Sanjay R. Arwade

Computational Analysis of Randomness in Structural Mechanics

This high-quality book captures in one concise volume the importance of probabilistic analysis of structures, the theory behind such analysis, and practical methods for solving structural mechanics problems that contain uncertainty. It provides a crucial gateway to probabilistic structural mechanics for instructors and students as well as researchers and practitioners who may have directed most of their efforts towards deterministic problems. The book provides an excellent, self-contained introduction to random vibrations, stochastic finite elements, and reliability analysis while also preparing the reader for more advanced study based on the many more specialized texts available in these topics. The first three chapters form the first part of the book, in which the elements of probability and statistics are introduced with sufficient rigour to prepare the reader with even limited experience in probability and statistics to benefit from the second part of the book. Chapter 1 motivates the study of randomness in structural mechanics through a series of fully worked example problems that immediately illustrate the key concepts of the remainder of the book. Chapter 2 presents the key elements of probability and statistics, such as random variables and distributions, expectation, estimation and Monte Carlo sampling. These concepts are used freely throughout the rest of the book, and provision of this chapter renders the book fully self-contained. Chapter 3 introduces regression, and the related idea of the response surface for complex probabilistic systems. The next and final three chapters form the technical core of the book. Chapter 4, on random vibrations, covers the solution of single and multi-degree of freedom systems using numerical, exact and approximate methods. Chapter 5 provides a concise, accessible description of how the stochastic finite element method may be used to solve problems involving spatial uncertainty and an introduction to the theory of random fields. Methods for calculating structural reliability, such as first-order reliability and Monte Carlo simulation with importance sampling are the focus of chapter 6, which also shows how response surfaces can be applied to reliability analysis. Aside from the timely, rigourous and thorough technical content, the book has several features to recommend it. First, it focuses intently on problems and examples of obvious relevance to students and practitioners of structural engineering. Second, the theoretical developments introduced are thoroughly illustrated with fully worked examples, and a smattering of exercises left for the reader give the reader the opportunity to practice implementing the presented techniques. Third, most of the analysis techniques described are accompanied by listings of Octave computer code designed to solve relevant examples. These listings are fully compatible with MATLAB and are a very valuable component of the book. A fine semester-long graduate course could be designed around the content of this book, and some of the material could even be reasonably included in the upper-level undergraduate curriculum, providing the connection between the probability and statistics course and the analysis and design classes that is too often lacking. At the graduate level, the book will have particular value as the basis for a general course in probabilistic structural mechanics in those departments where faculty staffing and expertise do not allow the offering of individual specialty courses in random vibrations, reliability and stochastic finite elements. In short, Computational Analysis of Randomness in Structural Mechanics will prove a valuable resource to any student or practitioner of structural engineering and mechanics.

Development of Laminated Bamboo Lumber: Review of Processing, Performance, and Economical Considerations

As focus is drawn toward more sustainable construction practices, use of bamboo as a structural building material is growing as a topic of interest. It is highly renewable, has low-embodied energy, and has the highest strength-to-weight ratio of steel, concrete, and timber. Composite lumber made from bamboo, termed laminated bamboo lumber (LBL), has gained the particular interest of researchers and practitioners of late, since it has bamboo’s mechanical properties but can be manufactured in well-defined dimensions, similar to commercially available wood products. Its primary drawbacks are that it is difficult to connect and is more costly than competing, locally available materials. This paper presents the advantages and challenges of embracing LBL as an alternative building material. Experimental and analytical data on production, performance, economics, and environmental impact of bamboo and LBL are reviewed, synthesized, and further analyzed to present an overview of the viability of using bamboo as a ...

Burrowing in marine muds by crack propagation: kinematics and forces

SUMMARY The polychaete Nereis virens burrows through muddy sediments by exerting dorsoventral forces against the walls of its tongue-depressor-shaped burrow to extend an oblate hemispheroidal crack. Stress is concentrated at the crack tip, which extends when the stress intensity factor (KI) exceeds the critical stress intensity factor (KIc). Relevant forces were measured in gelatin, an analog for elastic muds, by photoelastic stress analysis, and were 0.015±0.001 N (mean ± s.d.; N=5). Measured elastic moduli (E) for gelatin and sediment were used in finite element models to convert the forces in gelatin to those required in muds to maintain the same body shapes observed in gelatin. The force increases directly with increasing sediment stiffness, and is 0.16 N for measured sediment stiffness of E=2.7×104 Pa. This measurement of forces exerted by burrowers is the first that explicitly considers the mechanical behavior of the sediment. Calculated stress intensity factors fall within the range of critical values for gelatin and exceed those for sediment, showing that crack propagation is a mechanically feasible mechanism of burrowing. The pharynx extends anteriorly as it everts, extending the crack tip only as far as the anterior of the worm, consistent with wedge-driven fracture and drawing obvious parallels between soft-bodied burrowers and more rigid, wedge-shaped burrowers (i.e. clams). Our results raise questions about the reputed high energetic cost of burrowing and emphasize the need for better understanding of sediment mechanics to quantify external energy expenditure during burrowing.

