Catastrophic Failure Research Articles

Skin thickness effects on failure mechanisms in foam infilled composite sandwich structures

Sandwich structures are often prone to catastrophic failure due to premature separation between the core and skin layers. Geometric parameters, such as the thickness of the skin and core, along with the materials used in their manufacturing, directly influence the resistance to separation between the skin and core. This study explores how variations in skin thickness affect both failure modes and maximum load capacity in sandwich structures, utilizing both experimental testing and numerical simulations. Specimens were categorized as intact and pre-debonded samples. Each specimen featured four different skin thicknesses (3, 6, 8, and 10 layers of composite laminated skins, with corresponding thicknesses of 0.68, 1.33, 1.73, and 2.1 mm respectively). The specimens incorporated a foam-filled square corrugated core and underwent 3-point bending tests. Results revealed a significant shift in the failure mode: initially observed as upper skin fracture (V-shaped failure), it transitioned to separation between skins and cores with increased skin thickness, particularly in the presence of pre-debonding. Notably, the predominant failure mode did not involve separation between the skin and core in specimens without a pre-existing crack. Furthermore, numerical simulations effectively demonstrated the accurate capture of failure modes and loads using the Hashin and cohesive zone model (CZM).

A three-year project on Structural Health Monitoring of railway bridges: main results and lessons learnt

Bridges and viaducts worldwide are threatened by ageing, increasing loads and climate-related extreme events. The situation calls for immediate actions to prevent catastrophic failures and extend the life of our infrastructural heritage. In this regard, thanks to significant efforts from academics, Structural Health Monitoring is paving its way towards application in the real world. Unlike the simple monitoring activity, which is not new in the field, SHM is more identifiable as a paradigm, encompassing several steps and involving many stakeholders. Also, the focus is not on a project level but on the network as a whole. Such a new perspective offers the opportunity – and obliges at the same time – to look for synergies among different bridges, with the aim of designing standardized guidelines to foster SHM scalability. Embracing this vision, in 2020, RFI and the Mechanical Engineering Department of Politecnico di Milano gave birth to a collaboration oriented to testing the application of Structural Health Monitoring on the Italian railway infrastructure. The project had a twofold objective: on the one hand, the realisation of an automated software to extract and deliver information from the monitoring systems installed on three pilot bridges; on the other hand, the draft of guidelines that could report the lessons learnt and generalise as much as possible the design and exploitation of SHM on railway bridges. Given the interest in the feasibility of an extensive adoption of such practice, the pilot bridges were selected to be representative of the whole network. This paper wraps up the three-year project, intending to share the experience gained in the field and the take-home messages for future applications of SHM, with a focus on the key aspects for a broad spectrum adoption of this paradigm.

Stability Analysis of Gravity Dam using Matlab Programming

Gravity dams are critical structures in civil engineering, designed to withstand the immense forces exerted by water pressure and gravity. Ensuring their stability is paramount to prevent catastrophic failures that could result in significant economic and environmental consequences. This study presents a comprehensive stability analysis of a gravity dam using MATLAB programming. The analysis focuses on evaluating the structural integrity and safety factors of the dam under various loading conditions, including hydrostatic pressure and seismic forces. MATLAB's computational capabilities are utilized to perform rigorous numerical simulations, incorporating finite element analysis (FEA) and stability criteria assessments. Key aspects of the stability analysis include the determination of critical failure modes such as sliding, overturning, and base pressure distribution. Through MATLAB programming, the study explores different dam geometries and material properties to assess their influence on overall stability. Furthermore, sensitivity analyses are conducted to investigate the impact of uncertainties in input parameters on the dam's stability margins. This provides insights into potential weak points and allows for optimization of design parameters to enhance dam safety.

