Publicado

2023-03-10

Weighted Average Bridge Inspection Methodology (WABIM)

Metodología de Inspección de Puentes por Promedios Ponderados (WABIM)

DOI:

https://doi.org/10.15446/dyna.v90n225.104694

Palabras clave:

bridge methodology; damage quantification; pathologies; structural health; visual inspection (en)
metodología de puentes; cuantificación de daño; patologías; salud estructural; inspección visual (es)

Autores/as

This article discusses developing a methodology based on visual inspection for quantifying bridge damage (WABIM). The proposed methodology was developed through the application of weighted averages and a case study. Many current visual inspection methodologies, manuals, or guides related to bridges only allow qualitative results to be determined. Consequently, a high degree of inefficiency and inaccuracy was identified in the results from traditional methodologies; since they have a subjective approach, the results merely depend on the observer. Therefore, a methodological proposal was generated that allowed qualitative results to be described quantitatively, increasing the objectivity of the analysis and the accuracy of bridge maintenance plans. Rating ranges are used with weighted averages for each pathology, applied directly to the structural elements of the bridges. The classification guidelines and pathologies of bridge structures are adapted according to the Manual for the Visual Inspection of Bridges and Pontoons of Invías, Colombia. The case study was developed on a bridge in the city of Pereira, Colombia, presenting more significant surface deterioration and equipment deterioration. The WABIM methodology identified that periodic maintenance is required and the intervention's emphasis.

Este artículo contiene el desarrollo de una metodología basada en inspección visual para la cuantificación de daño en puentes mediante la aplicación de promedios ponderados y un caso de aplicación práctica. En atención a que, la mayoría de metodologías, manuales o guías de inspección visual actual relacionadas con puentes, sólo permiten determinar resultados cualitativos. Por consiguiente, se identificó un alto grado de ineficiencia en las diferentes perspectivas aplicadas al análisis de los resultados, teniendo así un enfoque subjetivo. Dado lo anterior, se genera una propuesta metodológica que permite describir resultados cualitativos de modo cuantitativo aumentando la objetividad de los análisis finales y el nivel de soporte a los planes de mantenimiento de puentes. Se emplean rangos de calificación con promedios ponderados para cada patología, aplicados directamente a los elementos estructurales de los puentes. Los lineamientos de clasificación y patologías de estructuras de puentes, se adaptan de acuerdo al Manual para la Inspección Visual de Puentes y Pontones del Invías, Colombia. El caso de estudio se desarrolló sobre un puente en la ciudad de Pereira, Colombia, que presentó un deterioro mayor en la superficie y equipamiento. El método WABIM permitió identificar que se requiere un mantenimiento periódico y el énfasis de la intervención.

Referencias

Djoković, J.M., Nikolić, R.R., Bujnák, J. Hadzima, B., Estimate of the steel bridges fatigue life and by application of the fracture mechanics. I.O.P. Conference Series: Materials Science and Engineering, 419(1), art. 012010, 2018. DOI: https://doi.org/10.1088/1757-899X/419/1/012010

Maki, Y., Ha, T.M., Fukada, S., Torii, K. and Ono, R., Current status of degraded road bridge slab located in mountainous area. MATEC Web of Conferences, 206, art. 01019, 2018. DOI: https://doi.org/10.1051/matecconf/201820601019

Medina, P.A., León-González, F.J. and Todisco, L., Data-driven prediction of long-term deterioration of RC bridges. Construction and Building Materials, 317, art. 125790, 2022. DOI: https://doi.org/10.1016/j.conbuildmat.2021.125790

Omar, T. and Nehdi, M.L., Condition assessment of reinforced concrete bridges: Current practice and research challenges. Infrastructures, 3(3), art. 036, 2018. DOI: https://doi.org/10.3390/infrastructures3030036

Tang, L., Maintenance and inspection of fiber-reinforced polymer (FRP) bridges: a review of methods. Materials, 14(24), art. 7826, 2021. DOI: https://doi.org/10.3390/ma14247826

Bachiri, T., Khamlichi, A. and Bezzazi, M., Detection of rebar corrosion in bridge deck by using GPR. MATEC Web of Conferences, 191, art. 00009, 2018. DOI: https://doi.org/10.1051/matecconf/201819100009

Chun, P.J., Kusumoto, M., Tsukada, K. and Okubo, K., Investigation and repair plan for abraded steel bridge piers: case study from Japan. Proceedings of the Institution of Civil Engineers: Forensic Engineering, 172(1), pp. 11-18. 2019. DOI: https://doi.org/10.1680/jfoen.18.00019

