Multivariate Adaptive Regression Splines Model to Predict Fracture Characteristics of High Strength and Ultra High Strength Concrete Beams

This paper presents Multivariate Adaptive Regression Splines (MARS) model to predict the fracture characteristics of high strength and ultra high strength concrete beams. Fracture characteristics include fracture energy (GF), critical stress intensity factor (KIC) and critical crack tip opening displacement (CTODc). This paper also presents the details of development of MARS model to predict failure load (Pmax) of high strength concrete (HSC) and ultra high strength concrete (UHSC) beam specimens. Characterization of mix and testing of beams of high strength and ultra strength concrete have been described. Methodologies for evaluation of fracture energy, critical stress intensity factor and critical crack tip opening displacement have been outlined. MARS model has been developed by establishing a relationship between a set of predicators and dependent variables. MARS is based on a divide and conquers strategy partitioning the training data sets into separate regions; each gets its own regression line. Four MARS models have been developed by using MATLAB software for training and prediction of fracture parameters and failure load.MARS has been trained with about 70% of the total 87 data sets and tested with about 30% of the total data sets. It is observed from the studies that the predicted values of Pmax, GF, KIC and CTODC are in good agreement with those of the experimental values.     

ANN Model to Predict Fracture Characteristics of High Strength and Ultra High Strength Concrete Beams

This paper presents fracture mechanics based Artificial Neural Network (ANN) model to predict the fracture characteristics of high strength and ultra high strength concrete beams. Fracture characteristics include fracture energy (Gf), critical stress intensity factor (KIC) and critical crack tip opening displacement (CTODc). Failure load of the beam (Pmax) is also predicated by using ANN model. Characterization of mix and testing of beams of high strength and ultra strength concrete have been described. Methodologies for evaluation of fracture energy, critical stress intensity factor and critical crack tip opening displacement have been outlined. Back-propagation training technique has been employed for updating the weights of each layer based on the error in the network output. Levenberg- Marquardt algorithm has been used for feed-forward back-propagation. Four ANN models have been developed by using MATLAB software for training and prediction of fracture parameters and failure load. ANN has been trained with about 70% of the total 87 data sets and tested with about 30% of the total data sets. It is observed from the studies that the predicted values of Pmax, Gf, failure load, KIc and CTODc are in good agreement with those of the experimental values.     

Prediction of Compressive Strength of Various SCC Mixes Using Relevance Vector Machine

This paper discusses the applicability of relevance vector machine (RVM) based regression to predict the compressive strength of various self compacting concrete (SCC) mixes. Compressive strength data various SCC mixes has been consolidated by considering the effect of water cement ratio, water binder ratio and steel fibres. Relevance vector machine (RVM) is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression and classification. The RVM has an identical functional form to the support vector machine, but provides probabilistic classification and regression. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. Compressive strength model has been developed by using MATLAB software for training and prediction. About 75% of the data has been used for development of model and 30% of the data is used for validation. The predicted compressive strength for SCC mixes is found to be in very good agreement with those of the corresponding experimental observations available in the literature.     

The Effect of Size on the Splitting Strength of Cubic Concrete Members

In the study of concrete fractures, split‐tension specimens, such as cylinders, cubes and diagonal cubes, are frequently preferred to beams. However, experimental investigations on concrete reveal that for the same specimen geometry, the nominal strength of specimen decreases with increasing specimen size. This phenomenon is named as the size effect in the fracture mechanics of concrete. Although nominal strength is also highly affected by the width of the distributed load in the split‐tension cylinder and cube specimens, this effect can be negligible within the practical range of the load‐distributed width in the diagonal cubes. However, the number of theoretical and experimental studies with diagonal split‐tension specimens is limited. Besides, a size effect formula for estimating the split‐tensile strength of the diagonal cube specimens has not been proposed. In this study, nine series of cube and diagonal cube specimens, with three different sizes but similar geometries, were tested under different load‐distributed widths. The ultimate loads obtained from the test results are analysed by the modified size effect law. Subsequently, prediction formulas are proposed, and they are compared with historical test data from the split‐cylinder specimens.     

