Academic literature on the topic 'Engineering - Statistical methods'

Aquesta tesi doctoral tracta sobre Enginyeria Kansei (EK), una tècnica per traslladar emocions transmeses per productes en paràmetres tècnics, i sobre mètodes estadístics que poden beneficiar la disciplina. El propòsit bàsic de l'EK és descobrir de quina manera algunes propietats d'un producte transmeten certes emocions als seus usuaris. És un mètode quantitatiu, i les dades es recullen típicament fent servir qüestionaris. S'extreuen conclusions en analitzar les dades recollides, normalment usant algun tipus d'anàlisi de regressió. L'EK es pot situar en l'àrea de recerca del disseny emocional. La tesi comença justificant la importància del disseny emocional. Com que el rang de tècniques usades sota el nom d'EK és extens i no massa clar, la tesi proposa una definició d'EK que serveix per delimitar el seu abast. A continuació, es suggereix un model per desenvolupar estudis d'EK. El model inclou el desenvolupament de l'espai semàntic – el rang d'emocions que el producte pot transmetre – i l'espai de propietats – les variables tècniques que es poden modificar en la fase de disseny. Després de la recollida de dades, l'etapa de síntesi enllaça ambdós espais (descobreix com diferents propietats del producte transmeten certes emocions). Cada pas del model s'explica detalladament usant un estudi d'EK realitzat per aquesta tesi: l'experiment dels sucs de fruites. El model inicial es va millorant progressivament durant la tesi i les dades de l'experiment es van reanalitzant usant noves propostes.Moltes inquietuds pràctiques apareixen quan s'estudia el model per a estudis d'EK esmentat anteriorment (entre d'altres, quants participants són necessaris i com es desenvolupa la sessió de recollida de dades). S'ha realitzat una extensa revisió bibliogràfica amb l'objectiu de respondre aquestes i altres preguntes. Es descriuen també les aplicacions d'EK més habituals, juntament amb comentaris sobre idees particularment interessants de diferents articles. La revisió bibliogràfica serveix també per llistar quines són les eines més comunament utilitzades en la fase de síntesi.La part central de la tesi se centra precisament en les eines per a la fase de síntesi. Eines estadístiques com la teoria de quantificació tipus I o la regressió logística ordinal s'estudien amb detall, i es proposen diverses millores. En particular, es proposa una nova forma gràfica de representar els resultats d'una regressió logística ordinal. S'introdueix una tècnica d'aprenentatge automàtic, els conjunts difusos (rough sets), i s'inclou una discussió sobre la seva idoneïtat per a estudis d'EK. S'usen conjunts de dades simulades per avaluar el comportament de les eines estadístiques suggerides, la qual cosa dóna peu a proposar algunes recomanacions.Independentment de les eines d'anàlisi utilitzades en la fase de síntesi, les conclusions seran probablement errònies quan la matriu del disseny no és adequada. Es proposa un mètode per avaluar la idoneïtat de matrius de disseny basat en l'ús de dos nous indicadors: un índex d'ortogonalitat i un índex de confusió. S'estudia l'habitualment oblidat rol de les interaccions en els estudis d'EK i es proposa un mètode per incloure una interacció, juntament amb una forma gràfica de representar-la. Finalment, l'última part de la tesi es dedica a l'escassament tractat tema de la variabilitat en els estudis d'EK. Es proposen un mètode (basat en l'anàlisi clúster) per segmentar els participants segons les seves respostes emocionals i una forma d'ordenar els participants segons la seva coherència en valorar els productes (usant un coeficient de correlació intraclasse). Com que molts usuaris d'EK no són especialistes en la interpretació de sortides numèriques, s'inclouen representacions visuals per a aquests dos nous mètodes que faciliten el processament de les conclusions.<br>Esta tesis doctoral trata sobre Ingeniería Kansei (IK), una técnica para trasladar emociones transmitidas por productos en parámetros técnicos, y sobre métodos estadísticos que pueden beneficiar la disciplina. El propósito básico de la IK es descubrir de qué manera algunas propiedades de un producto transmiten ciertas emociones a sus usuarios. Es un método cuantitativo, y los datos se recogen típicamente usando cuestionarios. Se extraen conclusiones al analizar los datos recogidos, normalmente usando algún tipo de análisis de regresión.La IK se puede situar en el área de investigación del diseño emocional. La tesis empieza justificando la importancia del diseño emocional. Como que el rango de técnicas usadas bajo el nombre de IK es extenso y no demasiado claro, la tesis propone una definición de IK que sirve para delimitar su alcance. A continuación, se sugiere un modelo para desarrollar estudios de IK. El modelo incluye el desarrollo del espacio semántico – el rango de emociones que el producto puede transmitir – y el espacio de propiedades – las variables técnicas que se pueden modificar en la fase de diseño. Después de la recogida de datos, la etapa de síntesis enlaza ambos espacios (descubre cómo distintas propiedades del producto transmiten ciertas emociones). Cada paso del modelo se explica detalladamente usando un estudio de IK realizado para esta tesis: el experimento de los zumos de frutas. El modelo inicial se va mejorando progresivamente durante la tesis y los datos del experimento se reanalizan usando nuevas propuestas. Muchas inquietudes prácticas aparecen cuando se estudia el modelo para estudios de IK mencionado anteriormente (entre otras, cuántos participantes son necesarios y cómo se desarrolla la sesión de recogida de datos). Se ha realizado una extensa revisión bibliográfica con el objetivo de responder éstas y otras preguntas. Se describen también las aplicaciones