The feasibility of an ultrathin dual-barrier scheme to inhibit interfacial diffusion and reactions in contact stacks of Co/NiSi/Si

Self-assembled monolayers (SAMs) and self-forming barriers (SFBs) are highly effective barriers of copper (Cu) interconnect systems. However, the barrier capability of SAMs and SFBs to contact metallization of cobalt (Co), a new important interconnect material, is yet to be explored. Here, contact layers of NiSi are grown by a two-step silicidation process on Si. Then, an all-wet SAM-seeding and electroless-plating process is used to coat (in sequence) an amine SAM and a MnO-added (only 0.1%) Co film onto NiSi/Si, using a specifically formulated N2H4-CoSO4-MnSO4 bath. The thermal stability of the Co(MnO)/SAM/NiSi/Si is evaluated, using (unalloyed) Co/SAM/NiSi/Si as a control. The 1.3 nm-thin SAM cannot protect the Co/SAM/NiSi/Si from catastrophic failure upon annealing, associated with serious Co/Si interfacial diffusion and the formation of faceted CoSi crystallites and a layered Co2Si/CoSi2 nanostructures at specific locations. By contrast, the Co(MnO)/SAM/NiSi/Si maintains an intact stack after annealing. Collaborative research results generated from (scanning) transmission electron microscopy, nanoscratch testing and surface-energy measurements suggest that the thermal stability enhancement of the whole Co(MnO)/SAM/NiSi/Si contact stack is related to the interfacial segregation of the added MnO as sub-5-nm dispersive crystalline particles, giving multiple functionalities as a SFB, Co-grain strengthening agent, and wetting/adhesion promoter.

Transverse low-velocity impact behaviors of pultruded carbon-fiber-reinforced polymer rods with tensile preloads: Experiment and simulation

Extensive research has been conducted on the replacement of steel cables with carbon-fiber-reinforced polymer (CFRP) cables to eliminate steel corrosion and self-weight problems in cable-supported bridges. The potential low-velocity impact (e.g., vehicle collision) may cause CFRP cables to experience catastrophic failure. However, little attention has been paid to the low-velocity impact on a pultruded composite rod. This article focused on understanding and modeling the transverse impact behaviors of the pultruded CFRP rod with tensile preloads (0–0.58 preload ratios). Two damage models were proposed for predicting these behaviors: one combining the fiber/matrix damage model with a cohesive zone model (Model A), and the other employing only the fiber/matrix damage model (Model B). The short-beam shear test method was proposed to identify the intended interlaminar debonding location when employing a cohesive zone model. Both proposed models were validated against the experimental results, and their applicability was given. It was found that the tensile preloading presented a ‘stiffening effect’ and changed the rod from shear stress-dominated failure to tensile stress-dominated failure.

Synergy of strength-ductility in a novel Al-Zn-Mg-Cu-Zr-Sc-Hf alloy through optimizing hierarchical microstructures

The strength improvement in the heat-treatable Al-Zn-Mg-Cu alloys is generally achieved by increasing the volume fraction of nanoprecipitates and reducing the grain size. However, utilizing one of them usually leads to a drastic decrease in ductility. Herein, we architect a hierarchical microstructure integrating bimodal grain structures, nanoprecipitates, and hard-brittle coarse particles wrapped by ductility coarse grain (CG) bands via conventional cold rolling (CR) deformation and heat treatment methods to break the strength-ductility dilemma in the Al-8.89Zn-1.98Mg-2.06Cu-0.12Zr-0.05Sc-0.05Hf (wt.%) alloy. The results reveal that the coupling of high-volume fraction (∼1.2%) nanoprecipitates, ∼52% narrow CG bands, and most coarse particles encapsulated by CG bands contribute to the 45% CR sample with outstanding overall mechanical properties (a tensile strength of 655 MPa, a yield strength of 620 MPa, and an elongation of 15.5%). Microstructure-based strength analysis confirms that the high strength relates to a trade-off between the hierarchical features, namely high-volume fraction nanoprecipitates to counterbalance the strength loss caused by grain coarsening. The excellent ductility is due to the introduction of medium CG content with a narrow width that can trigger a cross-scale strain distribution during plastic deformation, suppressing the catastrophic failure in the fine grain (FG) regions and facilitating the dimple fracture along the CG bands. This study proposes a feasible approach for tailoring hierarchical microstructures in Al-Zn-Mg-Cu alloys to achieve a superior strength-ductility combination.

