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Climate change affects the growth conditions for Norway spruce (Picea abies) which is the most important species for the European construction timber industry. As a countermeasure, the species mix in European forests is being changed to include more hardwood, but also more drought-resistant softwood species, for example Douglas-fir (Pseudotsuga menziesii). Previous research has shown that Douglas-fir can on the one hand be suitable to produce highstrength material. On the other hand, however, Douglas-fir wood can also have a considerably lower strength than what is expected from spruce. Therefore, there is need for improved strength prediction and strength grading methods for Douglas-fir, to enable the correct allocation of Douglas-fir roundwood to the most suitable target product. In the present study, computed tomography (CT) image reconstructions of 53 Douglas-fir logs were used to predict the strength of the sawn timber and thus to identify logs which are suitable to produce glulam lamellas. To achieve this, statistical modelling was combined with Finite Element (FE) modelling of destructive tensile tests, based on simulated fibre orientations for the boards., Funder: Fachagentur Nachwachsende Rohstoffe e.V.; Austrian Federal Ministry for Agriculture, Regions and Tourism; ERA-NETISBN för värdpublikation: 9781713873273, 9781713873273, READiStrengt

The use of Glass Fibre-Reinforced Polymer as a building material in structures or structural components is on the rise. Standards such as CUR96, DNV and JRC provide a basis of design with the material. However, there is a lack of confidence in the design phase with structures made of Glass Fibre-Reinforced Polymer, resulting in the use of large safety factors causing the components to be bloated in size. At the time of writing this report, the technical committee, CEN/TC 250 (responsible for developing structural Eurocodes), establishes a technical design specification for Fibre-Reinforced Polymer (FRP) structures. This technical specification describes a simplified and linear criterion to determine the capacity of a GFPR Laminate, in addition to being open for the use of Progressive Failure Analysis (PFA). However, the simplified and linear criterion is overly conservative, whereas there is a lack of faith in the use of the PFA considering the failure theories and degradation models that are currently in use. This report discusses the PFA, a non-linear, 5-step, advanced 2D analysis model, that can predict the static strength of in-plane stress dominated Glass Fibre-Reinforced Polymer laminate, with an arbitrary lay-up composition, based on existing knowledge and experiments, including the damage development under multi-axial stress states and stress redistribution. The research is limited to in-plane behavior, under tensile and compressive stresses. The static material response is characterized on a unidirectional ply level based on principal directions and based on experimental results obtained from the OptiDat program. The response predicted by the PFA for both tension and compression was in reasonable agreement with the experimental results. However, depending on the failure theory and degradation model used, there is potential for optimistic predictions of the laminate stress capacity. For future work, it is recommended to continue the research on a larger vari, Civil Engineering

Climate change affects the growth conditions for Norway spruce (Picea abies) which is the most important species for the European construction timber industry. As a countermeasure, the species mix in European forests is being changed to include more hardwood, but also more drought-resistant softwood species, for example Douglas-fir (Pseudotsuga menziesii). Previous research has shown that Douglas-fir can on the one hand be suitable to produce highstrength material. On the other hand, however, Douglas-fir wood can also have a considerably lower strength than what is expected from spruce. Therefore, there is need for improved strength prediction and strength grading methods for Douglas-fir, to enable the correct allocation of Douglas-fir roundwood to the most suitable target product. In the present study, computed tomography (CT) image reconstructions of 53 Douglas-fir logs were used to predict the strength of the sawn timber and thus to identify logs which are suitable to produce glulam lamellas. To achieve this, statistical modelling was combined with Finite Element (FE) modelling of destructive tensile tests, based on simulated fibre orientations for the boards., Funder: Fachagentur Nachwachsende Rohstoffe e.V.; Austrian Federal Ministry for Agriculture, Regions and Tourism; ERA-NETISBN för värdpublikation: 9781713873273, 9781713873273, READiStrengt

