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• Digital twin solution is demonstrated for a CHS X-joint under fatigue loading. • Digital twin is capable of accurately identifying, locating and quantifying cracks. • Relationship between crack size and nearby strains is revealed via substructuring. • Crack diagnosis is performed with affordable strain sensors. • Physics-informed model is validated through strain-interfaced physical twins. This paper aims to introduce a strain-interfaced local digital twin solution for a welded circular hollow section (CHS) X-joint subjected to brace axial loading. The solution comprises a series of machine learning algorithms to (1) identify the presence of cracks, (2) locate the cracks and (3) quantify the extent of cracking. These algorithms make use of strain readings in the vicinity of the crack to perform the diagnosis, representing a remote sensing methodology, thereby eliminating physical inspections. The validation of the proposed methodology includes two experiments – one each in the high and low cycle fatigue regime – demonstrating its wide scale applicability. The success of these experiments highlights the strong potential of affordable strain sensors in crack diagnosis assessments for CHS joints. [ABSTRACT FROM AUTHOR]

• A data-driven method is proposed to evaluate the ultimate response of masonry arches. • Machine learning metamodels, in the form of artificial neural networks, are adopted. • Numerical datasets using Matlab, Python and FEM software are generated. • A fast and accurate prediction of the collapse mode and ultimate load is achieved. • Source Matlab, Python files and generated datasets accompany the article. A data-driven methodology is proposed, for the investigation of the ultimate response of masonry arches. Aiming to evaluate their structural response in a computationally efficient framework, machine learning metamodels, in the form of artificial neural networks, are adopted. Datasets are numerically built, integrating Matlab, Python and commercial finite element software. Heyman's assumptions are adopted within non-linear finite element analysis, incorporating contact-friction laws between adjacent stones, to capture failure in the arch. The artificial neural networks are trained, validated, and tested using the least square minimization technique. It is shown that the proposed scheme can be used to provide a fast and accurate prediction of the deformed geometry, the collapse mechanism and the ultimate load. Cases studies demonstrate the efficiency of the method in random, new arch geometries. Relevant Matlab/Python scripts and datasets are provided. The method can be extended towards structural health monitoring and the concept of digital twin. [ABSTRACT FROM AUTHOR]