Remaining Useful Life Estimation (RUL) For Critical Mechanical Components In Tractor

The proposed framework utilizes an evolutionary mathematical approach to optimize data-related parameters for estimating the Remaining Useful Life (RUL) of critical mechanical components. By integrating advanced time window techniques and considering factors like downtime and failure occurrences, it offers a comprehensive solution for RUL prediction across various industrial contexts. Continuous monitoring systems play a pivotal role by detecting component degradation early, allowing for proactive maintenance and minimizing catastrophic failures. Combining Mean Time to Repair (MTTR) and Mean Time Between Failures (MTBF) offers valuable insights into system performance, enabling proactive maintenance strategies. This review explores mathematical methodologies for predicting RUL in tractors, crucial for enhancing maintenance planning and remanufacturing engineering.

Cascading blackout severity prediction with statistically-augmented graph neural networks

Higher variability in grid conditions, resulting from growing renewable penetration and increased incidence of extreme weather events, has increased the difficulty of screening for scenarios that may lead to catastrophic cascading failures. Traditional power-flow-based tools for assessing cascading blackout risk are too slow to properly explore the space of possible failures and load/generation patterns. We add to the growing literature of faster graph-neural-network (GNN)-based techniques, developing two novel techniques for the estimation of blackout magnitude from initial grid conditions. First we propose several methods for employing an initial classification step to filter out safe ‘non-blackout’ scenarios prior to magnitude estimation. Second, using insights from the statistical properties of cascading blackouts, we propose a method for facilitating non-local message-passing in our GNN models. We validate these two approaches on a large simulated dataset, and show the potential of both to increase blackout size estimation performance.

Diagnosis of Rotor Component Shedding in Rotating Machinery: A Data-Driven Approach.

The potential for rotor component shedding in rotating machinery poses significant risks, necessitating the development of an early and precise fault diagnosis technique to prevent catastrophic failures and reduce maintenance costs. This study introduces a data-driven approach to detect rotor component shedding at its inception, thereby enhancing operational safety and minimizing downtime. Utilizing frequency analysis, this research identifies harmonic amplitudes within rotor vibration data as key indicators of impending faults. The methodology employs principal component analysis (PCA) to orthogonalize and reduce the dimensionality of vibration data from rotor sensors, followed by k-fold cross-validation to select a subset of significant features, ensuring the detection algorithm's robustness and generalizability. These features are then integrated into a linear discriminant analysis (LDA) model, which serves as the diagnostic engine to predict the probability of rotor component shedding. The efficacy of the approach is demonstrated through its application to 16 industrial compressors and turbines, proving its value in providing timely fault warnings and enhancing operational reliability.

Development of an On-Shaft Vibration Sensing Module for Machine Wearable Rotor Imbalance Monitoring

Rotor imbalance is considered to be one of the main mechanical faults of rotating machinery; it may result in bearing damage and even catastrophic system failure. Recent progress in the Internet of Things (IoT) has promoted the application of novel sensing and computing techniques in the industry, and it is promising to employ novel IoT techniques for imbalance detection to avoid potential failures. Existing sensing techniques suffer from the impact of bearing structure dynamics, loss of accuracy during their lifetime, and security risks introduced by the sensor cabling and supports, which may, in turn, interfere with the machine operations due to inappropriate design and installation. This investigation provides an on-shaft machine wearable vibration sensing technique for effectively monitoring the running state of rotors while minimizing the interference with their operations. In this work, key investigations include the following: (1) theoretical modeling and an analysis of rotor imbalance, and its measurement with an on-shaft micro-electromechanical system (MEMS) accelerometer; (2) the development of a wirelessly powered, cordless on-shaft vibration measurement (OSVM) sensor for unobtrusive sensing of the vibration of rotating shafts; (3) the in-sensor computing design for optimizing the distribution of computing resources and decreasing data transmission. The tests and evaluation of the proposed techniques were conducted with a rotor test rig to demonstrate their feasibility. The presented investigation is a typical example of applying new sensing and computing paradigms to improve the flexibility and convenience of applications, which is a good reference for related investigations and practices.