基于内部失效机理预测评估渗碳Cr-Ni齿轮钢的超高周疲劳强度

对渗碳Cr-Ni齿轮钢进行应力比为0和0.3的室温超高周疲劳实验,观测试样中诱发裂纹萌生的夹杂和疲劳断口形貌,以全面评估渗碳Cr-Ni齿轮钢疲劳性能。将疲劳失效模式分为有细颗粒区(Fine granular area,FGA)的内部疲劳失效和有表面光滑区(Surface smooth area, SSA)的表面疲劳失效,并阐明了渗碳Cr-Ni齿轮钢的超高周内部疲劳破坏机制。基于累积损伤和位错能量法并结合细颗粒区形成机理和夹杂的最大评估尺寸,分别构建了两种渗碳Cr-Ni齿轮钢内部疲劳强度的预测模型。利用FGA尺寸与夹杂尺寸的比值和夹杂应力强度因子及应力比之间的关系,修正所提出的两种疲劳强度预测模型并给出了最大夹杂尺寸下的l_FGA-S-N曲线。结果表明,基于累积损伤法和位错能量法分别构建的疲劳强度预测模型都可用于预测评估渗碳Cr-Ni齿轮钢在多种应力比下的内部疲劳强度,基于位错能量法的强度预测模型精度较高。     

Predicting motor vehicle crashes using Support Vector Machine models

Crash prediction models have been very popular in highway safety analyses. However, in highway safety research, the prediction of outcomes is seldom, if ever, the only research objective when estimating crash prediction models. Only very few existing methods can be used to efficiently predict motor vehicle crashes. Thus, there is a need to examine new methods for better predicting motor vehicle crashes. The objective of this study is to evaluate the application of Support Vector Machine (SVM) models for predicting motor vehicle crashes. SVM models, which are based on the statistical learning theory, are a new class of models that can be used for predicting values. To accomplish the objective of this study, Negative Binomial (NB) regression and SVM models were developed and compared using data collected on rural frontage roads in Texas. Several models were estimated using different sample sizes. The study shows that SVM models predict crash data more effectively and accurately than traditional NB models. In addition, SVM models do not over-fit the data and offer similar, if not better, performance than Back-Propagation Neural Network (BPNN) models documented in previous research. Given this characteristic and the fact that SVM models are faster to implement than BPNN models, it is suggested to use these models if the sole purpose of the study consists of predicting motor vehicle crashes.     

汽车用先进高强钢本构模型与韧性断裂模型研究进展

轻量化是当前汽车行业全产业链共同面对的课题,提高先进高强钢使用比例是实现汽车轻量化的有效手段。对先进高强钢本构模型与韧性断裂模型的充分研究有助于提高先进高强钢开裂分析和预测的准确性,从而推动先进高强钢工程的应用进程。目前,在先进高强钢的研究过程中,学者们通常通过多种应变强化模型的线性组合,或结合微观结构与宏观力学行为进行多尺度分析来建立本构模型;通过多种应力状态下的准静态拉伸实验以及使用仿真与实验混合的方法来标定韧性断裂模型的参数。以第三代先进高强钢中的淬火配分（QP）钢为重点讨论对象,介绍了制备工艺与材料特性及其相关研究进展,并介绍了QP钢本构模型的研究现状、新近发展的非耦合韧性断裂模型以及考虑了应力三轴度和罗德角参数影响的韧性断裂模型在先进高强钢上的应用现状,最后指出了先进高强钢本构模型和韧性断裂模型未来的研究方向。     

Artificial Neural Network Prediction of Ultimate Strength of Unidirectional T-300/914 Tensile Specimens Using Acoustic Emission Response

Acoustic Emission (AE) Monitoring was used to evaluate unidirectional carbon epoxy specimens when tensile loaded with a 100 kN Universal Testing Machine. A series of eighteen samples were loaded to failure to generate AE data for this analysis. After data acquisition, AE response from each test was filtered to include only data collected up to 50% of the actual failure load for further analysis. Amplitude, Duration and Energy are effective parameters utilized to differentiate various failure modes in composites viz., matrix crazing, fiber cut, and delamination with several sub categories such as matrix splitting, fiber/matrix debonding, fiber pull-out etc. The ultimate strength prediction was performed with an Artificial Neural Network Back propagation algorithm. Peak Amplitude values varying from 35–100 dB were taken as the input to the network. The impact of signal amplitudes due to different failure mechanism to the ultimate strength was mapped using a supervised network having a middle layer with 45 neurons and actual failure loads were supplied as target values during training phase. The network finally trained with twelve specimens was able to predict failure loads of remaining six specimens with in the acceptable error tolerance.     

Effect of Loading Rate on Fracture Energy of High‐Strength Concrete

Abstract: This research deals with the sensitivity of several types of performance‐designed high‐strength concrete to the loading rate. Variations in the composition of the concrete produce the desired performance, for instance having null shrinkage or being able to be pumped at elevated heights without segregation, but they also produce variations in the fracture properties that are reported in this paper. We performed tests at five loading rates spanning six orders of magnitude in the displacement rate, from 1.74 × 10^?5 mm s^?1 to 17.4 mm s^?1. Load‐displacement curves show that their peak is higher as the displacement rate increases, whereas the corresponding displacement is almost constant. Fracture energy also increases, but only for loading rates higher than 0.01 mm s^?1. We use a formula based on a cohesive law with a viscous term [Anales de Mecánica de la Fractura 25 (2008) 793–797] to study the results. The correlation of the formula to the experimental results is good and it allows us to obtain the theoretical value for the fracture energy under strictly static conditions. In addition, both the fracture energy and the characteristic length of the concretes used in the study diminish as the compressive strength of their aggregates increases.     