Meng, D., Xiao, F., Zhang, L., Xu, X., Chen, G.S., Zatar, W. and Hulsey, J.L., Nonlinear vibration analysis of vehicle–bridge interaction for condition monitoring. Journal of Low Frequency Noise Vibration and Active Control, 38(3–4), pp. 1422-1432, 2019. DOI: https://doi.org/10.1177/1461348418811703

Rizzo, P. and Enshaeian, A., Challenges in bridge health monitoring: A review. In Sensors 21(13), art. 4336, 2021. DOI: https://doi.org/10.3390/s21134336

Vlašić, A., Srbić, M., Skokandić, D. and Ivanković, A.M., Post-earthquake rapid damage assessment of road bridges in Glina County. Buildings, 12(1), art. 42, 2022. DOI: https://doi.org/10.3390/buildings12010042

Fathalla, E., Tanaka, Y. and Maekawa, K., Effect of crack orientation on fatigue life of reinforced concrete bridge decks. Applied Sciences (Switzerland), 9(8), art. 1644, 2019. DOI: https://doi.org/10.3390/app9081644

Li, Y., Zhao, W., Zhang, X. and Zhou, Q., A two-stage crack detection method for concrete bridges using convolutional neural networks. IEICE Transactions on Information and Systems, E101D(12), pp. 3249.3252, 2018. DOI: https://doi.org/10.1587/transinf.2018EDL8150

Sieber, L., Urbanek, R. and Bär, J., Crack-detection in old riveted steel bridge structures. Procedia Structural Integrity, 17, pp. 339-346. 2019. DOI: https://doi.org/10.1016/j.prostr.2019.08.045

Wang, H., Hu, C.H., Hsieh, S.H., Tsai, Y.C., Chen, M.H. and Wang, C.Y., Velocity tomography-based condition inspection on concrete pylons of a pedestrian suspension bridge. I.O.P. Conference Series: Materials Science and Engineering, 615(1), art. 012078, 2019. DOI: https://doi.org/10.1088/1757-899X/615/1/012078

Huang, Q., Highway bridge test detection technology and application method based on big data. Journal of Physics: Conference Series, 1992(4), art. 042046, 2021. DOI: https://doi.org/10.1088/1742-6596/1992/4/042046

Jafari, F. and Dorafshan, S., Bridge inspection and defect recognition with using impact echo data, probability, and naive bayes classifiers. Infrastructures, 6(9), art. 132, 2021. DOI: https://doi.org/10.3390/infrastructures6090132

Dizaj, E.A., Padgett, J.E. and Kashani, M.M., A Markov chain-based model for structural vulnerability assessmentof corrosion-damaged reinforced concrete bridges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379(2203), art. 0290, 2021. DOI: https://doi.org/10.1098/rsta.2020.0290

Su, D., Liu, Y., Li, X. and Cao, Z., Study on optimization of inspection mechanism of concrete beam bridge. PLoS O.N.E., 16(8), art. 0256028, 2021. DOI: https://doi.org/10.1371/journal.pone.0256028

Tian, F., Zhao, Y., Che, X., Zhao, Y. and Xin, D., Concrete crack identification and image mosaic based on image processing. Applied Sciences (Switzerland), 9(22), art. 4826, 2019. DOI: https://doi.org/10.3390/app9224826

Setiati, R., Inspection and evaluation of bridge structures for earthquakes risk. I.O.P. Conference Series: Materials Science and Engineering, 930(1), art. 012032, 2020. DOI: https://doi.org/10.1088/1757-899X/930/1/012032

Mansour, M.D.M., Moustafa, I.M., Khalil, A.H. and Mahdi, H.A., An assessment model for identifying maintenance priorities strategy for bridges. Ain Shams Engineering Journal, 10(4), pp. 695-704, 2019. DOI: https://doi.org/10.1016/j.asej.2019.06.003

Saleem, M.R., Straus, A. and Napolitano, R., Interpretation of historic structure for non-invasive assessment using eye tracking. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 46(M-1–2021), pp. 653-660. 2021. DOI: https://doi.org/10.5194/isprs-Archives-XLVI-M-1-2021-653-2021