Fatigue Analysis of Prosthetic Osseointegration with a Novel Protection System

Recent advancements in osseointegration technology offer a promising alternative to traditional prosthetic limbs, leading to improved comfort and functionality for amputees. However, the primary concern lies in the risk of catastrophic failure and falling due to fractures within the osseointegration system. To address this challenge, a protective system has been designed. The new design includes an internally rod part with a notch at the bottom to ensure failure at that point, made of ductile cast iron. A cover made of Ti6Al4V alloy is placed to ensure fracture isolation within the protection nail during sudden loading, preventing unexpected falls. After conducting tensile tests for the mentioned alloys and materials, and then relying on the ground reaction force in the numerical analysis using SolidWorks software, Finite Element Analysis (FEA) simulations confirmed the effectiveness of this design, with the expected fracture point occurring in the protection nail after exceeding 54973 walking cycles, surpassing the expected maintenance lifespan of the prosthetic limb. This design represents a significant step forward in enhancing the safety and reliability of integrated prosthetic limbs, ultimately leading to improved mobility and peace of mind for amputees.Recent advancements in osseointegration technology offer a promising alternative to traditional prosthetic limbs, leading to improved comfort and functionality for amputees. However, the primary concern lies in the risk of catastrophic failure and falling due to fractures within the osseointegration system. To address this challenge, a protective system has been designed. The new design includes an internally rod part with a notch at the bottom to ensure failure at that point, made of ductile cast iron. A cover made of Ti6Al4V alloy is placed to ensure fracture isolation within the protection nail during sudden loading, preventing unexpected falls. After conducting tensile tests for the mentioned alloys and materials, and then relying on the ground reaction force in the numerical analysis using SolidWorks software, Finite Element Analysis (FEA) simulations confirmed the effectiveness of this design, with the expected fracture point occurring in the protection nail after exceeding 54973 walking cycles, surpassing the expected maintenance lifespan of the prosthetic limb. This design represents a significant step forward in enhancing the safety and reliability of integrated prosthetic limbs, ultimately leading to improved mobility and peace of mind for amputees.

Real-time pavement temperature prediction through ensemble machine learning

Pavement temperature is crucial for assessing asphalt pavement's performance and structural integrity due to its viscoelastic behavior. Currently, asphalt temperature prediction includes analytical, numerical, and statistical methods and relies on field investigation with intensive labor and time to hinder routine practice. Machine learning (ML) offers a promising solution for reliable and accurate real-time predictions by considering various dynamic environmental factors. This research employs four ensemble ML algorithms: eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), Random Forest (RF), and Adaptive Boosting (AdaBoost) to predict pavement temperature at different depths. XGB consistently outperformed other ensemble models, boasting the highest average R2 value (97.92%) and lower MAE and RMSE than LGBM, RF, and AdaBoost. XGB also significantly outperformed other commonly used models (linear regression, SVR, ANN, LSTM, and GRU) with an average reduction in errors of 35.29%, 63.75%, 42.75%, and an increase in R2 by 4.39% for MAE, MSE, RMSE, and R2, respectively. The Shapley additive explanation (SHAP) was applied to interpret the underlying influencing factors and their interactions, and revealed atmospheric temperature as the most impactful feature, contributing over 40.58% among the significant features.Furthermore, the predictive performance of XGB was enhanced by incorporating Principal Component Analysis to reduce computational consumption with average reductions of 8.98%, 16.59%, 9.01%, and 7.89% in MAE, MSE, MAPE, and RMSE. XGB with PCA emerges as the most effective and efficient model for predicting pavement temperatures at various depths to facilitate timely decision-making for preventive maintenance and rehabilitation and prevent future catastrophic failures.

