Automated knowledge graphs for complex systems (AutoGraCS): Applications to management of bridge networks

Abstract

With the ability to harness the power of big data, the digital twin (DT) technology has been increasingly applied to the modeling and management of structures and infrastructure systems, such as buildings, bridges, and power distribution systems. Supporting these applications, an important family of methods are based on graphs. For DT applications in modeling and managing smart cities, large-scale knowledge graphs (KGs) are necessary to represent the complex interdependencies and model the urban infrastructure as a system of systems. To this end, this paper develops a conceptual framework: Automated knowledge Graphs for Complex Systems (AutoGraCS). In contrast to existing KGs developed for DTs, AutoGraCS can support KGs to account for interdependencies and statistical correlations across complex systems. The established KGs from AutoGraCS can then be easily turned into Bayesian networks for probabilistic modeling, Bayesian analysis, and adaptive decision supports. Besides, AutoGraCS provides flexibility in support of users’ need to implement the ontology and rules when constructing the KG. With the user-defined ontology and rules, AutoGraCS can automatically generate a KG to represent a complex system consisting of multiple systems. The bridge network in Miami-Dade County, FL is used as an illustrative example to generate a KG that integrates multiple layers of data from the bridge network, traffic monitoring facilities, and flood water watch stations.

Keywords

System digital twin / Bayesian network / Infrastructure systems / Knowledge Graph

Relevance to resilience

This paper develops a conceptual framework Automated knowledge Graphs for Complex Systems (AutoGraCS). With the user-defined ontology and rules, AutoGraCS can automatically generate a large-scale knowledge graph (KG) to represent a complex system consisting of multiple systems. These generated KGs can consider the statistical correlations and interdependencies within the complex systems and can be easily turned into digital twins for probabilistic analysis. AutoGraCS is relevant to resilience because it can be used to generate large-scale KGs and digital twins that model the resilience of structures and infrastructure systems for cities and communities. The KGs and digital twins can then support the monitoring and decision-making regarding the community resilience.

CRediT authorship contribution statement

Minghui Cheng: Writing - original draft, Validation, Methodology, Formal analysis, Conceptualization. Syed M.H. Shah: Software, Data curation. Antonio Nanni: Writing - review & editing, Supervision, Funding acquisition. H. Oliver Gao: Writing - review & editing, Methodology, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors are grateful for the financial support received from US Department of Transportation Tier 1 University Transportation Center CREATE Award No. 69A3552348330.

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.rcns.2024.11.001.

References

[1] Broo DG, Bravo-Haro M, Schooling J. Design and implementation of a smart infrastructure digital twin. Autom Constr 2022;136:104171.

[2] Errandonea I, Beltrán S, Arrizabalaga S. Digital Twin for maintenance: a literature review. Comput Ind 2020;123:103316.

[3] Pregnolato M, Gunner S, Voyagaki E, De Risi R, Carhart N, Gavriel G, Tully P, Tryfonas T, Macdonald J, Taylor C.Towards Civil Engineering 4.0: concept, workflow and application of digital twins for existing infrastructure. Autom Constr 2022;141:104421.

[4] Wagg DJ, Worden K, Barthorpe RJ, Gardner P. Digital twins: state-of-the-art and future directions for modeling and simulation in engineering dynamics applications. ASCE-ASME J Risk Uncert Eng Syst, Part B Mech Eng 2020; 6(3):030901.

[5] Kapteyn MG, Pretorius JV, Willcox KE. A probabilistic graphical model foundation for enabling predictive digital twins at scale. Nat Comput Sci 2021; 1(5):337-47.

[6] Xiong M, Wang H. Digital twin applications in aviation industry: a review. Internat JAdv Manufactur Techn 2022;121:5677-92.

[7] Fonseca ÍA, Gaspar HM. Challenges when creating a cohesive digital twin ship: a data modelling perspective. Ship Techn Res 2021; 68(2):70-83.

[8] Groden M, Collette M. Fusing fleet in-service measurements using Bayesian networks. Marine Struct 2017;54:38-49.

[9] Zhu J, Collette M. A Bayesian approach for shipboard lifetime wave load spectrum updating. Struct Infrastr Eng 2017; 13(2):298-312.

[10] Lin K, Xu YL, Lu X, Guan Z, Li J. Digital twin-based collapse fragility assessment of a long-span cable-stayed bridge under strong earthquakes. Autom Constr 2021;123:103547.

[11] Shim CS, Dang NS, Lon S, Jeon CH.Development of a bridge maintenance system for prestressed concrete bridges using 3D digital twin model. Struct Infrastr Eng 2019; 15(10):1319-32.

[12] Yoon S, Lee S, Kye S, Kim IH, Jung HJ, Spencer BF. Seismic fragility analysis of deteriorated bridge structures employing a UAV inspection-based updated digital twin. Struct Multidiscipl Optimiz 2022; 65(12):346.

[13] Guo D, Zhong RY, Lin P, Lyu Z, Rong Y, Huang GQ. Digital twin-enabled graduation intelligent manufacturing system for fixed-position assembly islands. Robot Comput Integr Manuf 2020;63:101917.

[14] Jiang Y, Li M, Wu W, Wu X, Zhang X, Huang X, Zhong RY, Huang GQ. Multi-domain ubiquitous digital twin model for information management of complex infrastructure systems. Adv Eng Inform 2023;56:101951.

[15] Kunzer B., Berges M., Dubrawski A. The Digital twin landscape at the crossroads of predictive maintenance, machine learning and physics based modeling. arXiv Preprint arXiv:220610462 2022.

