Prediction Of Failure Modes Research Articles

Failure modes of reinforced concrete walls under cyclic loading: Experimental study and prediction method

Failure mode identification and prediction of RC walls are of critical importance for seismic strengthening of existing members and target quantification of performance-based seismic design. This paper aims to better understand the failure modes of RC walls under cyclic loading. Firstly, cyclic test was conducted to examine the effect of reinforcement strength (fy = 476 MPa vs 641 MPa), axial load ratio (0.1 vs 0.2), and shear demand to capacity ratio (Vf/vs = 0.38 vs 1.25) on the failure modes of RC walls. Then, the existing experimental data of 168 wall specimens was collected, and the statistical analyses were carried out to obtain the critical parameters for different failure modes, which were used to establish the prediction method for failure modes. Finally, the accuracy of the prediction method was evaluated by the experimental data. The study results show that the larger axial load can both increase the likelihood of concrete crushing as well as various out of plane buckling modes; shear demand to capacity ratio is a critical parameter to affect the failure process and mode of RC walls. The statistical analyses show that when shear span ratio (λ) ≥ 2.7 and Vf/vs ≤ 0.60, the wall specimens fail in flexure; when Vf/vs ≥ 1, or λ ≤ 1.1 and Vf/vs ≥ 0.80, the wall specimens fail in shear, and the wall specimens not within the above ranges fail in flexure-shear mode. The evaluation results show that the prediction method has a reasonable accuracy.

Seismic performance assessment of corroded RC columns based on data-driven machine-learning approach

Corrosion of steel reinforcements is a major factor that will adversely affect the seismic performance of the reinforced concrete (RC) columns. This paper investigates the application of machine learning (ML)-based approach for seismic failure mode and maximum bearing capacity prediction for corroded RC columns. A comprehensive database consisting of 180 cyclic tests of corroded RC columns are collected. Six ML algorithms including three single learning methods (k-Nearest neighbors, Decision tree, Artificial neural network) and three ensemble learning methods (Random forest, AdaBoost, CatBoost) are selected to develop the predictive model. The performance of the six models are evaluated and the application of ML-based approaches for life-cycle seismic performance assessment of RC column is demonstrated with a case-study column. The results show that the Random forest and CatBoost models have the best performance for seismic failure mode prediction with an accuracy of 89%. The best model for bearing capacity prediction is the CatBoost model which has a R2 of 0.92, and the CatBoost model is superior to the traditional mechanism-based code models for bearing capacity prediction. The ML-based models can conveniently predict the seismic failure mode and bearing capacity of RC columns in its life-cycle context without complicated numerical simulations or theoretical calculations.

Bending properties of sandwich sheets with metallic face sheets and additively manufactured 3D CFRP lattice cores

• Sandwich sheets with representative shell-lattice, truss-lattice and plate-lattice cores are proposed. • Effects of core topologies on mechanical properties and bendability of sandwich sheets are clarified. • Theoretical predictions of failure modes during bending of sandwich sheets with 3D lattice cores. • Bendable sandwich sheets with 3D lattice cores are successfully designed and produced. • Bendable sandwich sheets with multifunctional application potentials. To improve structural performance, bendability and potential functionality of metal-based sandwich sheets, the 3D lattice cores were sandwiched between thin metallic face sheets. The bending properties of sandwich sheets with 3D printed carbon fiber reinforced plastic (CFRP) lattice cores originating from basic topologies of spherical shell, tetrahedral truss and tetrahedral plate were systematically investigated. The effects of core topologies, core relative densities, core heights, face sheet thicknesses on structural properties were clarified. The common failure modes during bending of sandwich sheets, such as face buckling and shear failure of core, were deeply investigated by theoretical models and experimental tests. At a prescribed radius of 60 mm, the sandwich sheets with the bilayered dome, octet truss and plate lattice cores of relative density of 50 %, height of 6 mm and face sheet thickness of 0.5 mm are bendable. This study demonstrates the feasibility of design and production of bendable sandwich sheets with 3D lattice cores based on the theoretical and experimental validations. The designed bendable sandwich sheets can be adopted to replace conventional heavy monolithic metal sheets in various engineering applications, which is anticipated to fulfill the high demand for lightweight functional materials.