Analytical and experimental investigations on the fracture behavior of hybrid fiber reinforced concrete

In the present study, Mode-I fracture tests of hybrid fiber reinforced concrete (HFRC) composite beams were conducted and the fracture properties and other post peak strength characteristics of the HFRC composites were evaluated and analyzed. The HFRC composite was produced using three types of fibers namely steel, Kevlar and polypropylene. A total of 27 HFRC composite beam specimens were cast and tested using the RILEM recommended three point bending test. The main variables were the fiber volume content and combinations of different fibers. The load versus crack mouth opening displacement (CMOD) curves of HFRC composite beams were obtained. Inverse analysis was carried out to determine the tensile strength and crack opening relationship. Analytical models based on comprehensive reinforcing index were developed for determining the influence of the fibers on fracture energy, flexural tensile strength, equivalent tensile strengths and residual tensile strengths of HFRC composites. Based on the experimental results and inverse analysis, a model for predicting the tensile softening diagram of HFRC composite mixes was also developed. The analytical models show conformity with the experimental results.     

界面性能对陶瓷基复合材料拉伸强度的影响

基于陶瓷基复合材料拉伸试验现象引入了主裂纹损伤带的概念, 并将其宽度定义为界面脱粘长度. 由于界面性能对纤维应力集中有较大影响, 并且控制着材料的断裂模式, 分别给出了脆性断裂和韧性断裂的强度计算公式, 并引入了应力集中系数和界面脱粘能量释放率. 分析结果表明, 拉伸强度随着应力集中系数和界面脱粘能量释放率的增大而减小. 文中公式给出的预测值与试验值吻合较好, 表明断裂时纤维所承担的应力用脱粘段纤维平均应力来衡量是合适的.     

Fracture and progressive failure of defective graphene sheets and carbon nanotubes

An atomistic based finite bond element model for the prediction of fracture and progressive failure of graphene sheets and carbon nanotubes is developed by incorporating the modified Morse potential. The element formulation includes eight degrees of freedom reducing computational cost compared to the 12 degrees of freedom used in other FE type models. The coefficients of the elements are determined based on the analytical molecular structural mechanics model developed by the authors. The model is capable of predicting the mechanical properties (Young’s moduli, Poisson’s ratios and force–strain relationships) of both defect-free and defective carbon nanotubes under different loading conditions. In particular our approach is shown to more accurately predict Poisson’s ratio. The numerical prediction of nonlinear stress–strain relationships for defect-free nanotubes including ultimate strength and strain to failure of nanotubes is identical to our analytical molecular structural mechanics solution. An interaction based mechanics approach is introduced to model the formation of Stone–Wales (5-7-7-5) topological defect. The predicted formation energy is compared with ab initio calculations. The progressive failure of defective graphene sheets and nanotubes containing a 5-7-7-5 defect is studied, and the degradation of Young’s moduli, ultimate strength and failure strains of defective nanotubes is predicted.     

考虑细观组分影响的混凝土宏观力学性能理论预测模型

混凝土宏观力学性能与其内部细观结构构造密切相关。该文建立了一类能够考虑细观组分影响的混凝土宏观力学性能理论预测模型。首先,采用细观力学数值试验法对理论模型中的参数进行了标定;进而,基于该模型对混凝土断裂能和单轴抗拉强度在材料层次的尺寸效应行为进行了分析。结果表明：混凝土断裂能和单轴抗拉强度均随骨料级配（即最大骨料粒径）发生变化,且受到界面特性的影响。当界面过渡区力学性能相对薄弱时,混凝土强度较低,断裂能和单轴抗拉强度随骨料级配增大而呈现减小的趋势;当界面过渡区力学性能较强时,混凝土强度较高,断裂能和单轴抗拉强度随骨料级配增大亦呈现增大的趋势。计算结果与试验结果吻合良好,验证了该文建立的理论预测模型的准确性和合理性。     

Evaluation of Fracture Parameters by Double-G,Double-K Models and Crack Extension Resistance for High Strength and Ultra High Strength Concrete Beams