Chaowei, H., Kang, L., Hongyin, L. and Laiyong, W., Research on the material properties of prestressed concrete girder bridge after exposed to fire. I.O.P. Conference Series: Earth and Environmental Science, 371(4), art. 042027, 2019. DOI: https://doi.org/10.1088/1755-1315/371/4/042027

Ebensperger, L. and Donoso, J.P., New methodology for assessment of reinforced concrete structures with non-destructive testing Nueva metodología de diagnóstico de estructuras de hormigón armado con técnicas no-destructivas. [online]. 2021. Available at: www.ricuc.cl DOI: https://doi.org/10.4067/S0718-50732021000200233

Rocha, J.H.A. y Póvoas, Y., Detección de delaminaciones en puentes de concreto armado usando Termografía Infrarroja Detection of Delaminations in Reinforced Concrete Bridges Using Infrared Thermography. [online]. 2018. Available at: www.ricuc.cl DOI: https://doi.org/10.4067/S0718-50732019000100055

Santarsiero, G., Masi, A., Picciano, V. and Digrisolo, A., (). The Italian guidelines on risk classification and management of bridges: Applications and remarks on large scale risk assessments. Infrastructures, 6(8), art. 111, 2021. DOI: https://doi.org/10.3390/infrastructures6080111

Xijian, L., Qiuyan, S. and Xingxing, H., Static load test and evaluation of a separated interchange bridge. I.O.P. Conference Series: Earth and Environmental Science, 267(4), art. 042011, 2019. DOI: https://doi.org/10.1088/1755-1315/267/4/042011

Aliyari, M., Ashrafi, B. and Ayele, Y.Z., Hazards identification and risk assessment for UAV–assisted bridge inspections. Structure and Infrastructure Engineering, 18(3), pp. 412-428, 2022. DOI: https://doi.org/10.1080/15732479.2020.1858878

Aliyari, M., Droguett, E.L. and Ayele, Y.Z., UAV-based bridge inspection via transfer learning. Sustainability (Switzerland), 13(20), art. 11359, 2021. DOI: https://doi.org/10.3390/su132011359

Ivanovic, A., Markovic, L., Car, M., Duvnjak, I. and Orsag, M., Towards autonomous bridge inspection: Sensor mounting using aerial manipulators. Applied Sciences (Switzerland), 11(18), art. 88279, 2021. DOI: https://doi.org/10.3390/app11188279

Khedmatgozar-Dolati, S.S., Caluk, N., Mehrabi, A. and Khedmatgozar Dolati, S.S., Non‐destructive testing applications for steel bridges. In Applied Sciences (Switzerland), 11(20), art. 9757, 2021. DOI: https://doi.org/10.3390/app11209757

Mandirola, M., Casarotti, C., Peloso, S., Lanese, I., Brunesi, E. and Senaldi, I., Use of U.A.S. for damage inspection and assessment of bridge infrastructures. International Journal of Disaster Risk Reduction, 72, art. 102824, 2022. DOI: https://doi.org/10.1016/j.ijdrr.2022.102824

Achuthan, K., Hay, N., Aliyari, M. and Ayele, Y.Z., A digital information model framework for uas-enabled bridge inspection. Energies, 14(19), art. 6017, 2021. DOI: https://doi.org/10.3390/en14196017

Carnevale, M., Collina, A. and Peirlinck, T., A feasibility study of the drive-by method for damage detection in railway bridges. Applied Sciences (Switzerland), 9(1), art. 10160, 2019. DOI: https://doi.org/10.3390/app9010160

Kruachottikul, P., Cooharojananone, N., Phanomchoeng, G. and Kovitanggoon, K., Development of a user-centric bridge visual defect quality control assisted mobile application: a case of thailand’s department of highways. Applied Sciences (Switzerland), 11(20), art. 09555, 2021. DOI: https://doi.org/10.3390/app11209555

Macchiarulo, V., Milillo, P., Blenkinsopp, C. and Giardina, G., Monitoring deformations of infrastructure networks: a fully automated G.I.S. integration and analysis of InSAR time-series. Structural Health Monitoring. 21(4), pp. 1849-1878, 2022. DOI: https://doi.org/10.1177/14759217211045912

Mirzazade, A., Popescu, C., Blanksvärd, T. and Täljsten, B., Workflow for off-site bridge inspection using automatic damage detection-case study of the pahtajokk bridge. Remote Sensing, 13(14), art. 2665, 2021. DOI: https://doi.org/10.3390/rs13142665

