitoring system for short & medium span bridges and condition assessment. Frontiers of Structural and Civil Engineering, 2019, 13(3): 569–604 https://doi.org/10.1007/s11709-018-0498-y
6	J Hou, W Xu, Y Chen, K Zhang, H Sun, Y Li. Typical diseases of a long-span concrete-filled steel tubular arch bridge and their effects on vehicle-induced dynamic response. Frontiers of Structural and Civil Engineering, 2020, 14(4): 867–887 https://doi.org/10.1007/s11709-020-0649-9
7	M Domaneschi, C Pellecchia, E de Iuliis, G P Cimellaro, M Morgese, A A Khalil, F Ansari. Collapse analysis of the Polcevera viaduct by the applied element method. Engineering Structures, 2020, 214: 110659 https://doi.org/10.1016/j.engstruct.2020.110659
8	Y Kellouche, M Ghrici, B Boukhatem. Service life prediction of fly ash concrete using an artificial neural network. Frontiers of Structural and Civil Engineering, 2021, 15(3): 793–805 https://doi.org/10.1007/s11709-021-0717-9
9	S Komarizadehasl, M Khanmohammadi. Novel plastic hinge modification factors for damaged RC shear walls with bending performance. Advances in Concrete Construction, 2021, 12(4): 355–365 https://doi.org/10.12989/ACC.2021.12.4.355
10	X Qin, M Liang, X Xie, H Song. Mechanical performance analysis and stiffness test of a new type of suspension bridge. Frontiers of Structural and Civil Engineering, 2021, 15(5): 1160–1180 https://doi.org/10.1007/s11709-021-0760-6
11	E García-Macías, F Ubertini. MOVA/MOSS: Two integrated software solutions for comprehensive structural health monitoring of structures. Mechanical Systems and Signal Processing, 2020, 143: 106830 https://doi.org/10.1016/j.ymssp.2020.106830
12	Z Sun, N Hou, H Xiang. Safety and serviceability assessment for high-rise tower crane to turbulent winds. Frontiers of Structural and Civil Engineering, 2009, 3(1): 18–24 https://doi.org/10.1007/s11709-009-0009-2
13	Q Li, Y Xu, Y Zheng, A Guo, K Wong, Y Xia. SHM-based F-AHP bridge rating system with application to Tsing Ma Bridge. Frontiers of Structural and Civil Engineering, 2011, 5(4): 465–478 https://doi.org/10.1007/s11709-011-0135-5
14	K Kildashti, M Makki Alamdari, C W Kim, W Gao, B Samali. Drive-by-bridge inspection for damage identification in a cable-stayed bridge: Numerical investigations. Engineering Structures, 2020, 223: 110891 https://doi.org/10.1016/j.engstruct.2020.110891
15	K Matsuoka, H Tanaka. Drive-by deflection estimation method for simple support bridges based on track irregularities measured on a traveling train. Mechanical Systems and Signal Processing, 2023, 182: 109549 https://doi.org/10.1016/j.ymssp.2022.109549
16	S Teng, X Chen, G Chen, L Cheng. Structural damage detection based on transfer learning strategy using digital twins of bridges. Mechanical Systems and Signal Processing, 2023, 191: 110160 https://doi.org/10.1016/j.ymssp.2023.110160
17	A Karimpour, S Rahmatalla. Identification of structural parameters and boundary conditions using a minimum number of measurement points. Frontiers of Structural and Civil Engineering, 2020, 14(6): 1331–1348 https://doi.org/10.1007/s11709-020-0686-4
18	E Ghorbani, O Buyukozturk, Y J Cha. Hybrid output-only structural system identification using random decrement and Kalman filter. Mechanical Systems and Signal Processing, 2020, 144: 106977 https://doi.org/10.1016/j.ymssp.2020.106977
19	C Gentile, A Saisi. Continuous dynamic monitoring of a centenary iron bridge for structural modification assessment. Frontiers of Structural and Civil Engineering, 2015, 9(1): 26–41 https://doi.org/10.1007/s11709-014-0284-4
20	X Tong, S Song, L Wang, H Yang. A preliminary research on wireless cantilever beam vibration sensor in bridge health monitoring. Frontiers of Structural and Civil Engineering, 2018, 12(2): 207–214 https://doi.org/10.1007/s11709-017-0406-x