A hybrid methodology based on numerical and non-destructive experimental schemes, which is able to predict the structural level strength of composite laminates is proposed on the current work. The main objective is to predict the strength by substituting the up to failure experiments with non-destructive experiments where the investigated specimen is loaded up to 20% of its maximum load. A significant gap exists between the 20% and the 100% load which is proposed to be treated by high fidelity physics-based numerical models, deep learning techniques, and non-catastrophic experiments. Thus, a deep learning algorithm is developed, based on the convolutional neural networks and trained by probabilistic failure analysis datasets which result from the utilization of the stochastic finite element method. Also, the Monte Carlo dropout technique is embedded into the developed convolutional neural network to estimate the uncertainty induced by the investigated variations between the simulated and experimental data. The current paper provides a thorough description of the proposed methodology and a practical example which demonstrates the validity of the method., Structural Integrity & Composites

X-ray computed tomography (CT) of wood delivers internal density data of a scanned object, where, depending on the resolution, internal features like the pith, annual rings and knots can be identified. Some sawmills use CT scanners in front of the saw line to determine the optimal positioning of the log in the saw, to maximise the value yield of the sawn products. We envision that the gathered CT data also could be used for mechanical evaluations of the timber using numerical models of boards prior to sawing. In a recent study by the authors, a method was developed to create 3D and 1D finite element (FE) models based on CT scans of dried sawn timber, which could predict bending stiffness and strength in bending simulations with high accuracy. The objective of the present study is to explore how the method can be adapted to CT scans of logs before sawing. Our preliminary study was based on CT data of green Norway Spruce logs and the corresponding scans of dried sawn timber. The stiffness and strength were evaluated using four-point bending tests. Additionally, the resonance frequency of the logs was recorded. The corresponding volume of each piece of sawn timber was extracted from the log data and an FE model was created. The model accounted for the pith, the annual rings, the knots, and the local fibre deviations around knots. Various laws for local stiffness and different failure criteria were tested. The study showed how FE models of virtual pieces of sawn timber can be created from CT data and what obstacles need to be overcome for further development of the presented method. The results indicated that more detailed evaluations of the relationship between local stiffness and density may be required, in specific for knots and for wood in green state., ReadIStrength

Partially replacing ordinary Portland cement (OPC) with low-carbon supplementary cementitious materials (SCMs) in blended cement concrete (BCC) is perceived as the most promising route for sustainable concrete production. Despite having a lower environmental impact, BCC could exhibit performance inferior to OPC in design-governing functional properties. Hence, concrete manufacturers and scientists have been seeking methods to predict the performance of BCC mixes in order to reduce the cost and time of experimentally testing all alternatives. Machine learning algorithms have been proven in other fields for treating large amounts of data drawing meaningful relationships between data accurately. However, the existing prediction models in the literature come short in covering a wide range of SCMs and/or functional properties. Considering this, in this study, a non-linear multi-layered machine learning regression model was created to predict the performance of a BCC mix for slump, strength, and resistance to carbonation and chloride ingress based on any of five prominent SCMs: fly ash, ground granulated blast furnace slag, silica fume, lime powder and calcined clay. A database from>150 peer-reviewed sources containing>1650 data points was created to train and test the model. The statistical performance was found to be comparable to that of existing models (R = 0.94–0.97). For the first time, the model, Pre-bcc, was also made available online for users to conduct their own prediction studies., Peer Reviewed, Postprint (published version)

X-ray computed tomography (CT) of wood delivers internal density data of a scanned object, where, depending on the resolution, internal features like the pith, annual rings and knots can be identified. Some sawmills use CT scanners in front of the saw line to determine the optimal positioning of the log in the saw, to maximise the value yield of the sawn products. We envision that the gathered CT data also could be used for mechanical evaluations of the timber using numerical models of boards prior to sawing. In a recent study by the authors, a method was developed to create 3D and 1D finite element (FE) models based on CT scans of dried sawn timber, which could predict bending stiffness and strength in bending simulations with high accuracy. The objective of the present study is to explore how the method can be adapted to CT scans of logs before sawing. Our preliminary study was based on CT data of green Norway Spruce logs and the corresponding scans of dried sawn timber. The stiffness and strength were evaluated using four-point bending tests. Additionally, the resonance frequency of the logs was recorded. The corresponding volume of each piece of sawn timber was extracted from the log data and an FE model was created. The model accounted for the pith, the annual rings, the knots, and the local fibre deviations around knots. Various laws for local stiffness and different failure criteria were tested. The study showed how FE models of virtual pieces of sawn timber can be created from CT data and what obstacles need to be overcome for further development of the presented method. The results indicated that more detailed evaluations of the relationship between local stiffness and density may be required, in specific for knots and for wood in green state., ReadIStrength