Sadeghi, F., Zhu, X., Li, J. and Rashidi, M., A novel slip sensory system for interfacial condition monitoring of steel-concrete composite bridges. Remote Sensing, 13(17), art. 3377, 2021. DOI: https://doi.org/10.3390/rs13173377

Santos, A.F., Bonatte, M.S., Sousa, H.S., Bittencourt, T.N. and Matos, J.C., Improvement of the Inspection Interval of Highway Bridges through Predictive Models of Deterioration. Buildings, 12(2), art. 124, 2022. DOI: https://doi.org/10.3390/buildings12020124

Sinsamutpadung, N. and Sasaki, E., (). Strain-based Evaluation of Bridge Monitoring using Numerical Model Analysis. I.O.P. Conference Series: Materials Science and Engineering, 639(1), art. 012023, 2019. DOI: https://doi.org/10.1088/1757-899X/639/1/012023

Stochino, F., Fadda, M.L. and Mistretta, F., Assessment of RC bridges integrity by means of low-cost investigations. Frattura Ed Integrita Strutturale, 12(46), pp. 216-225, 2018. DOI: https://doi.org/10.3221/IGF-ESIS.46.20

Truong-Hong, L. and Lindenbergh, R., Automatically extracting surfaces of reinforced concrete bridges from terrestrial laser scanning point clouds. Automation in Construction, 135, art. 104127, 2022. DOI: https://doi.org/10.1016/j.autcon.2021.104127

Xia, Y., Lei, X., Wang, P. and Sun, L., Artificial intelligence based structural assessment for regional short-and medium-span concrete beam bridges with inspection information. Remote Sensing, 13(18), art. 3687, 2021. DOI: https://doi.org/10.3390/rs13183687

Zambon, I., Vidović, A., Strauss, A. and Matos, J., Condition prediction of existing concrete bridges as a combination of visual inspection and analytical models of deterioration. Applied Sciences (Switzerland), 9(1), art. 10148, 2019. DOI: https://doi.org/10.3390/app9010148

Brackenbury, D., Brilakis, I. and Dejong, M., Automated defect detection for masonry arch bridges. In: International Conference on Smart Infrastructure and Construction 2019, ICSIC 2019: Driving Data-Informed Decision-Making, 2019, pp. 3-10. DOI: https://doi.org/10.1680/icsic.64669.003

Kim, H. and Kim, C., Deep-learning-based classification of point clouds for bridge inspection. Remote Sensing, 12(22), pp. 1-13. 2020. DOI: https://doi.org/10.3390/rs12223757

Maroni, A., Tubaldi, E., Douglas, J., Ferguson, N., Val, D., McDonald, H., Lothian, S., Chisholm, A., Riches, O., Walker, D., Greenoak, E., Green, C. and Zonta, D., Managing bridge scour risk using structural health monitoring. In: International Conference on Smart Infrastructure and Construction 2019, ICSIC 2019: Driving Data-Informed Decision-Making, 2019, pp. 77-84. DOI: https://doi.org/10.1680/icsic.64669.077

Tan, L., Zhang, Z. and Wang, X., Research on design and application based on bridge inspection and maintenance scheme. I.O.P. Conference Series: Materials Science and Engineering, 914(1), art. 012040, 2020. DOI: https://doi.org/10.1088/1757-899X/914/1/012040

Pulido, B.S. y Rico, L.A., Caracterización de las patologías de los puentes peatonales en la localidad de Usaquén, Tesis de grado. Facultad de Ingeniería, Universidad Católica de Colombia, Bogotá, Colombia, 2018, 71 P.

Coy-Ramirez, S.C., Diagnóstico de puentes mediante inspección visual de pavimento flexible, con base en la comparación de la metodología PCI (Pavement Condition Index) y Vizir. Trabajo de grado, Universidad Militar Nueva Granada, 2016.