Experimental Study on Rock Deformation Localization Using Digital Image Correlation and Acoustic Emission

In this study, the digital image correlation (DIC) method and acoustic emission (AE) technology were combined to study the evolution of rock deformation localization in detail. The second-order spatial–temporal subset DIC (STS-DIC) algorithm was proposed and used for measuring strongly heterogeneous deformation fields of red sandstone specimens under uniaxial compression. The evolution of the deformation field was analyzed with a focus on the deformation localization stage. The length and width of the deformation localization band (DLB) were measured, and the relationships between the relative sliding rate of the DLB, the relative opening rate of the DLB, and the AE counts were identified. Deformation localization was found to result from the rapid evolution of the strain concentration before the peak stress. The complete development of the DLB is an inducing factor for catastrophic rock failure, and the failure modes of the rock specimens were consistent with the final state of the DLB. A good correlation was identified between the AE counts and the relative displacement rate of the DLB, and the sliding rate was found to have a significant influence on the AE counts.

Remote collaborative framework for real-time structural condition assessment using Augmented Reality

Civil structures worldwide are confronted with a growing threat of structural deterioration, aggravated by various factors such as climate change, population growth, and increased traffic. The latent nature of these issues often leads to undetected vulnerabilities until a catastrophic failure occurs, resulting in substantial losses. To address this challenge, there is a critical need for improved structural monitoring, condition assessment, and maintenance practices. Traditional inspection methods, relying on visual estimation and heavy equipment for inaccessible areas, present formidable obstacles to inspectors. These methods impede the safe and fast examination of structural damage, complicating tracking of structural deterioration, and hindering efficient condition assessment. Recognizing these challenges, this paper proposes a remote collaborative framework to enhance the efficiency of structural inspections by leveraging the capabilities of Augmented Reality (AR), QR code, and 5G network. The proposed framework centers on real-time remote collaboration among on-site and off-site inspectors, aiming to elevate safety, accessibility, and overall inspection efficacy. The integration of real-time data sharing and collaboration facilitates immediate decision-making, enabling inspectors to proactively address structural vulnerabilities and prevent potential failures. This study concludes that the proposed framework effectively facilitates real-time structural condition assessment for on-site AR users. Simultaneously, off-site web users can instantly track the progression of data over time through the utilization of 5G technology. The proposed advanced AR framework effectively demonstrates real-time structural condition assessment through a lab-scale experimental beam and a full-scale bridge.

Maximizing hydropower station safety against earthquake through extreme learning machine-enabled shear waves velocity prediction

Hydropower stations are important infrastructures for generating clean energy. However, they are vulnerable to natural disasters such as earthquakes, which can cause severe damage and even lead to catastrophic failures. Therefore, it is essential to develop effective strategies for maximizing hydropower station safety against earthquakes. To evaluate the potential shear rate of surrounding rock layers, the shear wave velocity (Vs) parameter can be used as a useful tool. This parameter helps to determine the velocity at which shear waves travel through the rock layers, which can indicate their stability and susceptibility to earthquakes. This study will investigate the significance of the Vs parameter in evaluating the potential shear rate of rock layers surrounding hydropower stations and how it can be used to ensure their safety and efficiency in earthquake-prone regions. Furthermore, a novel approach is proposed in this research, which involves using extreme learning machine (ELM) technology to predict Vs and enhance the seismic safety of hydropower stations. The ELM model predicts the Vs of the soil layers around the hydropower station, a crucial factor in determining the structure’s seismic response. The predicted Vs is then used to assess seismic hazard and design appropriate safety measures. The ML-ELM model outperformed both the ELM and empirical models, with an RMSE of 0.0432 μs/ft and R2 of 0.9954, as well as fewer outlier data predictions. This approach shows promise for predicting Vs in similar environments, and future research could explore its effectiveness in other datasets and practical applications.