21	X Zhu, H Hao. Development of an integrated structural health monitoring system for bridge structures in operational conditions. Frontiers of Structural and Civil Engineering, 2012, 6(3): 321–333 https://doi.org/10.1007/s11709-012-0161-y
22	M Gatti. Structural health monitoring of an operational bridge: A case study. Engineering Structures, 2019, 195: 200–209 https://doi.org/10.1016/j.engstruct.2019.05.102
23	S Bhowmick, S Nagarajaiah, Z Lai. Measurement of full-field displacement time history of a vibrating continuous edge from video. Mechanical Systems and Signal Processing, 2020, 144: 106847 https://doi.org/10.1016/j.ymssp.2020.106847
24	B Mobaraki, S Komarizadehasl, F J C Pascual, J A Lozano-Galant, R P Soriano. A novel data acquisition system for obtaining thermal parameters of building envelopes. Buildings, 2022, 12(5): 670 https://doi.org/10.3390/buildings12050670
25	J Lei, D Xu, J Turmo. Static structural system identification for beam-like structures using compatibility conditions. Structural Control and Health Monitoring, 2018, 25(1): e2062 https://doi.org/10.1002/stc.2062
26	G Morgenthal, N Hallermann, J Kersten, J Taraben, P Debus, M Helmrich, V Rodehorst. Framework for automated UAS-based structural condition assessment of bridges. Automation in Construction, 2019, 97: 77–95 https://doi.org/10.1016/j.autcon.2018.10.006
27	A J Hughes, L A Bull, P Gardner, R J Barthorpe, N Dervilis, K Worden. On risk-based active learning for structural health monitoring. Mechanical Systems and Signal Processing, 2022, 167: 108569 https://doi.org/10.1016/j.ymssp.2021.108569
28	D Zhu, Y Wang, J Brownjohn. Vibration testing of a steel girder bridge using cabled and wireless sensors. Frontiers of Structural and Civil Engineering, 2011, 5(3): 249–258 https://doi.org/10.1007/s11709-011-0113-y
29	O Avci, O Abdeljaber, S Kiranyaz, M Hussein, M Gabbouj, D J Inman. A review of vibration-based damage detection in civil structures: From traditional methods to machine learning and deep learning applications. Mechanical Systems and Signal Processing, 2021, 147: 107077 https://doi.org/10.1016/j.ymssp.2020.107077
30	Y Shi, J Zhang, J Jiao, R Zhao, H Cao. Calibration analysis of high-G MEMS accelerometer sensor based on wavelet and wavelet packet denoising. Sensors, 2021, 21(4): 1231 https://doi.org/10.3390/s21041231
31	L Zhou, Y Ge. Sectional model test study on vortex-excited resonance of vehicle-bridge system of Shanghai Bridge over Yangtze River. Frontiers of Structural and Civil Engineering, 2009, 3(1): 67–72 https://doi.org/10.1007/s11709-009-0007-4
32	M Alarcón, P Soto, F Hernández, P Guindos. Structural health monitoring of South America’s first 6-story experimental light-frame timber-building by using a low-cost Raspberry Shake seismic instrumentation. Engineering Structures, 2023, 275: 115278 https://doi.org/10.1016/j.engstruct.2022.115278
33	S Komarizadehasl, P Huguenet, F Lozano, J A Lozano-Galant, J Turmo. Operational and analytical modal analysis of a bridge using low-cost wireless arduino-based accelerometers. Sensors, 2022, 22(24): 9808 https://doi.org/10.3390/s22249808
34	C Costa, D Ribeiro, P Jorge, R Silva, R Calçada, A Arêde. Calibration of the numerical model of a short-span masonry railway bridge based on experimental modal parameters. Procedia Engineering, 2015, 114: 846–853 https://doi.org/10.1016/j.proeng.2015.08.038
35	C Bedon, E Bergamo, M Izzi, S Noè. Prototyping and validation of mems accelerometers for structural health monitoring—The case study of the pietratagliata cable-stayed bridge. Journal of Sensor and Actuator Networks, 2018, 7(3): 30–48 https://doi.org/10.3390/jsan7030030
36	H Tran-Ngoc, S Khatir, G de Roeck, T Bui-Tien, L Nguyen-Ngoc, M A Wahab. Model updating for Nam O Bridge using particle swarm optimization algorithm and genetic algorithm. Sensors, 2018, 18(12): 4131 https://doi.org/10.3390/s18124131
