Concrete structures cannot efficiently perform their functions over time due to chemical and physical external effects. Thus, enhancing the relationship between repair and aged structures, and also improving the durability properties of concrete is crucial in terms of sustainability. However, high costs, negative environmental effects, and incompatibility problems occur in repair/retrofit applications. Furthermore, three-quarters of the failures in the repaired/retrofitted structures are caused by a lack of repair durability. The need for repair in pavement/bridge decks is also frequently encountered, and early-age performance problems with repair materials cause pavement/bridge decks to be unavailable for certain periods of time. Engineered Cementitious Composite (ECC) can be effectively used as repair/retrofit and pavement/bridge deck material. It also has a minimal need for repair/retrofit thanks to its high durability properties. This article presents state-of-the-art research regarding the application of ECC as a repair/retrofit and pavement/bridge deck material. Studies in the literature show that the repair/retrofit properties of ECC outperform conventional concrete and steel fiber-reinforced concrete. ECC can be a solution to high early strength and drying shrinkage problems frequently encountered in the use of repair materials. It could also be used for different repair applications such as cast, sprayed, and trenchless rehabilitation. Moreover, ECC might fulfill specific requirements for pavement, pavement overlay, tunnel pavement, airfield pavement, and bridge deck. These superior performances are attributed to ECC’s kink-crack trapping mechanism, uniquely large inelastic strain capacity, strain hardening, high tensile strain capacity, and multiple microcracking and ductile behaviors, especially bonding behavior and self-healing.

The results of a study of the contact interaction of an open shell and a chrome-plated shaft with elastomeric filler installed coaxially are presented. The considered contact system is a model of the original design of the shell damper of dry friction. The design feature is the following: the bearing link of the damper is a thin-walled cylindrical shell with a cut along the generatrix; the working body of the damper is elastomeric filler; a hollow chrome-plated shaft centers the damper elements and allows it to be used in technological processes with the presence of aggressive and abrasive-containing media. The mechanical-mathematical modeling of the behavior of the presented damper under the conditions of operational loads has been carried out. The idea of identifying the properties of a cut isotropic shell, which bends under the conditions of a nonaxisymmetric contact load, and a strongly orthotropic continuous shell is applied. As a result, dependences were obtained to determine the rigidity and the maximum allowable load of the damper. The effect of the coefficient of friction of the contact pairs elastomer-shell and elastomer-shaft on the damper performance properties has been studied. A technique for the quasi-static analysis of structural damping in non-mobile, non-conservative shell systems with deforming filler has been developed. The hysteresis loops of the damper under a nonmonotonic load are constructed, the dependence of the amount of dissipated energy on the cycle asymmetry coefficient is found. An analysis of the results obtained showed that the use of open shells in friction shock absorbers can significantly reduce their rigidity compared to solid shells and thereby reduce the resonant frequencies of the dynamic system. This circumstance makes such vibration isolators particularly attractive for use in superresonance vibrators as working modules of drilling shock absorbers and elastic hangers of sucker rods in oil and gas production.

This paper studied the distribution of stresses near damage in the form of axial surface cracks in a pipeline reinforced with a spiral-wound composite coating. The authors applied the homogenization method to determine the effective elastic characteristics of a structurally anisotropic layered package. By means of the classical momentless theory of shells, it was established that the stress state of the coated intact pipe under the pressure of the pumped product depends on the parameters of the geometry of the capacity strip, as well as on the component composition of the heterogeneous coating. The finite element method was applied to solve the problem of plane deformation of a piecewise homogeneous ring with an internal crack perpendicular to the interface. This problem assumes the linearity of the materials and the ideal mechanical contact with the layers. The effect of the composite coating and the size of the damage on the magnitudes of the energy flow into the crack tip, and on the stress intensity factor, was studied in detail. Various variants of the coating were considered, namely, winding of the coating on an unloaded pipe and reinforcement of the pipe under repair pressure.

One of the primary challenges associated with bolts, frequently used in assembly structures and systems, is the issue of torque loosening. This problem can arise due to shock and vibration, potentially leading to significant damage and structural failure. The difficulty in identifying and monitoring torque loosening arises from the variability and nonlinear effects present in bolted joints. In this paper, we proposed a machine-learning algorithm architecture designed for pattern recognition, detection, and quantification of torque loosening in bolted joints. This approach combines unsupervised and supervised machine learning algorithms to address the challenges of assessing the bolt torque loosening issue. Our algorithm utilises a damage index, calculated from the frequency response of the jointed system using the Frequency Response Assurance Criterion, as input data for the unsupervised–supervised classification algorithm. This classification ML algorithm effectively identifies and categorises instances of torque loss by analysing indirect vibration measurements, even in situations where the bolted system’s state is unknown. Additionally, we introduce a regression algorithm to quantify torque loosening levels. The results obtained from our proposed machine learning algorithm to overcome torque loosening in bolted joints show that the inherent uncertainties of a data-driven approach intrinsically influence torque-related issues. This assessment is based on experimental raw data collected under diverse test conditions for the bolted structure. We employ a range of validation and cross-validation metrics to evaluate the effectiveness and accuracy of these ML algorithms in detecting and diagnosing torque-related issues. These metrics play a crucial role in assessing the algorithms’ efficiency and precision in determining the state of the bolted connection and the corresponding torque levels with associated uncertainty quantification.

Purpose: Structural damage can significantly alter a system's local flexibility, leading to undesirable displacements and vibrations. Analysing the dynamic structure feature through statistical analysis enables us to discriminate the current structural condition and predict its short- or long-term lifespan. By directly affecting the system's vibration, cracks and discontinuities can be detected, and their severity quantified using the DI. Two damage indexes (DI) are used to build a dataset from the beam's natural frequency and frequency response function (FRF) under both undamaged and damaged conditions, and numerical and experimental tests provided the data-driven. Methods: In this paper, we present the methodology based on machine learning (ML) to monitor the structural integrity of a beam-like structure. The performance of six ML algorithms, including k-nearest neighbors (kNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Naive Bayes (NB) are investigated. Results: The paper discusses the challenges of implementing each technique and assesses their performance in accurately classifying the dataset and indicating the beam's integrity. Conclusion: The structural monitoring performed with the ML algorithm achieved excellent metrics when inputting the simulation-generated dataset, up to 100%, and up to 95% having as input dataset provided from experimental tests. Demonstrating that the ML algorithm could correctly classify the health condition of the structure.