Prediction Of Failure Modes Research Articles

Assessment of impaired reinforced concrete dapped‐end beams: An analytical approach

AbstractReinforced concrete dapped‐end beams (DEBs) represent a unique structural type with the presence of recessed ends. The beam configuration is ideal for structural connections. However, the recessed area is structurally a disturbed region, and is vulnerable to embedded reinforcement deterioration. This study explored the potential of strength assessment of impaired DEBs via an analytical approach. A kinematics based model (KBM) was opted, and it was subjected to significant modifications to account to suit with distinct structural defects that were common to DEBs. The extended model (EKBM) was validated using experimental results available for eight beams. Both the load and failure mode predictions from the EKBM were found to be more accurate than those from the KBM, where the EKBM prediction offset was within a fair range of −23% to 15%. Furthermore, a parametric analysis was carried out to investigate the sensitivity of shear reinforcement deficiency and of reinforcement corrosion in the joint region on the beam load capacity. It revealed that the first two shear links were the most critical links. The load reduction was linearly proportional to the area reduction of reinforcement due to corrosion, however, the trend deviated from that at some combined deficiencies. The corroded reinforcement length was rather insensitive to the resulting beam strength.

Numerical Study of the Nonlinear Soil–Pile–Structure Interaction Effects on the Lateral Response of Marine Jetties

This study presents three-dimensional finite element analyses of two marine structures subjected to lateral loading to approximate environmental forces (e.g., wind, waves, currents, earthquakes). The first structure is a marine jetty supported by twenty-four piles, representative of an existing structure in Cyprus, while the second is a simplified four-pile marine structure. Soil–pile interaction is modelled using nonlinear p-y, τ-z, and q-z springs that are distributed along the piles, while steel plasticity is also considered. This study examines the relationship between failure modes, deformation modes, and plastic hinge locations with soil behaviour and soil reaction forces. It also aims at investigating the behaviour of the above structures in lateral loading and quantifying the consequences of unrealistic assumptions such as soil and steel linearity or tension-resistant q-z springs. The results indicate that such assumptions can lead to the wrong prediction of failure modes, plastic hinges, and critical elements while emphasising the crucial role of soil nonlinearity and axial pile–soil behaviour on the structural response. It is demonstrated that the dominant nonlinear sources relevant to this study, whether soil nonlinearity, plastic hinge formation, or a combination of the two, are primarily influenced by the axial capacity of soil–pile foundation systems, particularly their tensile component.

Large deformation simulation of uprooting of trees with complex root system architectures using material point method with embedded truss elements

Abstract Background and aims Urban trees in coastal cities like Hong Kong may suffer from an uprooting failure when subjected to extreme winds. A proper numerical model for tree uprooting simulation can help to select tree species or soil types that better resist uprooting failure. However, modeling tree uprooting is challenging as it is a cross-disciplinary problem involving complex root system architectures (RSAs) and large deformation of both roots and soils. This study aims to develop a hybrid numerical model that combines truss elements and material point method (MPM) to simulate the entire large-deformation uprooting process of trees with complex RSAs. Methods The tree uprooting model is developed by coupling truss elements in finite element method (FEM) with MPM. Laboratory pull-out tests using artificial roots and real root cuttings are adopted to validate the developed model. A comparative study is performed to investigate the difference between using complex and simplified RSAs in tree uprooting simulations. Results The developed model provides consistent predictions of peak load, critical displacement and failure mode when compared with results from laboratory tests. Moreover, the comparative study shows that the uprooting resistance obtained with a complex RSA is higher than that with a simplified RSA. The difference varies with the soil and root mechanical properties. Conclusion The developed hybrid model offers a novel way for simulating an entire tree uprooting process involving large deformations and complex RSAs. The study shows that using a simplified RSA to approximate the complex RSA might result in misleading failure modes.