Machine learning driven seismic performance limit state identification for performance-based seismic design of bridge piers

Performance-based earthquake engineering requires the identification of different damage states in structural components. Bridge piers are one of the most critical components in the bridge system that dictate the overall performance of bridges under seismic loading. The ability to predict different damage states of a bridge pier following an earthquake can be useful in restoring service or prescribing appropriate remediation. Machine learning techniques are becoming attractive in earthquake engineering for predicting failure modes of buildings and bridges. This study implements several machine learning techniques to capture different performance limit states such as spalling, core crushing, and bar buckling of reinforced concrete bridge piers under seismic loading. Using experimental data from the large scale testing of bridge piers, this study compares different machine learning techniques for predicting drift capacity performance limit states of circular and rectangular bridge piers. Model tuning using grid search algorithm and 5-fold crossvalidation is performed to optimize model predictions over a wide tuning parameter search space. The efficiency of different methods is compared using a reserved 20% testing dataset to determine the optimal machine learning regression method for each limit state. This study compares the respective bar buckling and concrete spalling drift limit state models against existing physics-based empirical models, where the machine learning models are found to have significantly improved accuracy and precision.

A Review of Predictive and Prescriptive Offshore Wind Farm Operation and Maintenance

Offshore wind farms are a rapidly developing source of clean, low-carbon energy and as they continue to grow in scale and capacity, so does the requirement for their efficient and optimised operation and maintenance. Historically, approaches to maintenance have been purely reactive. However, there is a movement in offshore wind, and wider industry in general, towards more proactive, condition-based maintenance approaches which rely on operational data-driven decision making. This paper reviews the current efforts in proactive maintenance strategies, both predictive and prescriptive, of which the latter is an evolution of the former. Both use operational data to determine whether a turbine component will fail in order to provide sufficient warning to carry out necessary maintenance. Prescriptive strategies also provide optimised maintenance actions, incorporating predictions into a wider maintenance plan to address predicted failure modes. Beginning with a summary of common techniques used across both strategies, this review moves on to discuss their respective applications in offshore wind operation and maintenance. This review concludes with suggested areas for future work, underlining the need for models which can be simply incorporated by site operators and integrate live data whilst handling uncertainties. A need for further focus on medium-term planning strategies is also highlighted along with consideration of the question of how to quantify the impact of a proactive maintenance strategy.

Quantification of the resistance modeling uncertainty of 19 alternative 2D nonlinear finite element approaches benchmarked against 101 experiments on reinforced concrete beams

AbstractNineteen 2D nonlinear finite element analysis (NLFEA) solution strategies were benchmarked against a wide variety of 101 experiments on reinforced concrete beams failing in bending, flexural shear, or shear compression. The relatively high number of solution strategies was motivated by the conviction that choices for the constitutive models, the finite element kinematics and equilibrium settings will interact, and must therefore be tested in conjunction. Modeling uncertainty distribution parameters are presented for the 19 solution strategies, using all beams, and using beams with and without stirrups separately. The resulting statistics are discussed against the correctness of the simulated failure modes and failure loads, revealing that rotating crack models perform well for the relatively ductile failures in beams with stirrups, while fixed crack models perform better for the more brittle failures in beams without stirrups. The tailored solution strategies that predict failure modes correctly, imply a log‐normal distribution of the modeling uncertainty with relatively low coefficients of variation. The outlook is that these estimates of the statistical properties of the modeling uncertainties could serve as a basis within safety formats.

