This study evaluates the performance of damaged concrete beams retrofitted with a purpose-designed mechanochromic composite, which provides structural reinforcement and visual feedback for structural health monitoring (SHM). The retrofitting process utilizes externally bonded reinforcement (EBR) on pre-damaged concrete prisms. The mechanochromic composite, a thin-ply hybrid material made of unidirectional ultra-high modulus (UHM) carbon/epoxy and S-glass/epoxy layers, changes color to indicate structural overload when the UHM carbon layer fractures due to excessive strain. Eighteen concrete specimens were prepared and subjected to four-point bending tests, assessing various combinations of damaged, undamaged, retrofitted, and non-retrofitted configurations. Results showed that the mechanochromic composite functions effectively as both a passive visual sensor and reinforcement. For instance, a 5 % crack depth reduced load-bearing capacity by 30 %, however, retrofitting with the mechanochromic composite improved load-bearing capacity by up to 208 % compared to undamaged beams. The study further discusses the effects of different damage levels on load-bearing capacity through flexural strength, load-displacement curves, and failure modes.

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Structural fatigue can lead to catastrophic failures in various engineering applications and must be properly monitored and effectively managed. This paper provides a state-of-the-art review of recent developments in structural fatigue monitoring using piezoelectric-based sensors. Compared to alternative sensing technologies, piezoelectric sensors offer distinct advantages, including compact size, lightweight design, low cost, flexible formats, and high sensitivity to dynamic loads. The paper reviews the working principles and recent advancements in passive piezoelectric-based sensors, such as acoustic emission wave and strain measurements, and active piezoelectric-based sensors, including ultrasonic wave and dynamic characteristic measurements. These measurements, captured under in-service dynamic strain, can be correlated to the remaining structural fatigue life. Case studies are presented, highlighting applications of fatigue life monitoring in metals, polymeric composites, and reinforced concrete structures. The paper concludes by identifying challenges and opportunities for advancing piezoelectric-based sensors for fatigue life monitoring in engineering structures.@en

This study proposes a machine learning (ML) model to predict the displacement response of high-rise structures under various vertical and lateral loading conditions. The study combined finite element analysis (FEA), parametric modeling, and a multi-objective genetic algorithm to create a robust and diverse dataset of loading scenarios for developing a predictive ML model. The ML model was trained using a recurrent neural network (RNN) with Long Short-Term Memory (LSTM) layers. The developed model demonstrated high accuracy in predicting time series of vertical, lateral (X), and lateral (Y) displacements. The training and testing results showed Mean Squared Errors (MSE) of 0.1796 and 0.0033, respectively, with R2 values of 0.8416 and 0.9939. The model’s predictions differed by only 0.93% from the actual vertical displacement values and by 4.55% and 7.35% for lateral displacements in the Y and X directions, respectively. The results demonstrate the model’s high accuracy and generalization ability, making it a valuable tool for structural health monitoring (SHM) in high-rise buildings. This research highlights the potential of ML to provide real-time displacement predictions under various load conditions, offering practical applications for ensuring the structural integrity and safety of high-rise buildings, particularly in high-risk seismic areas.@en

The complexity of drilling carbon fiber reinforced polymers (CFRP) requires accurate predictive models. This study addresses the challenge using an ensemble machine learning (ML) approach with stacked generalization. The model captures the relationships between key input variables—such as graphene nanoplatelet (GNP) content, ultrasonic assistance, tool type, stacking sequence, and feed rate—and output parameters, specifically thrust force and delamination. A nested feature scoring (NFS) method was employed for importance analysis, revealing tooling type and feed rate as key features for minimizing delamination and reducing thrust force, respectively. The machinability results revealed that ultrasonic drilling lowered thrust force by improving chip evacuation and reducing fiber breakage. HSS tools with cobalt content, alongside symmetrical stacking sequence, helped to further minimize both thrust force and delamination. However, the inclusion of GNPs led to an increase in thrust force and delamination, attributed to the increased strength of the CFRP/GNP composite. The process involved meticulous training, resulting in four optimal-fit models serving as inputs for the stacked meta-model. Iterative enhancements fortified the ensemble robustness, with fine-tuning of hyperparameters through Bayesian optimization. The ensemble superiority over individual models manifested in a remarkable reduction of mean absolute error (MAE) and root mean squared error (RMSE) by up to 97% and 124% for delamination, and 205% and 154% for thrust force, compared to the best base learner. Visual and statistical assessments effectively illuminated the intricate interactions between variables in the drilling process. The methodology resulted in a highly adaptable predictive model with applications across diverse manufacturing contexts.@en

A sensor for visualizing the fatigue load cycles was designed, fabricated, and tested. The sensor is made of a glass/carbon hybrid composite and utilizes the delamination length at the glass/carbon interface as an indicator for fatigue cycles. Appropriate design parameters were obtained by performing finite element analysis on the delamination development at the interface between the glass and carbon layers. Hybrid sensors with different carbon layer thicknesses were manufactured, attached to glass/epoxy substrates, and tested under fatigue loading. The predicted results based on the Paris law for crack extensions in one configuration are compared with the experiments for a different configuration to illustrate the efficacy of the approach.