Finite Element Modeling and Artificial Neural Network Analyses on the Flexural Capacity of Concrete T-Beams Reinforced with Prestressed Carbon Fiber Reinforced Polymer Strands and Non-Prestressed Steel Rebars

The use of carbon fiber reinforced polymer (CFRP) strands as prestressed reinforcement in prestressed concrete (PC) structures offers an effective solution to the corrosion issues associated with prestressed steel strands. In this study, the flexural behavior of PC beams reinforced with prestressed CFRP strands and non-prestressed steel rebars was investigated using finite element modeling (FEM) and artificial neural network (ANN) methods. First, three-dimensional nonlinear FE models were developed. The FE results indicated that the predicted failure mode, load-deflection curve, and ultimate load agreed well with the previous test results. Variations in prestress level, concrete strength, and steel reinforcement ratio shifted the failure mode from concrete crushing to CFRP strand fracture. While the ultimate load generally increased with a higher prestressed level, an excessively high prestress level reduced the ultimate load due to premature fracture of CFRP strands. An increase in concrete strength and steel reinforcement ratio also contributed to a rise in the ultimate load. Subsequently, the verified FE models were utilized to create a database for training the back propagation ANN (BP-ANN) model. The ultimate moments of the experimental specimens were predicted using the trained model. The results showed the correlation coefficients for both the training and test datasets were approximately 0.99, and the maximum error between the predicted and test ultimate moments was around 8%, demonstrating that the BP-ANN method is an effective tool for accurately predicting the ultimate capacity of this type of PC beam.

Prediction of Strength and Failure Modes in Reinforced Concrete Beam–Column Joints using Ensemble Machine Learning Techniques

The beam-column joint is one of the important structural elements that control the structural behaviour during seismic excitation. Moreover, failure of any of these components may lead to the partial or overall collapse of the entire structure. In view of the safety concern, there is a need to evaluate the response properties, such as failure modes and ultimate shear capacity of the beam-column joints. Conventional methods to evaluate the shear capacity of beam-column joints are usually time-consuming. Therefore, in this study, an attempt has been made to evaluate the joint shear capacity and mode of failure of an exterior beam-column joint using ensembled machine learning (ML) approaches like Decision Tree, Random Forest, AdaBoost, CatBoost, and LightGBM. The present study highlights the potential use of the CatBoost model as a useful tool that can assist in predicting the shear strength and failure mode. The results of the study reveal that this model has performed well in predicting the load-carrying capacity and shear strength with high correlation coefficients of 0.9836 and 0.9978, respectively. Moreover, it has been observed that the prediction model provided by the CatBoost algorithm exhibits the highest accuracy for classification in the failure mode of beam-column joints.

In-situ acoustic emission based technique for damage detection and identification and failure mode prediction in scarf repaired composite structures

One of the great challenges facing adhesively bonded repair procedures in the industry is the absence of a reliable structural health monitoring technique to ensure that the repaired region is intact during service. To this end, this paper proposes a novel in-situ acoustic emission-based methodology to detect and identify damages as well as to predict failure modes at early stages. In this essence, two tapered-scarf repaired plates are produced with different patch materials. The first patch consists of neat carbon fiber prepregs whereas in the second thermally exfoliated graphene oxide integrated carbon fiber prepregs are utilized. Testing the constituents of the repair system individually while monitoring their acoustic activity makes it possible to separate each damage type precisely. Hence, when the specimens of the repaired panel are tested, very detailed information is revealed regarding the current structural status of the specimen and probable failure scenarios. The effectiveness and robustness of the proposed methodology in detecting and identifying damages of varying severity in addition to predicting the failure scenario are verified in the composite panels repaired with pristine and graphene integrated patches.

Early age time-dependent mechanical properties of 3D-printed concrete with coarse aggregates

Incorporating coarse aggregate into 3D-printed concrete is essential for promoting its practical application, yet the impact on the early age time-dependent mechanical properties, crucial for the multilayer structure stability and quality, is underexplored. In this study, 3D-printed concrete with coarse aggregate (3DPCA) with varying mortar-to-coarse aggregate ratios (M/A) and coarse aggregate gradations were prepared, of which uniaxial unconfined compression and direct shear performance were experimentally investigated. The fundamental formulations between the early age mechanical parameters and the resting time of 3DPCA were characterized and then utilized in a digital model simulating actual printing. Results indicated that the 3DPCA early age mechanical properties, including compressive strength, Young's modulus, cohesion, and friction angle, increased with longer resting time and decreasing M/A, and higher proportions of aggregates above 9.5 mm. High coarse aggregate content caused three distinct compressive strength-displacement curve stages and complicated strength eigenvalue extraction. Coarse aggregate gradation affected shear properties more than content, with larger aggregates improving cohesion and friction angles. The failure mode prediction model based on ABAQUS could accurately predict the maximum collapse layer of 3DPCA that occurred in overall unstable collapse and over-expansion failure but was incapable of localized deformation.