37	J Farré-Checa, S Komarizadehasl, H Ma, J A Lozano-Galant, J Turmo. Direct simulation of the tensioning process of cable-stayed bridge cantilever construction. Automation in Construction, 2022, 137: 104197 https://doi.org/10.1016/j.autcon.2022.104197
38	S Samanta, S S Nanthakumar, R K Annabattula, X Zhuang. Detection of void and metallic inclusion in 2D piezoelectric cantilever beam using impedance measurements. Frontiers of Structural and Civil Engineering, 2019, 13(3): 542–556 https://doi.org/10.1007/s11709-018-0496-0
39	K Sobolev. Modern developments related to nanotechnology and nanoengineering of concrete. Frontiers of Structural and Civil Engineering, 2016, 10(2): 131–141 https://doi.org/10.1007/s11709-016-0343-0
40	S Komarizadehasl, B Mobaraki, H Ma, J A Lozano-Galant, J Turmo. Development of a low-cost system for the accurate measurement of structural vibrations. Sensors, 2021, 21(18): 6191–6213 https://doi.org/10.3390/s21186191
41	S Komarizadehasl, M Komary, A Alahmad, J A Lozano-Galant, G Ramos, J Turmo. A novel wireless low-cost inclinometer made from combining the measurements of multiple MEMS gyroscopes and accelerometers. Sensors, 2022, 22(15): 5605 https://doi.org/10.3390/s22155605
42	E Atencio, S Komarizadehasl, J A Lozano-Galant, M Aguilera. Using RPA for performance monitoring of dynamic SHM applications. Buildings, 2022, 12(8): 1140 https://doi.org/10.3390/buildings12081140
43	S Komarizadehasl, F Lozano, J A Lozano-Galant, G Ramos, J Turmo. Low-cost wireless structural health monitoring of bridges. Sensors, 2022, 22(15): 5725 https://doi.org/10.3390/s22155725
44	M Zhang, F Xu. Variational mode decomposition based modal parameter identification in civil engineering. Frontiers of Structural and Civil Engineering, 2019, 13(5): 1082–1094 https://doi.org/10.1007/s11709-019-0537-3
45	S KavithaR Joseph DanielK Sumangala. High performance MEMS accelerometers for concrete SHM applications and comparison with COTS accelerometers. Mechanical Systems and Signal Processing, 2016, 66–67: 410–424
46	F Zonzini, A Carbone, F Romano, M Zauli, L de Marchi. Machine learning meets compressed sensing in vibration-based monitoring. Sensors, 2022, 22(6): 2229 https://doi.org/10.3390/s22062229
47	Instruments Texas. Spectral Density, TI Precision Labs. 2015. Available at the website of Texas Instruments
48	H Rocha, C Semprimoschnig, J P Nunes. Sensors for process and structural health monitoring of aerospace composites: A review. Engineering Structures, 2021, 237: 112231 https://doi.org/10.1016/j.engstruct.2021.112231
49	N Pahnabi, S M Seyedpoor. Damage identification in connections of moment frames using time domain responses and an optimization method. Frontiers of Structural and Civil Engineering, 2021, 15(4): 851–866 https://doi.org/10.1007/s11709-021-0739-3
50	K A GrimmelsmanN Zolghadri. Experimental evaluation of low-cost accelerometers for dynamic characterization of bridges. In: Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics 2019. Cham: Springer Cham, 2020, 145–152
51	A GirolamiF ZonziniL De MarchiD BrunelliL Benini. Modal analysis of structures with low-cost embedded systems. In: Proceedings of 2018 IEEE International Symposium on Circuits and Systems (ISCAS). New York: IEEE, 2018, 1–4
52	A I Ozdagli, B Liu, F Moreu. Low-cost, efficient wireless intelligent sensors (LEWIS) measuring real-time reference-free dynamic displacements. Mechanical Systems and Signal Processing, 2018, 107: 343–356 https://doi.org/10.1016/j.ymssp.2018.01.034
53	Q Meng, S Zhu. Developing iot sensing system for construction-induced vibration monitoring and impact assessment. Sensors, 2020, 20(21): 6120 https://doi.org/10.3390/s20216120
54	M Mousavi, M Alzgool, B Davaji, S Towfighian. Event-driven MEMS vibration sensor: Integration of triboelectric nanogenerator and low-frequency switch. Mechanical Systems and Signal Processing, 2023, 187: 109921 https://doi.org/10.1016/j.ymssp.2022.109921