Evaluation of Existing Bond‐Slip Relations for CFRP‐Steel Joints and New Model for Linear and Nonlinear Adhesives

Existing bond‐slip (τ‐s) relations for fibre‐reinforced polymer (FRP)‐steel joints employ different shapes and mathematical expressions, inferring that their predictions of failure load and mode, and other interface properties, might be inconsistent or inaccurate. In this study, predictions of four widely used τ‐s relations are evaluated using a large experimental database of 78 double‐lap FRP‐steel specimens. To facilitate the evaluation process, a robust finite element (FE) model is developed for each test, implementing data from either of the existing τ‐s relations to define the FRP‐steel interface. Comparisons between test and FE results indicated that the existing τ‐s models were unable of predicting the ultimate load (Pu) and effective bond length (Leff) of FRP‐steel joints, or the relation between Pu and bond length and that between Leff and FRP modulus (Ef). A new τ‐s model is developed based on an inverse FE simulation, comparison with experimental results, and regression analysis. It considers the effects of Ef, the type of FRP reinforcement (sheet or plate), and applies to both linear and nonlinear adhesives. The model predictions were validated by comparing with results from small bond tests and large FRP‐strengthened steel beams tested under bending, yielding excellent results for Pu, failure mode, and all other interfacial properties.

Predicting failure modes and load-capacity of fiber-reinforced polymer rods in adhesively bonded anchorages based on numerical modeling

The tensile properties of fiber-reinforced polymer (FRP) rods in adhesively bonded anchorages are expected to be studied in detail. Therefore, the study aims to predict the failure modes and load-bearing capacity (load-capacity) of FRP rods subjected to direct tensile force. The cohesive zone models were employed to evaluate the interfacial bond between materials. Firstly, two representative volume element models of fibers and matrix were proposed to predict engineering constants and strengths of the FRP material in three dimensions. Then, the main simulation, including the FRP rod, filling material, and steel tube, was carried out to analyze FRP rods under the variation of cohesive zone model parameters. The load-capacity, failure modes, shear-lag effect were predicted based on the maximum stress criterion. The results revealed that the FRP material strengths enforce the failure in two modes associated with the transverse and longitudinal directions of FRP rods. In addition, diameter is a significant factor that increases the shear-lag effect and reduces the tensile strength of the FRP rods. The numerical simulation provided a new method to predict the load-capacity of FRP rods.

Novel 2D strain-rate-dependent lamina-based and RVE/phase-based progressive fatigue damage criteria for randomly loaded multi-layer fiber-reinforced composites

Two implicit progressive fatigue damage models that rely on new equivalent-damage and equivalent-stress criteria are presented for the prediction of various failure modes of the composites. The criteria are coupled with lamina-based and representative-volume-element-based damage progression approaches. The common concepts of residual strength and residual stiffness are revisited and modified. A fatigue life assessment algorithm that incorporates the strain-rate-dependence of the fatigue strengths and stiffnesses, and random and asynchronous changes of the stress components, distinct mean values, and phase shifts of the stress components is employed. New ideas and new post-processing procedures are employed in the current research. It is the first time that the significant impacts of the strain-rate-dependence of the properties of the composites on stress and fatigue life analyses are investigated. Results of the proposed fatigue criteria are first implemented to a composite plate with a complex lamination scheme under a random transverse load and the predicted fatigue lives are verified by the experimental results. Then, these criteria are implemented to a composite chassis frame of an SUV car under realistic random road inputs and the theoretical results are verified by the experimental results. Results confirm the significant role of the strain-rate-dependence effects on the fatigue lives.

Post-fire collapse assessment of the bank buildings (Belfast, UK) masonry façade via discrete element macro-analysis

On 28 August 2018, a fire broke out in the Bank Buildings, a five-story heritage construction located in the historic center of Belfast (UK), largely destroying its internal structure and leaving the outer unreinforced masonry (URM) walls free-standing. Of particular concern was the structural integrity of the East URM façade, whose cast-iron supporting system was lost in the fire. To assist the initial design of preventive public safety measures and investigate potential consequences of both local and global failures in a reduced timeframe, a simplified discrete element macro-model was developed to simulate various collapse scenarios. In this work, the adopted assumptions and modeling strategies are presented and discussed, including the selection of material properties, damping scheme, and simplified discretization. Predicted failure modes, debris distributions, magnitudes and directions of impact velocities are also compared, and main differences discussed and scrutinized. This practical and innovative application of discrete macro models enabled us to obtain reasonable results - not readily attainable using different analysis approaches - in a timely manner and provide solutions in a critical situation. Such novel employment of discrete element models - previously mostly confined to small-scale research problems - opens new structural simulation opportunities, challenges and research questions and, as such, might be of interest to both applied researchers and structural engineering professionals.