Artificial neural network-enhanced mathematical simulation based on Carrera unified formulation for dynamic analysis and structural integrity assessment of advanced nanocomposites reinforced tunnel structures

This article presents the application of the Carrera Unified Formulation (CUF) for dynamic analysis and structural integrity assessment of advanced nanocomposite-reinforced tunnel structures. CUF offers a generalized framework that simplifies the modeling of complex structures, providing an efficient approach to address the dynamic behavior of nanocomposites. Advanced nanocomposites, which enhance mechanical performance and durability, are increasingly used in critical infrastructure such as tunnel systems. Traditional methods often fall short in capturing the intricate interactions within these materials, particularly under dynamic loads. CUF overcomes these challenges by incorporating higher-order theories and multi-scale analysis, allowing for accurate prediction of displacements, stresses, and potential failure modes. To further validate the results, an Artificial Neural Network (ANN) model is trained using simulated data, ensuring robust predictions for various loading conditions. The ANN assists in approximating the dynamic response and integrity of the structure, enabling real-time assessments with minimal computational expense. The combined CUF-ANN approach demonstrates high accuracy and efficiency, making it a reliable tool for the structural integrity assessment of nanocomposite-reinforced tunnels. This study highlights the significant potential of integrating CUF and ANN for the analysis and design of advanced engineering structures subjected to dynamic environmental conditions.

Web-Based Maintenance Prediction of Machine Conditions and Failure Modes Using Machine Learning

Machine failure may result in unplanned downtime, production losses, safety risks, and increased costs. Hence, predicting machine failures is a significant challenge faced by many industries. Predictive Maintenance (PdM) techniques can help mitigate these risks by predicting machine failures before they occur. This research, therefore, develops two supervised Machine Learning (ML) algorithms to predict failure conditions and the associated failure modes for a milling machine. Six machine features were investigated. The ML models were then developed using a four-stage process including data pre-processing, training, evaluation, and deployment. Several ML algorithms were applied then the results were evaluated using accuracy, recall, precision, and [Formula: see text]1-score. The results showed that the Random Forest algorithm was the most effective in predicting machine conditions and failure modes of the milling machine. A web application for PdM was finally developed and tested for the PdM of the milling machine. In conclusion, the developed web application including ML algorithms can support the effective PdM for the machine, which leads to enhancing its availability and improving productivity. Future research considers adopting ML algorithms for predicting machine conditions and failure types of other machines.

Influence of Fiber Volume Fraction on the Predictability of UD FRP Ply Behavior: A Validated Micromechanical Virtual Testing Approach.

Enhancing the understanding of the behavior, optimizing the design, and improving the predictability and reliability of manufactured unidirectional (UD) FRP plies, which serve as primary building blocks for structural FRP laminates and components, are crucial to achieving a safe and cost-effective design. This research investigated the influence of fiber volume fraction (vf) on the predictability and reliability of the homogenized elastic properties and damage initiation strengths of two different types of UD FRP plies using validated micromechanical virtual testing for representative volume element (RVE) models. Several sources of uncertainties were included in the RVE models. This study also proposed a modified algorithm for microstructure generation and explored the effect of vf on the optimal sizes of the RVE in terms of fiber number. Virtual tests were systematically conducted using full factorial DOE coupled with Monte Carlo simulation. The modified algorithm demonstrated exceptional performance in terms of convergence speed and jamming limit, significantly reducing the time required to generate microstructures. The developed RVE models accurately predicted failure modes, loci, homogenized elastic properties, and damage initiation strengths with a mean error of less than 5%. Also, it was found that increasing vf led to a concurrent increase in the optimal size of the RVE. While it was found that the vf had a direct influence on homogenized elastic properties and damage initiation strengths, it did not significantly affect the reliability and predictability of these properties, as indicated by low correlation coefficients and fluctuations in the coefficient of variation of normalized properties.

