Open Access

Bending beams and slabs are typical examples for structural elements used for reinforced concrete structures such as bridge girders, T-beams and bridge decks. Their strength related failure modes at maximum loading can be divided into bending and shear failure. The failure of beams loaded in bending can occur with or without indication. Therefore, conventional design concepts aim on failure modes with sufficient indication (e.g. large deflections or cracks), as it occurs in the case of secondary flexural compression failure. These indicating factors can also be used for Structural Health Monitoring (SHM) of civil infrastructure systems (e.g. bridges) to identify structural changes. In this context, non-destructive testing (NDT) methods offer different techniques for measuring deflections or crack formation and opening. However, profound knowledge on the determining failure modes of bending beams and their detection by NDT methods is required for the reliable application of SHM. Different NDT methods have been used in this study for analysing the load-bearing behaviour of a reinforced concrete beam in bending. The different measuring techniques are briefly described and their applicability is discussed by means of experimental results. For this purpose, the load-bearing behaviour of a reinforced concrete beam having a span of 2.75 m was investigated in a four-point bending flexural test at laboratory scale. The focus is on the characterization of determining failure modes by optical NDT and the comparison with classical measuring techniques (e.g. deformation measurements by displacement transducers). The bending beam was equipped with two single-mode (SM) sensor fibres. One fibre served as Distributed Optical Fibre Sensor (DOFS), whereas the other fibre contained Fibre Bragg Grating (FBG) sensors. In addition, optical deformation measurements using Digital Image Correlation (DIC) and Stereophotogrammetry (SP) were conducted.

Grouted connections are intensively used in offshore rigs, platforms as well as jacket and monopile offshore wind turbine structures. Being located in remote offshore conditions, these connections can experience considerable adverse loading during their lifetimes. Degradation was reported inside similar connections, which were installed in the last three decades. Grouting in the offshore sites may often be proven difficult, which eventually leads to reduced load-bearing capacity of connections in the long run. Thus, repair and rehabilitation of such connections should be planned ahead to minimize operational delays and costs. In this study, scaled grouted connections were manufactured using a novel mould, whose integrity were monitored using digital image correlation (DIC). The connections were loaded under static load to visualize the main failure pattern using distributed fibre optic sensors and acoustic emission (AE) analysis. Grouted connections were then repaired using a cementitious injectable grout. The effectiveness of the grout injection was monitored using dye penetration technique. Finally, specimens are reloaded to identify the potential of such repair for grouted connections.

Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model—and its qualitative and at times quantitative accuracy—on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces, and even the dynamics of a small protein. The model can be applied out of the box as a starting or “foundation” model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users obtain reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step toward democratizing the revolution in atomic-scale modeling that has been brought about by ML force fields.