Resolving Pediatric Preventive Care Gaps Through Hospital Inreach.

Panel management processes have been used to help improve population-level care and outreach to patients outside the health care system. Opportunities to resolve gaps in preventive care are often missed when patients present outside of primary care settings but still within the larger health care system. We hypothesized that we could design a process of "inreach" capable of resolving care gaps traditionally addressed solely in primary care settings. Our aim was to identify and resolve gaps in vaccinations and screening for lead exposure for children within our primary care registry aged 2 to 66 months who were admitted to the hospital. We sought to increase care gaps closed from 12% to 50%. We formed a multidisciplinary team composed of primary care and hospital medicine physicians, nursing leadership, and quality improvement experts within the Division of General and Community Pediatrics. The team identified a smart aim, mapped the process, predicted failure modes, and developed a key driver diagram. We identified, tested, and implemented multiple interventions related to role assignment, identification of admitted patients with care gaps, and communication with the inpatient teams. After increasing the reliability of our process to identify and contact the hospital medicine team caring for patients who needed action to 88%, we observed an increase in the preventive care gaps closed from 12% to 41%. A process to help improve preventive care for children can be successfully implemented by using quality improvement methodologies outside of the traditional domains of primary care.

Geometrical Scaling Effects in the Mechanical Properties of 3D-Printed Body-Centered Cubic (BCC) Lattice Structures

This paper investigates size effects on the mechanical response of additively manufactured lattice structures based on a commercially available polylactic acid (PLA) polymer. Initial attention is focused on investigating geometrical effects in the mechanical properties of simple beams and cubes. Following this, a number of geometrically scaled lattice structures based on the body-centered cubic design were manufactured and tested in order to highlight size effects in their compression properties and failure modes. A finite element analysis was also conducted in order to compare the predicted modes of failure with those observed experimentally. Scaling effects were observed in the compression response of the PLA cubes, with the compression strength increasing by approximately 19% over the range of scale sizes investigated. Similar size-related effects were observed in the flexural samples, where a brittle mode of failure was observed at all scale sizes. Here, the flexural strength increased by approximately 18% when passing from the quarter size sample to its full-scale counterpart. Significant size effects were observed following the compression tests on the scaled lattice structures. Here, the compression strength increased by approximately 60% over the four sample sizes, in spite of the fact that similar failure modes were observed in all samples. Finally, reasonably good agreement was observed between the predicted failure modes and those observed experimentally. However, the FE models tended to over-estimate the mechanical properties of the lattice structures, probably as a result of the fact that the models were assumed to be defect free.

Physics-Based Machine Learning: Data Needs and Practices for Failure Mode Classification and Life Prediction

Machine learning and other advanced analysis approaches hold promise to reduce the time and effort needed to transition a new battery technology from innovation to deployment. Prediction of life and performance from sparse data is a key part of the technology revolution which will serve as a cornerstone of a reduced carbon energy future. Complimenting the prediction of life is the classification and prediction of specific failure modes and mechanisms to provide sufficient feed back to the R&D process so that key technology gaps can be addressed earlier and to ensure appropriate measures are taken to reduce long-term development costs.Here we discuss the use of machine learning to perform early failure mode classification and life prediction. Looking at two distinct sets of data for different fast charging applications provides the opportunity to extend differing levels of physicality to predictions. In the first instance a set of Nissan Leaf cells are used for prediction of performance to less than 1.2% mean absolute percent error.1 In the second instance a combination of experimental and synthetic data are used for failure mode classification and quantification for research cells used for extreme fast charging.2 The comparison of one set of data using commercial cells and one using research cells provides opportunity to revisit key data acquisition parameters and best practices that will enhance future efforts.[1] M. Ross Kunz, Eric J. Dufek, Zonggen Yi, et al “Early battery performance prediction for mixed use charging profiles using hierarchal machine learning” Batter. Supercaps, 2021,DOI: 10.1002/batt.202100079[2] B.R. Chen, M.R. Kunz, T.R. Tanim, E.J. Dufek “A machine learning framework for early detection of lithium plating combining multiple physics-based electrochemical signatures” Cell Reports Physical Science, 2021, 2(3), 100352