This paper presents the advanced analytical methodologies such as Double- G and Double - K models for fracture analysis of concrete specimens made up of high strength concrete (HSC, HSC1) and ultra high strength concrete. Brief details about characterization and experimentation of HSC, HSC1 and UHSC have been provided. Double-G model is based on energy concept and couples the Griffith's brittle fracture theory with the bridging softening property of concrete. The double-K fracture model is based on stress intensity factor approach. Various fracture parameters such as cohesive fracture toughness (K_Ic^c), unstable fracture toughness (K_Ic^un) and initiation fracture toughness (K_Icⁱⁿⁱ) have been evaluated based on linear elastic fracture mechanics and nonlinear fracture mechanics principles. Double-G and double-K method uses the secant compliance at the peak point of measured P-CMOD curves for determining the effective crack length. Bi-linear tension softening model has been employed to account for cohesive stresses ahead of the crack tip. From the studies, it is observed that the fracture parameters obtained by using double - G and double - K models are in good agreement with each other. Crack extension resistance has been estimated by using the fracture parameters obtained through double - K model. It is observed that the values of the crack extension resistance at the critical unstable point are almost equal to the values of the unstable fracture toughness K_Ic^un of the materials. The computed fracture parameters will be useful for crack growth study, remaining life and residual strength evaluation of concrete structural components.     

Strength of Polycrystalline Tungsten Monocarbide under Tension

Analytical approach to determining strength of the polycrystalline tungsten monocarbide in tension is evolved from the results of microstress analysis in the polycrystal. The self-consistency method with account of residual thermal stresses has been used as the calculation scheme. The ultimate strength in tension is defined by the condition for equality of the maximum tensile stresses at the grain boundary at the instant of compression or tension fracture. The agreement between the calculated and experimental results is fairly good.     

A Model to Describe the Fracture of Porous Polygranular Graphite Subject to Neutron Damage and Radiolytic Oxidation

Two linked models have been developed to explore the relationship between the amount of porosity arising in service from both radiolytic oxidation and fast neutron damage that influences both the strength and the force-displacement (load-displacement) behaviour and crack propagation in pile grade A graphite used as a nuclear reactor moderator material. Firstly models of the microstructure of the porous graphite for both unirradiated and irradiated graphite are created. These form the input for the second stage, simulating fracture in lattice-type finite element models, which predicts force (load)-displacement and crack propagation paths. Microstructures comprising aligned filler particles, typical of needle coke, in a porous matrix have been explored. The purpose was to isolate the contributions of filler particles and porosity to fracture strength and crack paths and consider their implications for the overall failure of reactor core graphite.     

电梯用高强度螺钉断裂分析

某电梯用高强度螺钉在安装2个月后出现批量断裂现象,利用直读光谱仪、氮氢氧联合测定仪、光学显微镜、扫描电子显微镜等对螺钉断裂原因进行了分析。结果表明:该批螺钉由于镀锌后除氢不及时导致其本身氢含量较高和螺钉冷拔后未进行车削导致其在镀锌前表面就已存在大量的微小缺陷,是导致该批螺钉安装后发生断裂的两个主要原因;这些微小缺陷充当了氢陷阱并造成应力集中,在螺钉所受的静拉应力作用下,氢在缺陷处不断富集,最终导致螺钉发生氢致延迟脆性断裂。     

A New Drop‐Weight Impact Machine for Studying Fracture Processes in Structural Concrete

Abstract: This paper describes the main characteristics of a new drop‐weight impact machine that has been specifically designed for studying the dynamic mechanical behaviour of structural concrete samples. Such a type of equipment has been used to generate simple and measurable fracture processes under moderate‐to‐fast loading rates, contrary to blast chambers, which produce complicated crack patterns that are difficult to analyse. The machine consists of two main parts, the mechanical structure and the data acquisition system. The former is just a hammer, guided by two robust columns, which can impact the specimen with energy up to 7860 J. The latter consists of piezoelectric force sensors, accelerometers and optical fibre photoelectric sensors plus oscilloscopes and signal conditioners. The paper also presents the results of some preliminary tests on plain‐notched specimens that show the sensitivity of the work of fracture of a high‐strength concrete to the loading rate.     

Response of steel fiber reinforced high strength concrete beams: Experiments and code predictions

The shear-flexure response of steel fiber reinforced concrete (SFRC) beams was investigated.Thirty-six reinforced concrete beams with and without conventional shear reinforcement (stirrups) were tested under a four-point bending configuration to study the effectiveness of steel fibers on shear and flexural strengths, failure mechanisms, crack control, and ductility.The major factors considered were compressive strength (normal strength and high strength concrete up to 100 MPa), shear span-effective depth ratio (a/d = 1.5, 2.5, 3.5), and web reinforcement (none, stirrups and/or steel fibers).The response of RC beams was evaluated based on the results of crack patterns, load at first cracking, ultimate shear capacity, and failure modes.The experimental evidence showed that the addition of steel fibers improves the mechanical response, i.e., flexural and shear strengths and the ductility of the flexural members.Finally, the most recent code-based shear resistance predictions for SFRC beams were considered to discuss their reliability with respect to the experimental findings. The crack pattern predictions are also reviewed based on the major factors that affect the results.