Atadero, R.A., Jia, G., Abdallah, A. and Ozbek, M.E., An integrated uncertainty-based bridge inspection decision framework with application to concrete bridge decks. Infrastructures, 4(3), art. 50, 2019. DOI: https://doi.org/10.3390/infrastructures4030050

Frøseth, G.T. and Rönnquist, A., Finding the train composition causing greatest fatigue damage in railway bridges by Late Acceptance Hill Climbing. Engineering Structures, 196(July), art. 109342. 2019 DOI: https://doi.org/10.1016/j.engstruct.2019.109342

Klueva, N., Emelyanov, S., Kolchunov, V. and Gubanova, M., Criterion of crack resistance of corrosion damaged concrete in plane stress state. Procedia Engineering, 117(1), pp. 179-185, 2015. DOI: https://doi.org/10.1016/j.proeng.2015.08.144

Corrêa, R.A.P., Stutz, L.T. and Tenenbaum, R.A., Identificação de danos estruturais em placas baseada em um modelo de dano contínuo. Revista Internacional de Métodos Numéricos para Calculo y Diseño en Ingeniería, 32(1), pp. 58-64, 2016. DOI: https://doi.org/10.1016/j.rimni.2014.11.004

Tenžera, D., Puž, G. and Radiæ, J., Visual inspection in evaluation of bridge condition. Gradjevinar, 64(9), pp. 717-726, 2012. DOI: https://doi.org/10.14256/jce.718.2012

Ye, C., Acikgoz, S., Pendrigh, S., Riley, E. and DeJong, M.J., Mapping deformations and inferring movements of masonry arch bridges using point cloud data. Engineering Structures, 173(April), pp. 530-545, 2018. DOI: https://doi.org/10.1016/j.engstruct.2018.06.094

INVÍAS, Manual para la inspección visual de puentes y pontones, 2006.

Hinostroza, G. y Gisbel, C., Evaluación de la condición del pavimento rígido por el método PCI en el anillo vial Tramo Chaupimarca- Yanacocha-Pasco 2018, Teesis de grado, Universidad Nacional Daniel Alcides Carrión, Pasco, Perú, 2019.

Cómo citar

IEEE

[1]
C. C. Amariles-López y C. C. Osorio-Gómez, «Weighted Average Bridge Inspection Methodology (WABIM)», DYNA, vol. 90, n.º 225, pp. 55–63, mar. 2023.

ACM

[1]
Amariles-López, C.C. y Osorio-Gómez, C.C. 2023. Weighted Average Bridge Inspection Methodology (WABIM). DYNA. 90, 225 (mar. 2023), 55–63. DOI:https://doi.org/10.15446/dyna.v90n225.104694.

ACS

(1)
Amariles-López, C. C.; Osorio-Gómez, C. C. Weighted Average Bridge Inspection Methodology (WABIM). DYNA 2023, 90, 55-63.

APA

Amariles-López, C. C. & Osorio-Gómez, C. C. (2023). Weighted Average Bridge Inspection Methodology (WABIM). DYNA, 90(225), 55–63. https://doi.org/10.15446/dyna.v90n225.104694

ABNT

AMARILES-LÓPEZ, C. C.; OSORIO-GÓMEZ, C. C. Weighted Average Bridge Inspection Methodology (WABIM). DYNA, [S. l.], v. 90, n. 225, p. 55–63, 2023. DOI: 10.15446/dyna.v90n225.104694. Disponível em: https://revistas.unal.edu.co/index.php/dyna/article/view/104694. Acesso em: 18 mar. 2026.

Chicago

Amariles-López, Cristhian Camilo, y Cristian Camilo Osorio-Gómez. 2023. «Weighted Average Bridge Inspection Methodology (WABIM)». DYNA 90 (225):55-63. https://doi.org/10.15446/dyna.v90n225.104694.

Harvard

Amariles-López, C. C. y Osorio-Gómez, C. C. (2023) «Weighted Average Bridge Inspection Methodology (WABIM)», DYNA, 90(225), pp. 55–63. doi: 10.15446/dyna.v90n225.104694.

MLA

Amariles-López, C. C., y C. C. Osorio-Gómez. «Weighted Average Bridge Inspection Methodology (WABIM)». DYNA, vol. 90, n.º 225, marzo de 2023, pp. 55-63, doi:10.15446/dyna.v90n225.104694.

Turabian

Amariles-López, Cristhian Camilo, y Cristian Camilo Osorio-Gómez. «Weighted Average Bridge Inspection Methodology (WABIM)». DYNA 90, no. 225 (marzo 3, 2023): 55–63. Accedido marzo 18, 2026. https://revistas.unal.edu.co/index.php/dyna/article/view/104694.

Vancouver

1.
Amariles-López CC, Osorio-Gómez CC. Weighted Average Bridge Inspection Methodology (WABIM). DYNA [Internet]. 3 de marzo de 2023 [citado 18 de marzo de 2026];90(225):55-63. Disponible en: https://revistas.unal.edu.co/index.php/dyna/article/view/104694

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