Response simulations of clamped circular steel plates under uniform impulse and effects of axisymmetric stiffener configurations

The paper presents Finite Element Analysis (FEA) and simulations of large deformations and observed ductile fracture failure modes of clamped circular plates subjected to uniform impulsive loads using phenomenological failure models for damage initiation and progression. The proposed numerical setup is founded on a well-defined model of the constitutive material properties based on inverse FEA simulations of the uniaxial tension test for the calibration of the material and failure parameters. The presented numerical model using the tri-axiality weighted accumulated plastic strain damage model in conjunction with a progressive degradation scheme for the evolution and progression of failure in the structure, is calibrated from a single uni-axial tension test. Published experimental results of large plastic deformations, onset of thinning and tearing at the boundary for clamped steel circular plates subjected to uniformly loaded air blasts have been used to assess the performance and efficacy of the proposed numerical scheme and procedure, with inclusion of temperature effects due to plastic dissipation. Towards improvement in the predictions by the numerical model, a hybrid damage model involving a two part ductile fracture locus has been proposed, which shows excellent correlation with the experimental results presented in terms of non-dimensional parameters over a wide range of impulsive loading and stress states, covering all the relevant failure modes. The validated numerical model has further been used to study the effect of flat bar axisymmetric stiffener configurations on the structural response and failure mechanisms of clamped circular plates, which has not been seen in the literature so far. The numerical simulations have also been used to gain insights into the evolution of plastic zones, damage progression and predicted failure modes in circumferentially oriented transversely stiffened steel plates with increasing intensity of impulsive loads.

Failure mode prediction of resistance spot welded quenching and partitioning stainless steel

Failure mode prediction of resistance spot welded quenching and partitioning stainless steel

Analytical method for the evaluation of the residual service life of prestressed concrete beams subjected to corrosion deterioration

AbstractSince catastrophic failures caused by corrosion deterioration have been observed worldwide, nowadays a key research topic consists in the reliable prediction of the long‐term performance of corroded reinforced concrete structures. In this article, firstly, a refined model for the assessment of the residual service life of prestressed concrete (PC) beams has been presented. The analytical model, according to the most recent formulations proposed in the scientific literature, allows to take into account the effects induced by corrosion over time in the calculation of bending, shear, and anchorage resistances. Secondly, a case study concerning a PC beam without transversal reinforcement, exposed to wet‐dry cycles using seawater, has been presented considering the spatial variability of corrosion. To this aim, a constant and a varying spatial distribution of corrosion have been assumed. Finally, the residual service life values, evaluated according to the two assumed spatial distributions of corrosion in strands, have been compared. The proposed model can be used for the prediction of the possible failure modes of PC beams over time, considering both the reduction of the mechanical properties of the prestressing steel and the bond degradation due to corrosion deterioration according to recent experimental evidence.

Seismic behavior of a novel beam to reinforced concrete-filled steel tube column joint

This paper presents an experimental investigation of a novel beam to reinforced concrete-filled steel tube (RCFST) column joint. Four beam-to-RCFST-column specimens, varying with a cross-section of middle steel tube (250 × 250 × 6, 300 × 300 × 9, 350 × 350 × 12) and transfer sleeves (with or without), were tested under the cyclic loading to clarify the main seismic indicators, such as failure modes, hysteretic performances, stiffness degradation, strength degradation, energy dissipation capacity and the strain responses of the novel beam-to-RCFST-column joint. The hysteretic curves of the tested beam-to-RCFST-column joint show obvious pinch phenomena because of the slip around the transfer sleeve. Transfer sleeve is the key factor to affect the strength of the tested beam-to-RCFST-column joint. The finite element analysis (FEA) shows a reliable prediction of mechanical properties and failure modes.