ATTACHMENT WITH SUTURE VIA BONE TUNNELS IS COMPARABLE BIOMECHANICALLY TO SINGLE-ROW ANCHOR SUTURE FOR REATTACHMENT OF ACHILLES TENDON TO BONE

Insertional Achilles tendinitis with considerable degeneration that failed non-operative treatment typically requires tendon debridement and reattachment to bone. It is common practice for tendons to be reattached back with anchor sutures, but this poses a challenge to patients who are not able to afford them. Bony anchorage of tendons may be performed by passing sutures through tunnels, but the strength of repair compared to by using anchors is not known. We investigated the load at clinical and catastrophic failure of these two methods of reattachment. Sixteen paired Achilles tendons along with the calcaneus were harvested from eight fresh frozen cadavers. Paired randomization was done. For the anchor suture group, two 5’0 anchors with polyethylene #2 sutures were used for reattachment whereas for the suture only group, tendons were reattached to bone using braided polyester #2 sutures via two bony tunnels. All samples were mounted on a materials testing system and preloaded at 50N for 60sec before load to failure at a rate of 1mm/sec. With the assumption that preloading has removed tendon crimp and any subsequent extension is a result of gapping at the repair site, loads at 5mm, 10mm, 15mm, and 20mm of extension were noted as well as the maximal load at failure. We found higher loads were needed to cause an extension of 5 to 20mm in the suture only group compared to the anchor suture group but these data were not significant. On the other hand, the anchor suture group required higher loads before catastrophic failure occurred compared to the suture only group, but this again is not significant. We conclude that suture only reattachment of the Achilles tendon is comparable in strength with anchor suture reattachment, and this method of reattachment can be considered for patients who do not have access to anchor sutures.

Designing Novel Endodontic Implants and Evaluation of the Stress Distribution in Maxillary Anteriors Using Finite Element Analysis.

Introduction Endodontic implants, or didontic implants, offer a promising solution for stabilizing compromised teeth with a guarded prognosis and prolonging their clinical survival rate. Despite their potential benefits, they retired out of practice due to failures that arose from the lack of a biocompatible seal and engaging in dentin. Novel designs, based on evidence-based research with the help of bioceramics, present an opportunity to overcome these challenges andhence, enhance the clinical efficacy of endodontic implants. Thus the aim of this study is to design novel endodontic implants and evaluate their stress distribution in maxillary incisors using finite element analysis (FEA). Materials and methodology FEAis a biomechanical study to assess the stress distribution and extent of displacementto assess the clinical efficacy of novel endodontic implants in maxillary anterior teeth. Three 3D models (Model 1, Model 2, and Model 3)are designed to be meshed, and material elastic properties of the tooth and periapical tissues are applied. Boundary conditions were established, and a constant axial load value of 600 N was applied at a 45° angle. The FEA analysis was done under the loading conditions to assess the stress patterns for the three 3D models in comparison to the intact tooth on the ANSYS software (ANSYS Inc, Pennsylvania). Results FEA simulations revealed the distribution of stress within the tooth structure under functional occlusal forces, as Von Misses stresses were analyzed to assess the likelihood of material yielding and failure, which was comparable to that of an intact tooth. The maximum stress of deformation was as follows: intact: 1.7589e-5 MAX; Model 1:3.3804e-6 MAX; Model 2:2.638e-5 MAX; and Model 3: 2.1986e-5 MAX. The area of stress concentrations did not occur at the interface of the coronal or apical seal, which prevented catastrophic failures. Conclusion By leveraging advanced design principles and materials, these implants offer a promising alternative to traditional approaches,particularlyin trauma cases with a poor prognosis for the survival of the teeth leading to loss of tooth. Further clinical studies are warranted to validate the efficacy and long-term success of these novel endodontic implants in diverse patient populations.

Sensitivity-matrix-independent electrical impedance tomography: Damage detection for composite materials

Carbon fiber reinforced polymer (CFRP) is widely utilized due to their superior properties. The structural health monitoring (SHM) for composite materials is crucial to prevent catastrophic failures. Electrical impedance tomography (EIT), which allows for the visualization of the conductivity distribution through the measurement of electrical parameters on the boundary, presents a potential solution for SHM. Existing studies applying EIT to CFRP concentrate on traditional linearization methods that rely on sensitivity matrix, which ignore challenges posed by non-uniform sensitivity. This paper introduces an EIT image reconstruction method that is independent of the sensitivity matrix, offering a novel way for rapid damage detection and real-time SHM. Furthermore, the domain shape transformation is achieved by conformal mapping. Simulations and experiments indicate that the sensitivity-matrix-independent EIT can avoid issues such as blurring in the central region and artifacts near the boundary caused by non-uniform distribution of the sensitivity in the CFRP damage detection.