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The study considers the delamination resistance of carbon/epoxy laminates modified with Thermoplastic Nanoparticles of Polysulfone (TNPs). A new electrospinning nanofiber technique was utilized to convert polysulfone polymer into nanoparticles and uniformly disperse them within the resin. Fracture toughness was evaluated under loading modes I and II. In mode I, the toughness (GIC) increased significantly from 170 to 328 J/m² with TNPs incorporation. However, mode II showed minimal change, with GIIC values of 955 J/m² for virgin and 950 J/m² for TNPs-modified specimens. Scanning Electron Microscopy (SEM) was employed to depict the influence of TNPs on damage characteristics and crack propagation patterns. In mode I, crack deviation enhanced toughness as TNPs bypassed the PSU, while in mode II, cracks propagated through TNPs, resulting in particle smearing on the epoxy surface. This highlights TNPs' potential to modify the fracture toughness in mode I loading, but their effect is constrained in mode II loading scenarios.@en

Polymer composite laminates have established themselves as essential materials across a wide type of industrial fields because of their specific mechanical properties such as high strength and low weight. Among the main issues they face is susceptibility to delamination damage. This comprehensive review paper investigates various damage mechanisms and associated phenomena that obvious during delamination within polymer composite laminates. Delamination can primarily arise in Mode I, Mode II and mixed Mode I &II loading scenarios. Notably, the damage features can vary significantly between these conditions. This paper aims to characterize and identify delamination-dominated damage features by conducting a comprehensive examination of the parameters that influence these features, all based on an extensive literature review and utilizing fractography analysis. The findings of this review illustrate the valuable insights that can be obtained from delamination fracture surfaces through the utilization of fractography images and the examination of damage features. For instance, it is possible to recognize details such as determining of global crack growth direction, calculating the rate of fatigue crack growth, and anticipating of strain energy released rate. This deeper understanding aids in pinpointing the key factors contributing to delamination damage. It could offer valuable insights for designing composites resistant to delamination. Additionally, it may assist in determining the underlying causes of catastrophic failures in tragic events.

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Engineering structures, such as bridges, wind turbines, airplanes, ships, buildings, and offshore platforms, often experience uncertain dynamic loadings due to environmental factors and operational conditions. The lack of knowledge about the load spectrum for these structures poses challenges in terms of design and can lead to either over-engineering or catastrophic failure. This research introduces a robust and innovative device, analogous to a "Fitbit" for structures, capable of measuring complex loading conditions throughout the structure's lifespan. The proposed approach involves developing a middleware, referred to as an "extension," which facilitates the transfer of mechanical deformation to a piezoelectric sensor. This approach overcomes challenges associated with directly attaching piezoelectric sensors to the structure's surface such as rupture possibility in higher strain and attaching on rough surfaces. The feasibility study primarily focuses on validating the performance of the extension and monitoring variation trends. The ultimate objective is to develop an Internet of Things (IoT) sensor node capable of measuring applied cyclic loads. To achieve this goal, an electronic system and embedded software will be developed to capture the complex load spectrum and convert it into a fatigue damage index for predicting the structure's fatigue life. The collected data will be transmitted to the user through a wireless communication platform. The proposed sensor design is versatile, allowing for both attachment and embedding and is demonstrated here for monitoring fatigue in engineering structures.

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Visual inspection is one of the most common nondestructive evaluation techniques that is performed through vision. This chapter will summarize visual inspection principles for detecting impact damage in composite materials. Effective parameters and available standards for visual inspection of low-velocity impact damage are introduced, and the relevant challenges are discussed. Current developments in improving visual inspection, including the use of artificial intelligence algorithms, as well as smart coating layers for improved visibility of impact damage are reported. Recent case studies are included, and future research directions are discussed.

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The present paper reports an overview of mechanochromic self-reporting thin-ply hybrid composite sensors, which are designed to visually indicate overload in structures. These sensors, made from combinations of high-strain and low-strain materials, change appearance earlier than the final fracture, providing a clear visual cue of damage like delamination or strain overload. They can be used for various applications, for example, overload monitoring, and to detect barely visible impact damage in composites.@en

A new method of creating in-plane combined tension-shear stress states using only tensile loading is proposed. Thin-ply angle-ply carbon/epoxy laminates are sandwiched between unidirectional glass layers to eliminate any stress concentration around the samples ends and gripping zone. Use of the thin plies successfully suppresses early occurrence of other modes of failure i.e. matrix cracking and free-edge delamination, so the first mode of failure is fibre failure in the angle-ply carbon sub-laminate. Compared with other methods of creating combined tension-shear stresses, the proposed technique has a simpler geometry, is significantly easier and cheaper to manufacture and test. Therefore, it provides more repeatable low-scatter experimental results. The obtained experimental results showed that the presence of in-plane shear stresses did not have a significant impact on the tensile fibre-direction failure strain of the tested carbon/epoxy laminate, suggesting that even at high shear stresses, the longitudinal tensile strength of the carbon/epoxy laminate is not significantly reduced.

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This paper investigates the structural performance of flat double-layer grids with various constitutive units, addressing a notable gap in the literature on diverse geometries. Six common types of flat double-layer grids are selected to provide a comprehensive comparison to understand their structural performance. Parametric models are built using Rhino and Grasshopper plugins. Single- and multi-objective optimization processes are conducted on the considered models to evaluate structural mass and maximum deflection. The number of constitutive units, the structural depth, and the cross-section diameter of the members are selected as design variables. The analysis reveals that the semi-octahedron upon square-grid configuration excels in minimizing structural mass and deflection. Furthermore, models lacking a full pyramid form exhibit higher deflections. Sensitivity analyses disclose the critical influence of the design variables, particularly highlighting the sensitivity of structural mass to the number of constitutive units and cross-section diameter. These findings offer valuable insights and practical design considerations for optimizing double-layer grid space structures.@en