Despite previous efforts to relate concrete proportioning and strength, a robust knowledgebased model for accurate concrete strength predictions is still lacking. As an alternative to physical or chemical-based models, machine learning (ML) methods offer a new solution to this problem. Although ML can handle the complex, non-linear, non-additive relationship between concrete mixture proportions and strength, it requires large datasets. This is a concern as reliable strength data is rather limited, especially for industrial concretes. Here, based on a large dataset (>10,000 observations) of measured compressive strengths from industrially-produced concretes, we compare the ability of select ML algorithms to “learn” how to reliably predict concrete strength as a function of the size of the dataset. Based on these results, we discuss the competition between how accurate a given model can eventually be (when trained on a large dataset) and how much data is actually required to train this model.

Anticipating the benefits and potential of using Wire and Arc Additive Manufacturing (WAAM) in the construction field for structural connections to enhance automation, energy efficiency, and material utilization rate, the process to provide a reliable and efficient prediction for the strength of WAAM components is the focus of this thesis. Finite Element Analysis (FEA) is proven reliable and can be used as a method for strength prediction. ABAQUS has been chosen as a simulation tool due to its automatic and adaptive ability for computation and the dominancy in academic research. Current WAAM components research builds WAAM models with the heat source and printing path, which took an unacceptable time (could be several days) to simulate the behavior (distortion and residual stress) for meter-scale structural WAAM steel components. A semi-analytical model, which implicitly incorporated the behavior of the WAAM material by the direct use of components' mechanical test data as the FEA inputs, can speed up the strength prediction process. This research is built with a feasible process with unique steps for predicting WAAM components' strength. First, WAAM components’ lab tests from Van Bolderen [1] have been reviewed and numerically examined before being used for designing WAAM joints , which makes the data used more reliable. Four lab tests that have been reviewed and simulated are the tensile test, the compression test, the bending test, and the buckling test. Examined material non-linearity, anisotropy of the material, and the surface roughness have been applied to predict the strength of the joints. Five aims and five actions have been set and implemented in the design of WAAM printed gridshell joints. Moment-rotational curves for three joints are presented to acquire flexural stiffnesses and moment capacities. Second, the inclusion of the defined geometrical imperfections (the surface roughness and the lack of straightness) and material imperfections (anisotropy) to

Anticipating the benefits and potential of using Wire and Arc Additive Manufacturing (WAAM) in the construction field for structural connections to enhance automation, energy efficiency, and material utilization rate, the process to provide a reliable and efficient prediction for the strength of WAAM components is the focus of this thesis. Finite Element Analysis (FEA) is proven reliable and can be used as a method for strength prediction. ABAQUS has been chosen as a simulation tool due to its automatic and adaptive ability for computation and the dominancy in academic research. Current WAAM components research builds WAAM models with the heat source and printing path, which took an unacceptable time (could be several days) to simulate the behavior (distortion and residual stress) for meter-scale structural WAAM steel components. A semi-analytical model, which implicitly incorporated the behavior of the WAAM material by the direct use of components' mechanical test data as the FEA inputs, can speed up the strength prediction process. This research is built with a feasible process with unique steps for predicting WAAM components' strength. First, WAAM components’ lab tests from Van Bolderen [1] have been reviewed and numerically examined before being used for designing WAAM joints , which makes the data used more reliable. Four lab tests that have been reviewed and simulated are the tensile test, the compression test, the bending test, and the buckling test. Examined material non-linearity, anisotropy of the material, and the surface roughness have been applied to predict the strength of the joints. Five aims and five actions have been set and implemented in the design of WAAM printed gridshell joints. Moment-rotational curves for three joints are presented to acquire flexural stiffnesses and moment capacities. Second, the inclusion of the defined geometrical imperfections (the surface roughness and the lack of straightness) and material imperfections (anisotropy) to