Theoretical prediction and experimental validation of flexural behaviour and failure modes of 3D-woven honeycomb sandwich panels

This study presents a novel approach to enhance the flexural performance of sandwich composite panels by introducing 3D-woven honeycomb (HC) composites as core material. The flexural performance and failure modes of these novel structures are compared with traditional aluminium honeycombs across various facesheet materials. Experimental findings are complemented by numerical analyses, while observed failure modes are compared with theoretical flexural failure modes. The results reveal that 3D-woven Kevlar HC cores demonstrate superior flexural stiffness and strength due to its integrated structure, while enhanced energy absorption is attributed to unique deformation mechanisms within the 3D-woven core. Failure modes observed in the experiments closely aligned with theoretical predictions, further validating the approach. Overall, the findings validate the potential of novel 3D-woven Kevlar HC cores as promising alternatives for lightweight, high-performance sandwich composite panels.

Failure mode and load prediction of steel bridge girders through 3D laser scanning and machine learning methods

AbstractCorrosion poses a significant threat to the longevity of steel bridges, impacting overall structural integrity. To effectively assess the structural condition of corroded steel bridges, conventional methods rely on visual inspections or single point measurements. To enhance and modernize this approach, this study introduces a novel framework integrating laser scanning data, computational models, and convolutional neural networks (CNNs). The CNN models are trained on a data set consisting of more than 1400 artificial corrosion scenarios generated by parameterizing real scan data from naturally corroded girders. This innovative method predicts the residual capacity and failure mode of corroded beam ends, achieving a low error rate of up to 3.3%. Unlike established evaluation procedures, the proposed evaluation framework directly utilizes post‐processed laser scanner output, eliminating the need for feature extraction and calculations.

Experimental investigation of the coupled effects of bedding planes and flaws on fracture evolution of soft bedded rocks based on 3D printing and DIC technologies

Bedded rocks are a typical geological formation with significant implications for understanding tectonic evolution, rock engineering, and geological hazard mitigation. This study introduces a novel method for fabricating bedded rocks using 3D printing (3DP) technology. By comparing the mechanical anisotropic properties and failure modes of 3DP bedded rocks with natural bedded rocks, we validate the feasibility of this approach. Compression tests are conducted on 3DP bedded rocks containing a single flaw to investigate the coupled effects of bedding planes and flaws on the mechanical properties. Digital image correlation (DIC) is employed for full-field deformation measurement during loading, enabling the study of deformation and crack evolution patterns. The results indicate that bedding planes dominate the brittleness of 3DP bedded rocks containing a single flaw. Significant plastic characteristics and prolonged nonlinear deformation stages are observed when the bedding angle (α) is between 0° and 45°, while brittleness becomes apparent beyond 45°. Both strength and elastic modulus are decided by bedding planes and initial flaws. By comparing stress–strain curves and rate of effective variance change (REVC)-strain curves, crack initiation can be quantitatively identified, revealing a linear correlation between strength and stress values at crack initiation points. The bedding plane also dominates crack propagation and failure modes: when α ≤ 30°, cracks propagate through the bedding, indicating tensile failure; when 45° ≤ α＜90°, cracks propagate along the bedding, indicating tensile-shear or shear failure; and when α = 90°, cracks propagate along bedding planes, indicating tensile failure. The findings of this study provide insights for explaining and predicting failure modes in engineering projects involving bedded rock formations, such as slopes, tunnels, and coal mines.

Study on the lower head failure in severe accidents Part II: Thermal-mechanical coupling behavior analysis on CAP1400 reactor lower head