[16] Vieira J, Poças Martins J, Marques de Almeida N, Patrício H, Gomes Morgado J. Towards resilient and sustainable rail and road networks: a systematic literature review on digital twins. Sustainability 2022; 14(12):7060.

[17] Chen X, Jia S, Xiang Y. A review: knowledge reasoning over knowledge graph. Expert Syst Appl 2020;141:112948.

[18] Austin M, Delgoshaei P, Coelho M, Heidarinejad M. Architecting smart city digital twins: combined semantic model and machine learning approach. J Manag Eng 2020; 36(4):04020026.

[19] Coelho M, Austin MA, Blackburn MR. Semantic behavior modeling and event-driven reasoning for urban system of systems. Internat J Adv Intellig Syst 2017; 10(3):365-82.

[20] France-Mensah J, O’Brien WJ. A shared ontology for integrated highway planning. Adv Eng Inform 2019;41:100929.

[21] Le T, Jeong HD. Interlinking life-cycle data spaces to support decision making in highway asset management. Autom Constr 2016;64:54-64.

[22] El-Gohary NM, El-Diraby TE. Domain ontology for processes in infrastructure and construction. J Constr Eng Manag 2010; 136(7):730-44.

[23] Wu C, Wu P, Wang J, Jiang R, Chen M, Wang X. Ontological knowledge base for concrete bridge rehabilitation project management. Autom Constr 2021;121:103428.

[24] Zhang S., Boukamp F., Teizer J. Ontology-based semantic modeling of construction safety knowledge: Towards automated safety planning for job hazard analysis (JHA). Automation in construction 2015;52:29-41.

[25] Akroyd J, Mosbach S, Bhave A, Kraft M. Universal digital twin-a dynamic knowledge graph. Data-Centric Eng 2021;2:e14.

[26] Eibeck A, Lim MQ, Simulator Kraft MJ-Park. An ontology-based platform for cross- domain scenarios in process industry. Comput Chem Eng 2019;131:106586.

[27] Hofmeister M, Brownbridge G, Hillman M, Mosbach S, Akroyd J, Lee KF, Kraft M. Cross-domain flood risk assessment for smart cities using dynamic knowledge graphs. Sustain Cities Soc 2024;101:105113.

[28] Savage T, Akroyd J, Mosbach S, Krdzavac N, Hillman M, Kraft M. Universal Digital Twin: integration of national-scale energy systems and climate data. Data-Centric Eng 2022;3:e23.

[29] Cheng M., Geng Z., Gao H.O. Custom Bayesian Networks for Performance-oriented System Digital Twin. Struct Infrastr Eng 2024; (Under Review).

[30] Applegate CJ, Tien I. Framework for probabilistic vulnerability analysis of interdependent infrastructure systems. J Comput Civil Eng 2019; 33(1):04018058.

[31] Byun JE, D’Ayala D. Urban seismic resilience mapping: a transportation network in Istanbul. Turkey. Scient Reports 2022;12:8188.

[32] Cheng M, Gao HO. Data-driven life-cycle risk assessment of bridge networks using Bayesian network. 8th international symposium on life-cycle civil engineering, IALCCE 2023. CRC Press/Balkema; 2023.

[33] Johansen C, Tien I. Probabilistic multi-scale modeling of interdependencies between critical infrastructure systems for resilience. Sustain Resilient Infrastruct 2018;3:1-15.

[34] Bismut E, Straub D. Optimal adaptive inspection and maintenance planning for deteriorating structural systems. Reliab Eng Syst Saf 2021;215:107891.

[35] Cheng M, Frangopol DM. Optimal load rating-based inspection planning of corroded steel girders using Markov decision process. Probabil Eng Mech 2021;66:103160.

[36] Marcot BG, Penman TD. Advances in Bayesian network modelling: integration of modelling technologies. Environm Modell Software 2019;111:386-93.

[37] Bensi MT. A bayesian network methodology for infrastructure seismic risk assessment and decision support. Berkeley: University of California; 2010.

[38] Bensi M, Der Kiureghian A, Straub D. Efficient Bayesian network modeling of systems. Reliab Eng Syst Saf 2013;112:200-13.

[39] Byun JE, Zwirglmaier K, Straub D, Song J. Matrix-based Bayesian Network for efficient memory storage and flexible inference. Reliab Eng Syst Saf 2019;185:533-45.

[40] Tien I, Der Kiureghian A. Algorithms for Bayesian network modeling and reliability assessment of infrastructure systems. Reliab Eng Syst Saf 2016;156:134-47.

[41] Bensi M, Der Kiureghian A, Straub D. Bayesian network modeling of correlated random variables drawn from a Gaussian random field. Struct Safet 2011; 33(6):317-32.

[42] Bocchini P, Frangopol DM. A stochastic computational framework for the joint transportation network fragility analysis and traffic flow distribution under extreme events. Probabil Eng Mech 2011; 26(2):182-93.

[43] Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. J Machine Learn Res 2011;12:2825-30.

[44] Cheng M, Frangopol DM. Efficient scenario analysis for optimal adaptation of bridge networks under deep uncertainties through knowledge transfer. Struct Safety 2023;100:102278.

[45] Liu L, Yang DY, Frangopol DM. Network-level risk-based framework for optimal bridge adaptation management considering scour and climate change. J Infrastruct Syst 2020; 26(1):04019037.

[46] FHWA National bridge inventory (NBI).FHWA; 1992.

[47] FDOT FDOT traffic monitoring handbook. FDOT; 2023.

[48] US geological survey UG. National water information system data available on the world wide web (USGS water data for the nation). USGS Surface-water annual statistics for wisconsin; 2016.