Despite previous efforts to relate concrete proportioning and strength, a robust knowledgebased model for accurate concrete strength predictions is still lacking. As an alternative to physical or chemical-based models, machine learning (ML) methods offer a new solution to this problem. Although ML can handle the complex, non-linear, non-additive relationship between concrete mixture proportions and strength, it requires large datasets. This is a concern as reliable strength data is rather limited, especially for industrial concretes. Here, based on a large dataset (>10,000 observations) of measured compressive strengths from industrially-produced concretes, we compare the ability of select ML algorithms to “learn” how to reliably predict concrete strength as a function of the size of the dataset. Based on these results, we discuss the competition between how accurate a given model can eventually be (when trained on a large dataset) and how much data is actually required to train this model.

In this study, the predictive capability for compressive strength of IBK a K-Nearest Neighbor algorithm was put to test in High Performance Concrete (HPC) with steel fiber addition. To achieve this objective, 150 x 300 mm cylindrical specimens were casted at least three for each batch and steel fibers were added from 0.50% - 2.00% at 0.25% interval. The mean and standard deviation were determined, and these were used to generate 100 compressive strength values within this range for each proportion. IBK classifier with K =1 nearest neighbors and 3 split percentages for training and testing were utilized. Results indicate that it is possible to generate good compressive strength results from good mean and standard deviation values. The prediction capability was very high using this algorithm with small amount of associated errors. Validation of the model using predicted versus actual results shows a very high correlation coefficient. This result indicates the efficiency of the model and its predictive capacity. It also indicates that this can improve the optimization capacity of HPC mixtures with steel fiber addition.

In this study, the predictive capability for compressive strength of IBK a K-Nearest Neighbor algorithm was put to test in High Performance Concrete (HPC) with steel fiber addition. To achieve this objective, 150 x 300 mm cylindrical specimens were casted at least three for each batch and steel fibers were added from 0.50% - 2.00% at 0.25% interval. The mean and standard deviation were determined, and these were used to generate 100 compressive strength values within this range for each proportion. IBK classifier with K =1 nearest neighbors and 3 split percentages for training and testing were utilized. Results indicate that it is possible to generate good compressive strength results from good mean and standard deviation values. The prediction capability was very high using this algorithm with small amount of associated errors. Validation of the model using predicted versus actual results shows a very high correlation coefficient. This result indicates the efficiency of the model and its predictive capacity. It also indicates that this can improve the optimization capacity of HPC mixtures with steel fiber addition.

Traditionally, ultimate tensile strength (UTS) is used as the main property for the characterization of lamellar graphite iron (LGI) alloys under static loads. The main models found in the literature for predicting UTS of pearlitic lamellar graphite iron are based on either regression analysis on experimental data or on modified Griffith or Hall-Petch equation. In pearlitic lamellar graphite iron the primary austenite dendritic network, transformed to pearlite, reinforces the bulk material while the distance between those pearlite grains, defines the maximum continuous defect size in the bulk material. Recently the novel parameter of the Diameter of Interdendritic Space has been used to express the flow length in a modified Griffith equation for the prediction of the UTS in LGI. Nevertheless this model neglects the strengthening effect of the pearlite lamellar spacing within the perlite grains. A model based on modified Hall-Petch equation was developed in this work. The model considers the effect of both microstructure parameters and covers a broad spectrum of microstructure sizes typical for complex shape castings with various wall thicknesses.