Comparative evaluation of marginal adaptation and fatigue resistance of endodontically treated premolars restored with direct and indirect coronal restorations: an in vitro study

BackgroundAn optimum restoration for reconstructing endodontically treated teeth should provide excellent marginal adaptation, high fracture resistance as well as maximum tooth structure conservation. The purpose of this study was to evaluate the marginal adaptation and fatigue resistance of different coronal restorations in endodontically treated premolars.MethodsThirty sound maxillary first premolars were endodontically treated and received MOD cavities. Teeth were randomly allocated into three groups (n = 10) according to the type of coronal restoration: Group R: polyethylene fibers (ribbond), fibers-reinforced composite (everX posterior) and final layer of nano-hybrid composite. Group O: indirect lithium disilicate overlay and Group C: fiber-post, resin composite restoration, and lithium disilicate crown. Marginal gap assessment was performed before and after thermocycling (5000 cycles) using stereomicroscope. Samples were subjected to stepwise-stress loading starting at 200 N, and increased by 100 N in each step until failure occurred. Statistical analysis was done by One-way ANOVA followed Tukey`s Post Hoc test for multiple comparison. Paired t test was used to compare the marginal adaptation before and after thermocycling. Survival probability was evaluated by Life table survival analysis. Failure mode analysis was performed with Chi-square test.ResultsMarginal gap was significantly the lowest in group R (37.49 ± 5.05) and (42.68 ± 2.38), while being the highest in group C (59.78 ± 5.67) and (71.52 ± 5.18) in before and after thermocycling respectively (P < 0.0001). Fatigue resistance was the highest for group O (1310.8 ± 196.7), and the lowest for group R (905.4 ± 170.51) with a significant difference between groups (P < 0.0001). Crown group had the highest percentage (80%) of catastrophic failure, while, overlay group exhibited the lowest (20%).ConclusionsDirect restoration without cuspal coverage using ribbon fibers with short FRC provided better marginal adaptation than indirect overlays and crowns, but fatigue resistance wasn’t significantly improved. Adhesive ceramic overlays showed the best fatigue performance and the least catastrophic failure rate compared to both direct fiber-reinforced composite and indirect ceramic full coverage restorations.Clinical significanceIndirect adhesive overlays are a suitable, more conservative restorative option for endodontically treated teeth than full coverage restorations, especially when tooth structure is severely compromised.

Accessing versatile tensile ductility of amorphous materials by fractal nanoarchitecture design

Amorphous materials have been known to be intrinsically brittle under tensile stress. This is especially true in silicon-based and metallic-based glasses. In particular, for the latter, they quite often show promising mechanical properties superior to their crystalline counterparts. However, lacking tensile ductility strongly prohibits them from servicing the societies. Although with decades of immense research efforts, we are still waiting for a sophisticated solution. To march in this direction, in this work, we are dedicated to finding a practical method by smart fractal nano-architecture design through advanced computational modeling. This mainly aims to circumvent the intrinsic strain localization at the nano-scale to avoid catastrophic failure. By distributing the external strain to numerous self-confined nano-branches, we successfully achieve astonishing and tunable tensile ductility. This strategy proves to be very effective for different classes of amorphous materials. We found that the tensile properties of the nano-architectured glasses depend on both the constituent elements and the nanostructure. This demonstrates our flexible capability to design desired mechanical properties for a specific material at any spatial length scale. The current findings will inspire experimental realization by cutting-edge 3D-printing techniques and call for optimal design by top-notch graph neural network deep learning algorithms. This work also proposes a new ’structure’-property relationship to efficiently bridge experimental fabrication and computational design.