55	S Sony, S Laventure, A Sadhu. A literature review of next-generation smart sensing technology in structural health monitoring. Structural Control and Health Monitoring, 2019, 26(3): e2321 https://doi.org/10.1002/stc.2321
56	A Shrestha, J Dang, X Wang, S Matsunaga. Smartphone-based bridge seismic monitoring system and long-term field application tests. Journal of Structural Engineering, 2020, 146(2): 04019208 https://doi.org/10.1061/(ASCE)ST.1943-541X.0002513
57	J Reilly, S Dashti, M Ervasti, J D Bray, S D Glaser, A M Bayen. Mobile phones as seismologic sensors: Automating data extraction for the ishake system. IEEE Transactions on Automation Science and Engineering, 2013, 10(2): 242–251 https://doi.org/10.1109/TASE.2013.2245121
58	E Ozer, R Purasinghe, M Q Feng. Multi-output modal identification of landmark suspension bridges with distributed smartphone data: Golden Gate Bridge. Structural Control and Health Monitoring, 2020, 27(10): e2576 https://doi.org/10.1002/stc.2576
59	S Quqa, P F Giordano, M P Limongelli. Shared micromobility-driven modal identification of urban bridges. Automation in Construction, 2022, 134: 104048 https://doi.org/10.1016/j.autcon.2021.104048
60	J Kolakowski, V Djaja-Josko, M Kolakowski. UWB monitoring system for AAL applications. Sensors, 2017, 17(9): 2092 https://doi.org/10.3390/s17092092
61	Market Back. iPhone XR in Back Market. 2022. Available at the website of Back Market
62	Q Wu, Y Chen. Adaptive cooperative control of a soft elbow rehabilitation exoskeleton based on improved joint torque estimation. Mechanical Systems and Signal Processing, 2023, 184: 109748 https://doi.org/10.1016/j.ymssp.2022.109748
63	S Komarizadehasl, B Mobaraki, H Ma, J A Lozano-Galant, J Turmo. Low-cost sensors accuracy study and enhancement strategy. Applied Sciences, 2022, 12(6): 3186–3215 https://doi.org/10.3390/app12063186
64	Laboratories Applus+. Acoustic and Vibration Calibration. 2022. Available at the website of Applus+
65	C Stampfer, H Heinke, S Staacks. A lab in the pocket. Nature Reviews Materials, 2020, 5(3): 169–170 https://doi.org/10.1038/s41578-020-0184-2
66	J P Barrajón, A F S Juan. Validity and reliability of a smartphone accelerometer for measuring lift velocity in bench-press exercises. Sustainability, 2020, 12(6): 2312 https://doi.org/10.3390/su12062312
67	Z Christoforou, C Gioldasis, Y Valero, G Vasileiou-Voudouris. Smart traffic data for the analysis of sustainable travel modes. Sustainability, 2022, 14(18): 11150 https://doi.org/10.3390/su141811150
68	Aachen University RWTH. Phyphox Sensor Database. 2022. Available at the website of Phyphox
69	Politècnica de Catalunya (UPC) Universitat. Laboratory of Technology of Structures & Materials “Lluis Agulló” (LATEM). 2022 (available at the website of LATEM)
70	HBM. X60 Cold Curing Glue for Strain Gauge Installations. 2021 (available at the website of HBM)
71	K M Kwong. MEMS accelerometer specifications and their impact in inertial applications. Thesis for the Master’s Degree. Toronto: University of Toronto, 2017
72	R R Ribeiro, R D M Lameiras. Evaluation of low-cost MEMS accelerometers for SHM: Frequency and damping identification of civil structures. Latin American Journal of Solids and Structures, 2019, 16(7): e203 https://doi.org/10.1590/1679-78255308
73	PCB Piezotronics, Inc. Model 356A01 ICP® Accelerometer Installation and Operating Manual. 2023
74	J D Braido, Z M C Pravia. Application of MEMS accelerometer of smartphones to define natural frequencies and damping ratios obtained from concrete viaducts and footbridge. Revista IBRACON de Estruturas e Materiais, 2022, 15(2): e15206 https://doi.org/10.1590/s1983-41952022000200006
75	M Feng, Y Fukuda, M Mizuta, E Ozer. Citizen sensors for SHM: Use of accelerometer data from smartphones. Sensors, 2015, 15(2): 2980–2998 https://doi.org/10.3390/s150202980
76	3M Company. Adhesive Transfer Tapes with Adhesive 300 (Slam Stick), 2002

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