An obstacle to the wide spread use of additive manufacturing (AM) is the difficulty in estimating the effects of process parameters on the mechanical properties of the manufactured part. The complex relationship between the geometry, parameters and mechanical properties makes it impractical to derive an analytical relationship and calls for the use of a numerical model. An approach to formulate a numerical model in developed in this thesis. The AM technique focused in this thesis is fused filament fabrication (FFF). A numerical model is developed by recreating FFF build process in a simulation environment. Machine instructions generated by a slicer to build a part is used to create a numerical model. The model acts as a basis to determine the effects of process parameters on the stiffness and the strength of a part. Determining the stiffness of the part is done by calculating the response of the model to a uniformly distributed load. The strength of the part depends on it's thermal history. The developed numerical model serves as a basis to implement models describing the relation between thermal history and strength. The developed model is suited to optimize FFF parameters as it encompass effects of all FFF parameters. A genetic algorithm is used to optimize the FFF parameters for minimum weight with a minimum stiffness constraint., Ett hinder för att additiv tillverkning (AT), eller ”3D-printing”, ska få ett bredare genomslag är svårigheten att uppskatta effekterna av processparametrar på den tillverkade produktens mekaniska prestanda. Det komplexa förhållandet mellan geometri och processparametrar gör det opraktiskt och komplicerat att härleda analytiska uttryck för att förutsäga de mekaniska egenskaperna. Alternativet är att istället använda numeriska modeller. Huvudsyftet med denna avhandling har därför varit att utveckla en numerisk modell som kan användas för att förutsäga de mekaniska egenskaperna för detaljer tillverkade genom AT. AT-tekniken som avses är inriktad på Fused Filament Fabrication (FFF). En numerisk modell har utvecklats genom att återskapa FFF-byggprocessen i en simuleringsmiljö. Instruktioner (skriven i GCode) som används för att bygga en detalj genom FFF har här översatts till en numerisk FE-modell. Modellen används sen för att bestämma effekterna av processparametrar på styvheten och styrkan hos den tillverkade detaljen. I detta arbete har strukturstyvheten hos olika detaljer beräknats genom att utvärdera modellens svar för jämnt fördelade belastningsfall. Styrkan, vilket är starkt beroende på den tillverkade detaljens termiska historia, har inte utvärderats. Den utvecklade numeriska modellen kan dock fungera som underlag för implementering av modeller som beskriver relationen mellan termisk historia och styrka. Den utvecklade modellen är anpassad för optimering av FFF-parametrar då den omfattar effekterna av alla FFF-parametrar. En genetisk algoritm har använts i detta arbete för att optimera parametrarna med avseende på vikt för en given strukturstyvhet.

An obstacle to the wide spread use of additive manufacturing (AM) is the difficulty in estimating the effects of process parameters on the mechanical properties of the manufactured part. The complex relationship between the geometry, parameters and mechanical properties makes it impractical to derive an analytical relationship and calls for the use of a numerical model. An approach to formulate a numerical model in developed in this thesis. The AM technique focused in this thesis is fused filament fabrication (FFF). A numerical model is developed by recreating FFF build process in a simulation environment. Machine instructions generated by a slicer to build a part is used to create a numerical model. The model acts as a basis to determine the effects of process parameters on the stiffness and the strength of a part. Determining the stiffness of the part is done by calculating the response of the model to a uniformly distributed load. The strength of the part depends on it's thermal history. The developed numerical model serves as a basis to implement models describing the relation between thermal history and strength. The developed model is suited to optimize FFF parameters as it encompass effects of all FFF parameters. A genetic algorithm is used to optimize the FFF parameters for minimum weight with a minimum stiffness constraint., Ett hinder för att additiv tillverkning (AT), eller ”3D-printing”, ska få ett bredare genomslag är svårigheten att uppskatta effekterna av processparametrar på den tillverkade produktens mekaniska prestanda. Det komplexa förhållandet mellan geometri och processparametrar gör det opraktiskt och komplicerat att härleda analytiska uttryck för att förutsäga de mekaniska egenskaperna. Alternativet är att istället använda numeriska modeller. Huvudsyftet med denna avhandling har därför varit att utveckla en numerisk modell som kan användas för att förutsäga de mekaniska egenskaperna för detaljer tillverkade genom AT. AT-tekniken som avses är inriktad på Fused Filament Fabrication (FFF). En numerisk modell har utvecklats genom att återskapa FFF-byggprocessen i en simuleringsmiljö. Instruktioner (skriven i GCode) som används för att bygga en detalj genom FFF har här översatts till en numerisk FE-modell. Modellen används sen för att bestämma effekterna av processparametrar på styvheten och styrkan hos den tillverkade detaljen. I detta arbete har strukturstyvheten hos olika detaljer beräknats genom att utvärdera modellens svar för jämnt fördelade belastningsfall. Styrkan, vilket är starkt beroende på den tillverkade detaljens termiska historia, har inte utvärderats. Den utvecklade numeriska modellen kan dock fungera som underlag för implementering av modeller som beskriver relationen mellan termisk historia och styrka. Den utvecklade modellen är anpassad för optimering av FFF-parametrar då den omfattar effekterna av alla FFF-parametrar. En genetisk algoritm har använts i detta arbete för att optimera parametrarna med avseende på vikt för en given strukturstyvhet.