Under severe accident conditions with core meltdown, large amounts of molten material relocate to the lower head at high temperatures. Significant thermal and mechanical loads may lead to failure of the reactor pressure vessel (RPV) lower head, spreading radioactive fission products. Lower head failure time and location are crucial for accident mitigation and consequence assessment. This study developed a thermodynamic failure analysis model for the lower head by investigating material failure mechanisms, coupling this into an Integrated Severe Accident Analysis code (ISAA), and conducting experimental validation and applied research. Part I mainly introduces the thermodynamic analysis module development, validation against OLHF experiments, and preliminary failure criteria analysis. Results indicate the model accurately predicts lower head failure behavior, and Larson-Miller life fraction and Hedl-Dorn parameter criteria accurately assess lower head integrity. Part II presents applied research after validation and preliminary analysis. Using the improved ISAA, melt pool behavior and lower head thermal–mechanical response were analyzed for a CAP1400 small break loss-of-coolant accident (SBLOCA), assuming multiple safety system failures. Results indicate the bottom of the lower head melt pool didn’t form a hard shell due to high heat release rate, only an oxide shell layer around the second layer. Decay heat from melt pool components cannot be dissipated, leading to lower head failure. Further External Reactor Vessel Cooling (ERVC) heat transfer model analysis and research are needed in ISAA. The two developed mechanical analysis models predict material failure at high temperatures, and the timing and location of lower head failure are consistent, at approximately 16,000 s at 25.38°±5.47° on the lower head. The predicted failure mode is similar to Part I validation, indicating both Larson-Miller life fraction and Hedl-Dorn parameter criteria provide consistent lower head integrity judgments.

Failure prediction of an open-hole laminate under compression

Failure prediction of open-hole laminates under compression still remains a great challenge. A number of unique mechanics theories for composites developed by the author are successfully applied to analyze in plane compression induced various failures of the notched laminates in this paper. A buckling mode must be incorporated into the analysis of the compression induced delamination, and the rules for selecting a scale factor for the buckling analysis through ABAQUS are established. To simulate delamination, the interlaminar matrix stress modification method is applied. A more pertinent criterion for delamination is proposed. When and where is delamination initiated, how can the initiated delamination be propagated and how much is the delaminated area to be attained can be easily reproduced. Intralaminar failures are estimated based on Bridging Model and the matrix true stress theory. The ultimate strength of a notched laminate is assumed when any primary layer element outside neighborhoods of the stress singularity and weak singularity points firstly attains an ultimate failure. A limited, if not the minimum, number of inputs are required all measurable independently and following existing standards with no data calibration. Except for the pre-buckling analysis, no iteration is needed for prediction of all the other failures. The predicted failure modes and ultimate compressive loads of several laminates with single or double holes agree well with our measured counterparts.

Ballistic resistance of 7XXX laminate aluminum alloy plates and base alloys subjected to different projectiles

The ballistic impact of two 7XXX aluminum alloys, 7A52-T6 and 7A62-T6, as well as their combination in a 7B52 laminate plate, was simulated and experimentally tested. Three types of projectiles were utilized hard ogival nosed, hard blunt nosed, and 304 steel core projectiles to investigate the ballistic resistance and failure mechanisms of the aluminum alloy targets. Additionally, an existing modified Gurson-Tvergaard-Needleman (GTN) model incorporating shear, void growth, and spalling failure mechanisms, was employed to simulate the ballistic impact process, utilized to analyze energy dissipation during impact. The results reveal that the 7A52 alloy only experiences ductile hole expansion failure, whereas the 7A62 high-strength aluminum alloy undergoes spalling and fragmentation subsequent to penetration, leading to potential impact hazards. Due to its backing layer being the highly ductile 7A52 alloy, the laminate plate will not produce fragments after penetration. The improved GTN model provided better predictions of the impact failure modes of the aluminum alloys. Concerning impact performance, when subjected to hard ogival projectiles, the laminate alloy did not enhance material impact resistance. However, when impacted by blunt-nosed projectiles, the laminate alloy exhibited a 27 % improvement over the 7A52 plate and an 11.9 % increase over the 7A62 plate. Furthermore, when impacted by 304 steel core ogival projectiles, the laminate alloy demonstrated over a 41.3 % improvement compared to the 7A52 plate and over a 15.5 % increase compared to the 7A62 plate. The laminate plate's high ballistic performance was attributed to the increased plastic deformation energy after spalling failure, while ensuring high energy absorption during ductile hole expansion penetration.