Probabilistic Models for Wind Turbine and Wind Farm Performance

A Markov model for the performance of wind turbines is developed that accounts for component reliability and the effect of wind speed and turbine capacity on component reliability. The model is calibrated to the observed performance of offshore turbines in the north of Europe, and uses wind records obtained from the coast of the state of Maine in the northeast United States in simulation. Simulation results indicate availability of 0.91, with mean residence time in the operating state that is nearly exponential and has a mean of 42 days. Using a power curve typical for a 2.5 MW turbine, the capacity factor is found to be beta distributed and highly non-Gaussian. Noticeable seasonal variation in turbine and farm performance metrics are observed and result from seasonal fluctuations in the characteristics of the wind record. The input parameters to the Markov model, as defined in this paper, are limited to those for which field data are available for calibration. Nevertheless, the framework of the model is readily adaptable to include, for example: site specific conditions; turbine details; wake induced loading effects; component redundancies; and dependencies. An on-off model is introduced as an approximation to the stochastic process describing the operating state of a wind turbine, and from this onoff process an Ornstein–Uhlenbeck (O–U) process is developed as a model for the availability of a wind farm. The O–U model agrees well with Monte Carlo (MC) simulation of the Markov model and is accepted as a valid approximation. Using the O–U model in design and management of large wind farms will be advantageous because it can provide statistics of wind farm performance without resort to intensive large scale MC simulation. [DOI: 10.1115/1.4004273]

Measurement and Stochastic Computational Modeling of the Elastic Properties of Parallel Strand Lumber

This paper describes a model for the spatial variation of the elastic modulus of parallel strand lumber (PSL) that is based on bending experiments and also describes a validated stochastic computational model that incorporates orthotropic elasticity and uncertainty in strand geometry and material properties. The PSL exhibits significant variability both within members and between members, but this variability is less than that of equivalent sawn-wood members, and decreases with increasing member size. The correlation length of the elastic modulus is found to be several meters and is independent of the cross-sectional size. The variance of PSL elastic modulus is found to scale inversely with the number of strands in the cross section. The validated computational model is flexible enough to allow preliminary exploration of the properties of new mixes of species and strand sizes in PSL material design.

The effect of crown architecture on dynamic amplification factor of an open-grown sugar maple (Acer saccharum L.)

Tree failure may cause significant economic and societal disruptions in urban environments. A better understanding of the relationship between branches and stem as they affect the dynamic response of decurrent trees under wind loading is needed to reduce the risk of tree failure. Finite element (FE) models were used to identify the parameters that primarily impact tree response. A base model was developed using data from a sugar maple (Acer saccharum L.) located in Belchertown, MA, USA, from which parametric models were subsequently developed. Confidence in the base model was gained by comparing the natural frequency of this tree with experimental results. Results from a parametric study incorporating changes in eight different tree parameters (stem diameter, slenderness ratio of branches, number of branches, damping ratio, branch attachment heights, branch attachment angles, branch azimuth angles, and elastic modulus) are then presented to help identify critical model properties that affect the dynamic amplification factor (Rd) of the tree. A single parameter was varied in each model while keeping others unchanged from the base model. Parameters with the greatest effect on Rd included stem diameter, number and slenderness of branches in the crown, elastic modulus of stem and branches, and damping ratio. Thus, it may be possible to use pruning to alter crown architecture to reduce the risk of tree failure.Tree failure may cause significant economic and societal disruptions in urban environments. A better understanding of the relationship between branches and stem as they affect the dynamic response of decurrent trees under wind loading is needed to reduce the risk of tree failure. Finite element (FE) models were used to identify the parameters that primarily impact tree response. A base model was developed using data from a sugar maple (Acer saccharum L.) located in Belchertown, MA, USA, from which parametric models were subsequently developed. Confidence in the base model was gained by comparing the natural frequency of this tree with experimental results. Results from a parametric study incorporating changes in eight different tree parameters (stem diameter, slenderness ratio of branches, number of branches, damping ratio, branch attachment heights, branch attachment angles, branch azimuth angles, and elastic modulus) are then presented to help identify critical model properties that affect the dynamic amplification factor (Rd) of the tree. A single parameter was varied in each model while keeping others unchanged from the base model. Parameters with the greatest effect on Rd included stem diameter, number and slenderness of branches in the crown, elastic modulus of stem and branches, and damping ratio. Thus, it may be possible to use pruning to alter crown architecture to reduce the risk of tree failure.