Traditionally, ultimate tensile strength (UTS) is used as the main property for the characterization of lamellar graphite iron (LGI) alloys under static loads. The main models found in the literature for predicting UTS of pearlitic lamellar graphite iron are based on either regression analysis on experimental data or on modified Griffith or Hall-Petch equation. In pearlitic lamellar graphite iron the primary austenite dendritic network, transformed to pearlite, reinforces the bulk material while the distance between those pearlite grains, defines the maximum continuous defect size in the bulk material. Recently the novel parameter of the Diameter of Interdendritic Space has been used to express the flow length in a modified Griffith equation for the prediction of the UTS in LGI. Nevertheless this model neglects the strengthening effect of the pearlite lamellar spacing within the perlite grains. A model based on modified Hall-Petch equation was developed in this work. The model considers the effect of both microstructure parameters and covers a broad spectrum of microstructure sizes typical for complex shape castings with various wall thicknesses.

Thirty-two (32) boards of Norway spruce with cross-sectional dimensions of 145×45 mm2 were first tested non-destructively in a four-point static bending test, were then thermally modified according to the ThermoWood® process, and were finally tested destructively in the mentioned test set up. For one of these boards, the 2D strain fields occurring due to pure bending were recorded, both before and after thermal modification, over the surface of a knotty part of the board using a non-contact optical deformation measurement system. The objectives were to get more insight into the static bending behaviour of thermally modified timber (TMT), specifically with regard to the local and global modulus of elasticity (MOE) and their respective relationship to bending strength, and the strain development around a cluster of knots. The bending strength was significantly reduced by thermal treatment, whereas the effect on the MOEs was limited. Linear regression analyses demonstrated that bending strength of TMT can be predicted by employing stiffness as indicating property. Strain field measurements showed that at the examined levels of loading the quantity and distribution of strains in a knotty area were not influenced by thermal modification. It was therefore suggested that the influence of thermal modification on global stiffness, as well as on local stiffness around knots, is limited., Ej belagd 190524

Traditionally, ultimate tensile strength (UTS) is used as the main property for the characterization of lamellar graphite iron (LGI) alloys under static loads. The main models found in the literature for predicting UTS of pearlitic lamellar graphite iron are based on either regression analysis on experimental data or on modified Griffith or Hall-Petch equation. In pearlitic lamellar graphite iron the primary austenite dendritic network, transformed to pearlite, reinforces the bulk material while the distance between those pearlite grains, defines the maximum continuous defect size in the bulk material. Recently the novel parameter of the Diameter of Interdendritic Space has been used to express the flow length in a modified Griffith equation for the prediction of the UTS in LGI. Nevertheless this model neglects the strengthening effect of the pearlite lamellar spacing within the perlite grains. A model based on modified Hall-Petch equation was developed in this work. The model considers the effect of both microstructure parameters and covers a broad spectrum of microstructure sizes typical for complex shape castings with various wall thicknesses.