Shear strength model and prediction of failure mode for multi-spiral column

The damage of reinforced concrete columns due to shear is often serious, so this type of failure should be avoided from the design. In this paper, a model derived based on the discrete computational method is proposed to calculate the shear strength of column carried by multi-spiral transverse reinforcement, accounting for the effect of compression depth. Furthermore, based on this model, a method is proposed to predict the failure mode of multi-spiral columns. The test database of multi-spiral columns from previous studies is used to validate both the shear strength and failure mode predictions. The proposed model with a crack angle of 40 degrees gives the best estimation of the shear strength of multi-spiral columns, and the proposed method predicts well the failure mode of these columns. To avoid shear failure, the ratio of the minimum shear strength calculated from the proposed model with a crack angle of 40 degrees to the shear force based on the moment-curvature analysis is suggested to be larger than 1.2.

Integration of future maintenance actions in the prediction parameters of the ATLAS COPCO ZR 200 compressor

The prediction of several failure modes of an industrial equipment requires the development of prediction systems with several interdependent parameters. The integration of future maintenance actions with this type of prediction system is a major asset for maintenance decision making. This is even more relevant in the event that after having predicted the future occurrence of several failure modes, the maintenance department does not have the necessary resources to correct all the predicted failure modes at once. In this case it becomes necessary to know how much longer the equipment will work if future partial maintenance actions that do not correct all failure modes are implemented. It is to contribute to the resolution of this problem that we propose an architecture integrating the future maintenance actions to the prediction of several interdependent parameters. This architecture is based on the association of Proportional Integral Derivative regulators to Neuro-Fuzzy systems taking into account the four previous instants to predict the next instant. An application is made with accuracies of the order of 70% for the prediction of the phenomena of fouling of the coolers and of the order of 90% for the prediction of the phenomena of clogging of the filters of the ATLAS COPCO compressor, this with Central Processing Unit values not exceeding one minute.

Predicting failure modes of 3D-printed multi-material polymer sandwich structures from process parameters

The emergence of additive manufacturing (AM) technologies, such as fused deposition modeling (FDM), have enabled the realization of structures with superior mechanical performance through lightweighting and multi-material architectures. However, the complexity associated with the internal geometric features and potential material configurations have also presented new challenges in designing these structures to optimize mechanical performance. In particular, the failure mechanisms and their relationship to the load bearing capacity of the structures may vary compared to analogous structures designed using conventional manufacturing techniques. In this work, we investigate failure modes of 3 D-printed (3DP) multi-material polymer sandwich beam structures manufactured from acrylonitrile butadiene styrene (ABS) and polycarbonate (PC) materials and subjected to three-point bend loading. Digital Image Correlation (DIC) is utilized to understand the effects of different process parameters on the mechanical response of the 3DP structures. ABS and PC dogbone tensile specimens were printed to establish the baseline properties in tension with varying raster angles and infill patterns. Multi-material sandwich beam structures were then printed with honeycomb cores using different processing and architectural parameters, and the failure modes and loads of these structures were compared with predications from a failure model for sandwich structures that accounts for the following conventional failure modes: (1) indentation, (2) face sheet bending, (3) core bending, and (4) core shear. With minor changes in the processing and geometric parameters, failure modes could be shifted from the face sheets to core bending and core shear, as evidenced by the DIC strain field measurements, and the corresponding max load-to-weight ratios could be increased. Estimations of the tensile properties of the face sheets and core were found to be sufficiently accurate when combining classical lamination theory (CLT) and rule-of-mixtures (ROM) models, while the failure model also predicted the load bearing capacity and failure mode in three-point bending with reasonable accuracy.