Cracking of soft collagenous tissues under suture retention

Soft collagenous tissues consist of collagen fibers and soft matrix, such as skin, muscle, and heart valve, etc. Suture of the tissues is ubiquitous in surgery, which may cause fracture of tissues due to inevitably introduced defects. However, the cracking mechanism of soft collagenous tissues under suture retention remains elusive. Herein, we use bovine pericardium as a model tissue to study the suture related fracture considering various fiber orientations. Fracture tests reveal distinct toughness and fracture modes of the tissue depending on the fiber orientation, such as crack propagation and crack deflection. The maximum J-integral criterion has been established to predict the direction of crack propagation in the tissue. We further conduct suture retention test to evaluate the suture resistance of tissues, and find two different failure modes: fracture and cutting. Microscopic examination of the fracture surface demonstrates that the tissue can rupture with long fiber pullout, cut through matrix failure, or cut by a combination of fiber break and pullout. The cohesive element model is used to analyze the cutting mode. The predicted failure modes and force-displacement curves match well with experiments. This paper may provide guidance to surgical operations and benefit new artificial collagenous materials.

Hybrid Machine Learning Algorithms for Prediction of Failure Modes and Punching Resistance in Slab-Column Connections with Shear Reinforcement

This study presents a data-driven model for identifying failure modes (FMs) and predicting the corresponding punching shear resistance of slab-column connections with shear reinforcement. An experimental database that contains 328 test results is used to determine nine input variables based on the punching shear mechanism. A comparison is conducted between three typical machine learning (ML) approaches: random forest (RF), light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost) and two hybrid optimized algorithms: grey wolf optimization (GWO) and whale optimization algorithm (WOA). It was found that the XGBoost classifier had the highest accuracy rate, precision, and recall values for FM identification. In testing, WOA-XGBoost has the best accuracy in predicting punching shear resistance, with R2, MAE, and RMSE values of 0.9642, 0.087 MN, and 0.126 MN, respectively. However, a comparison between experimental values and calculated values derived from classical analytical methods clearly demonstrates that existing design codes need to be improved. Additionally, Shapley additive explanations (SHAP) were applied to explain the model’s predictions, with factors categorized according to their impact on failure modes and punching shear resistance. By modifying these parameters, punching resistance can be improved while reducing unpredictable failure. With the proposed hybrid algorithms, it is possible to determine the failure modes and the punching shear resistance of slabs during the preliminary stages of the construction.

An improved tensile strength and failure mode prediction model of FDM 3D printing PLA material: Theoretical and experimental investigations

3D-printed polylactic acid (PLA) is being increasingly used in practical engineering. In the past, a certain number of models were developed to describe the tensile strength of PLA specimens. However, two opposite tensile strength variation trends with raster angle varying were observed in previous experiments, which cannot be reasonably explained by the current tensile strength models. To resolve this issue, an improved tensile strength and failure mode prediction model is proposed based on the assumption of transversely isotropic material. Corresponding experiments of PLA specimens have also been performed to validate the accuracy of the proposed model. It has been demonstrated that this model could accurately predict the tensile strength and failure mode of 3D printing PLA material. In addition, the opposite strength trends mentioned above with raster variation could also be perfectly explained using the improved prediction model.

Feature extraction for artificial intelligence enabled food supply chain failure mode prediction

The Farm to Fork Strategy of the European Commission is a contingency plan aimed at always ensuring a sufficient and varied supply of safe, nutritious, affordable, and sustainable food to citizens. The learning from previous crises such as COVID-19 indicates that proactive strategies need to span numerous levels both within and external to food networks, requiring both vertical and horizontal collaborations. However, there is a lack of systematic performance management techniques for ripple effects in food supply chains that would enable the prediction of failure modes. Supervised learning algorithms are commonly used for prediction (classification) problems, but machine learning struggles with large data sets and complex phenomena. Consequently, this research proposes a manual approach to feature extraction for artificial intelligence with the aim of reducing dimensionality for more efficient algorithm performance, and improved interpretability/explainability for benefits in terms of ethics and managerial decision-making. The approach is based on qualitative comparative analysis informed by in-depth case knowledge which is refined through Boolean logic, yielding solutions that reflect complex causality as opposed to single failure point modes. Two case exemplars are presented to support the proposed framework for implementation: export readiness of dairy supply chains under the Russia-Ukraine war, and egg supply chain sustainability during COVID-19 lockdown in the United Kingdom.