This research establishes a correlation between the predicted fiber orientation in Steel Fiber Reinforced Concrete (SFRC) and the flexural behavior of the composite material. It is well known that the proper alignment of fibers in the direction of tensile force enhances the mechanical properties of concrete. As recent studies have corroborated, the flow induced by self-compacting properties can influence fiber orientation. Thus, both technologies may be combined in the casting of a real-scale Steel Fiber Reinforced Self-Compacting Concrete (SFRSCC) wall (3-m high, 6-m long and 0.15-m thick). Computational Fluid Dynamics (CFD) methods are applied, implementing a Bingham plastic model in a homogeneous fluid, in order to predict fiber orientation. The acceptability of the expected orientations is confirmed by analyzing the velocity fields of the fluid throughout successive time-steps of the simulation and by linking those fields to the bending strengths (pre- and post-cracking) of prismatic specimens extracted from the wall. Expected orientations are further supported by non-destructive magnetic methods. Prediction techniques are of paramount importance nowadays, because design rules are only based on the response of preliminary prismatic specimens that are not representative of the final real structural elements. This novel methodology characterizes the material through the analysis of numerical simulations, without the cost of casting real elements, which notably simplifies the assessment of materials for use in new structural elements and geometries.

Current work concentrates in modelling failure and damage of various woven fabric reinforcement system taken from selected literatures. Experimental observations under quasi-static loading revealed that initial failure occurs at notch vicinity consist of one or combinations of matrix cracking, splitting and laminate de-lamination. The crack then propagated along net-section of distance of about one notch radius size, which thereafter occurrence usually corresponds to catastrophic failures that seen significant tow fractures. The distance of cracked zone signifies ultimate material failure usually referred as “effective damage zone” that useful to be implemented within fracture mechanics concept. These complex morphologies mechanisms can be represented by implementing physically-based constitutive model. Series works of notched woven fabric composite systems plate were implemented within two-dimensional extended finite element method (XFEM) framework were carried out by implementing constitutive models based on basic material properties data of respective system. Good agreement is shown in comparison with experimental data in various and other closed-form approaches composite fabric systems.

Current work concentrates in modelling failure and damage of various woven fabric reinforcement system taken from selected literatures. Experimental observations under quasi-static loading revealed that initial failure occurs at notch vicinity consist of one or combinations of matrix cracking, splitting and laminate de-lamination. The crack then propagated along net-section of distance of about one notch radius size, which thereafter occurrence usually corresponds to catastrophic failures that seen significant tow fractures. The distance of cracked zone signifies ultimate material failure usually referred as “effective damage zone” that useful to be implemented within fracture mechanics concept. These complex morphologies mechanisms can be represented by implementing physically-based constitutive model. Series works of notched woven fabric composite systems plate were implemented within two-dimensional extended finite element method (XFEM) framework were carried out by implementing constitutive models based on basic material properties data of respective system. Good agreement is shown in comparison with experimental data in various and other closed-form approaches composite fabric systems.

Current work concentrates in modelling failure and damage of various woven fabric reinforcement system taken from selected literatures. Experimental observations under quasi-static loading revealed that initial failure occurs at notch vicinity consist of one or combinations of matrix cracking, splitting and laminate de-lamination. The crack then propagated along net-section of distance of about one notch radius size, which thereafter occurrence usually corresponds to catastrophic failures that seen significant tow fractures. The distance of cracked zone signifies ultimate material failure usually referred as “effective damage zone” that useful to be implemented within fracture mechanics concept. These complex morphologies mechanisms can be represented by implementing physically-based constitutive model. Series works of notched woven fabric composite systems plate were implemented within two-dimensional extended finite element method (XFEM) framework were carried out by implementing constitutive models based on basic material properties data of respective system. Good agreement is shown in comparison with experimental data in various and other closed-form approaches composite fabric systems.