A Monte Carlo simulation model for stationary non-Gaussian processes

A class of stationary non-Gaussian processes, referred to as the class of mixtures of translation processes, is defined by their finite dimensional distributions consisting of mixtures of finite dimensional distributions of translation processes. The class of mixtures of translation processes includes translation processes and is useful for both Monte Carlo simulation and analytical studies. As for translation processes, the mixture of translation processes can have a wide range of marginal distributions and correlation functions. Moreover, these processes can match a broader range of second order correlation functions than translation processes. The paper also develops an algorithm for generating samples of any non-Gaussian process in the class of mixtures of translation processes. The algorithm is based on the sampling representation theorem for stochastic processes and properties of the conditional distributions. Examples are presented to illustrate the proposed Monte Carlo algorithm and compare features of translation processes and mixture of translation processes. q 2003 Elsevier Science Ltd. All rights reserved.

Variability of the Compressive Strength of Parallel Strand Lumber

Measurement of the compressive strength of parallel strand lumber (PSL) is conducted on specimens of varying size with nominally identical mesostructure. The mean of the compressive strength is found to vary inversely with the specimen size, and the coefficient of variation of the strength is found to decrease with increasing specimen size, and to be smaller than the coefficient of variation of strength for solid lumber. The correlation length of the compressive strength is approximately 0.5 m, and this correlation length leads to significant specimen-to-specimen variation in mean strength. A computational model is developed that includes the following properties of the PSL mesostructure: the strand length, the grain angle, the elastic constants, and the parameters of the Tsai-Hill failure surface. The computational model predicts the mean strength and coefficient of variation reasonably well, and predicts the correct form of correlation decay, but overpredicts the correlation length for compressive strength, likely because of sensitivity to the distribution of strand length used in the model. The estimates of the statistics of the PSL compressive strength are useful for reliability analysis of PSL structures, and the computational model, although still in need of further development, can be used in evaluating the effect of mesostructural parameters on PSL compressive strength.

Variability of breaking wave characteristics and impact loads on offshore wind turbines supported by monopiles

Most existing and planned offshore wind turbines (OWTs) are located in shallow water where the possibility of breaking waves impacting the structure may influence design. Breaking waves and their associated impact loads are challenging to model because the breaking process is a strongly non-linear phenomenon with significant statistical scattering. Given the challenges and uncertainty in modeling breaking waves, there is a need for comparing existing models with simultaneous environmental and structural measurements taken from utility-scale OWTs exposed to breaking waves. Overall, such measurements are lacking; however, one exception is the Offshore Wind Turbines at Exposed Sites project, which recorded sea state conditions and associated structural loads for a 2.0 MW OWT supported by a monopile and located at the Blyth wind farm off the coast of England. Measurements were recorded over a 17 month campaign between 2001 and 2003, a period that included a storm that exposed the instrumented OWT to dozens of breaking waves. This paper uses the measurements from this campaign to categorize and identify breaking waves and quantify the variability of their impact loads. For this particular site and turbine, the distribution of measured mudline moments due to breaking waves has a mean of 8.7MN-m, a coefficient of variation of 26% and a maximum of 14.9MN-m. The accuracy of several breaking wave limits and impact force models is compared with the measurements, and the impact force models are shown to represent the measurements with varying accuracy and to be sensitive to modeling assumptions. Copyright

Structural Configuration and Building Energy Performance

The civilengineering andarchitecturalcommunities arehighlyfocusedthesedays ondesigningbuildingsthatmaximizeutilization ofenergyavailablefromnaturalresourcesthroughmeanssuchaspassivesolarheatingandpassiveventilationandminimizingtheconsumption ofenergyproducedexternaltothebuildingitself.Indeed,so-callednet-zero-energybuildings,whichwouldrequirenonetenergyinputfortheir operation, have been identified as an aspirational goal for architects and engineers. It has been suggested that for each of the four major climate zones there exists an optimal building morphology,consisting offloor plan geometry and placement of the primary structural system for lateral loads, the structural core or wall, which contains major mechanical services and vertical transportation conduits. This paper presents a quanti- tative study of the effect of building morphology on energy performance in each of the four climate zones. The energy analysis is performed using Autodesk Ecotect Analysis 2011. Four building morphologies are investigated, each representing a high-rise commercial building with equivalent area, height, and material use. For comparison, results are presented in terms of annual sensible heating and cooling loads. A three- dimensional rendering of how the different building types might respond under wind loads is presented to indicate how the environmental and structural performances become coupled when the building is designed onlywith environmental performance in mind.DOI:10.1061/(ASCE) AE.1943-5568.0000103.