This study is an investigation into methods of developing ergonomic guidelines for automotive assembly tasks involving insertion of small parts. The study was conducted in four major parts: 1) a method of determining and evaluating subjective exertion limits was modified and tested, 2) a large dataset was collected from an industrial population in 10 simulated assembly line tasks, 3) a smaller dataset was collected from a student population to assess hand dominance effects, and 4) strength data obtained was compared with a strength prediction model to determine if the model could predict manual insertion forces. The traditional method of psychophysical data collection requires participants to extrapolate sensations from a relativity short session to judge if the task could be done for a much longer period. Maximum acceptable limits (MALs) are typically derived from having participants adjust a weight, resistance, or frequency to an acceptable level. The present study evaluated a relatively new method of collecting MAL data for simple, single-digit exertions where participants were asked to determine an MAL by self-adjusting and then regulating to maintain the exertion level. Results showed that MAL values obtained from a series of self-regulated exertions were independent of both analysis method and duration (5 minutes vs. 25 minutes) used for evaluation, and that the method was repeatable both within and between sessions. Ergonomic guidelines are often obtained from the strength capacity for a certain task, as it is important to ensure that workers possess sufficient strength to accomplish a task. As task demands increase, however, a larger percentage of a worker's strength capability in required, and other factors, such as performance and worker comfort, tend to be compromised. In this work, both strength capacity and subjective limits were obtained for a variety of simulated tasks to facilitate development of guidelines for the specific tasks. The relationship be

To achieve satisfactory levels of strength, fracture toughness, and reliability for man-rated systems such as jet engines, fiber reinforced ceramic matrix composites are needed. An elevated temperature axial testing system is developed to investigate and characterize fatigue behavior of Nicalon fiber reinforced enhanced silicon carbide matrix. composites at 1800 of under fully reversed cyclic loading. Notch effect on quasi-static tensile response is also considered. Quasi-static and fatigue damage mechanisms and failure modes are examined using various specimen geometries, load levels, fatigue ratios, and laminates stacking sequences by employing a number of NDE techniques. Issues such as damage tolerance and durability are addressed by conducting interrupted fatigue tests at various stages of life for different load levels. Results are compared to the predictions of remaining strength and life, obtained using a performance simulation code. Initial results indicate existence of a threshold stress value which limits the use of the material system.

Modern durability and damage tolerance predictions for composite material systems rely on accurate estimates of the local stress and material states for each of the constituents, as well as the manner in which the constituents interact. In this work, an number of approaches to estimating the stress states and interactions are developed. First, an elasticity solution is presented for the problem of a penny-shaped crack in an N-phase composite material system opened by a prescribed normal pressure. The stress state around such a crack is then used to estimate the stress concentrations due to adjacent fiber fractures in a composite materials. The resulting stress concentrations are then used to estimate the tensile strength of the composite. The predicted results are compared with experimental values. In addition, a cumulative damage model for fatigue is presented. Modifications to the model are made to include the effects of variable amplitude loading. These modifications are based upon the use of remaining strength as a damage metric and the definition of an equivalent generalized time. The model is initially validated using results from the literature. Also, experimental data from APC-2 laminates and IM7/K3B laminates are used in the model. The use of such data for notched laminates requires the use of an effective hole size, which is calculated based upon strain distribution measurements. Measured remaining strengths after fatigue loading are compared with the predicted values for specimens fatigued at room temperature and 350°F (177°C).

To achieve satisfactory levels of strength, fracture toughness, and reliability for man-rated systems such as jet engines, fiber reinforced ceramic matrix composites are needed. An elevated temperature axial testing system is developed to investigate and characterize fatigue behavior of Nicalon fiber reinforced enhanced silicon carbide matrix. composites at 1800 of under fully reversed cyclic loading. Notch effect on quasi-static tensile response is also considered. Quasi-static and fatigue damage mechanisms and failure modes are examined using various specimen geometries, load levels, fatigue ratios, and laminates stacking sequences by employing a number of NDE techniques. Issues such as damage tolerance and durability are addressed by conducting interrupted fatigue tests at various stages of life for different load levels. Results are compared to the predictions of remaining strength and life, obtained using a performance simulation code. Initial results indicate existence of a threshold stress value which limits the use of the material system.