de IK más habituales, junto con comentarios sobre ideas particularmente interesantes de distintos artículos. La revisión bibliográfica sirve también para listar cuáles son las herramientas más comúnmente utilizadas en la fase de síntesis. La parte central de la tesis se centra precisamente en las herramientas para la fase de síntesis. Herramientas estadísticas como la teoría de cuantificación tipo I o la regresión logística ordinal se estudian con detalle, y se proponen varias mejoras. En particular, se propone una nueva forma gráfica de representar los resultados de una regresión logística ordinal. Se introduce una técnica de aprendizaje automático, los conjuntos difusos (rough sets), y se incluye una discusión sobre su idoneidad para estudios de IK. Se usan conjuntos de datos simulados para evaluar el comportamiento de las herramientas estadísticas sugeridas, lo que da pie a proponer algunas recomendaciones. Independientemente de las herramientas de análisis utilizadas en la fase de síntesis, las conclusiones serán probablemente erróneas cuando la matriz del diseño no es adecuada. Se propone un método para evaluar la idoneidad de matrices de diseño basado en el uso de dos nuevos indicadores: un índice de ortogonalidad y un índice de confusión. Se estudia el habitualmente olvidado rol de las interacciones en los estudios de IK y se propone un método para incluir una interacción, juntamente con una forma gráfica de representarla. Finalmente, la última parte de la tesis se dedica al escasamente tratado tema de la variabilidad en los estudios de IK. Se proponen un método (basado en el análisis clúster) para segmentar los participantes según sus respuestas emocionales y una forma de ordenar los participantes según su coherencia al valorar los productos (usando un coeficiente de correlación intraclase). Puesto que muchos usuarios de IK no son especialistas en la interpretación de salidas numéricas, se incluyen representaciones visuales para estos dos nuevos métodos que facilitan el procesamiento de las conclusiones.<br>This PhD thesis deals with Kansei Engineering (KE), a technique for translating emotions elicited by products into technical parameters, and statistical methods that can benefit the discipline. The basic purpose of KE is discovering in which way some properties of a product convey certain emotions in its users. It is a quantitative method, and data are typically collected using questionnaires. Conclusions are reached when analyzing the collected data, normally using some kind of regression analysis. Kansei Engineering can be placed under the more general area of research of emotional design. The thesis starts justifying the importance of emotional design. As the range of techniques used under the name of Kansei Engineering is rather vast and not very clear, the thesis develops a detailed definition of KE that serves the purpose of delimiting its scope. A model for conducting KE studies is then suggested. The model includes spanning the semantic space – the whole range of emotions the product can elicit – and the space of properties – the technical variables that can be modified in the design phase. After the data collection, the synthesis phase links both spaces; that is, discovers how several properties of the product elicit certain emotions. Each step of the model is explained in detail using a KE study specially performed for this thesis: the fruit juice experiment. The initial model is progressively improved during the thesis and data from the experiment are reanalyzed using the new proposals. Many practical concerns arise when looking at the above mentioned model for KE studies (among many others, how many participants are used and how the data collection session is conducted). An extensive literature review is done with the aim of answering these and other questions. The most common applications of KE are also depicted, together with comments on particular interesting ideas from several papers. The literature review also serves to list which are the most common tools used in the synthesis phase. The central part of the thesis focuses precisely in tools for the synthesis phase. Statistical tools such as quantification theory type I and ordinal logistic regression are studied in detail, and several improvements are suggested. In particular, a new graphical way to represent results from an ordinal logistic regression is proposed. An automatic learning technique, rough sets, is introduced and a discussion is included on its adequacy for KE studies. Several sets of simulated data are used to assess the behavior of the suggested statistical techniques, leading to some useful recommendations. No matter the analysis tools used in the synthesis phase, conclusions are likely to be flawed when the design matrix is not appropriate. A method to evaluate the suitability of design matrices used in KE studies is proposed, based on the use of two new indicators: an orthogonality index and a confusion index. The commonly forgotten role of interactions in KE studies is studied and a method to include an interaction in KE studies is suggested, together with a way to represent it graphically. Finally, the untreated topic of variability in KE studies is tackled in the last part of the thesis. A method (based in cluster analysis) for finding segments among subjects according to their emotional responses and a way to rank subjects based on their coherence when rating products (using an intraclass correlation coefficient) are proposed. As many users of Kansei Engineering are not specialists in the interpretation of the numerical output from statistical techniques, visual representations for these two new proposals are included to aid understanding.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 175-182).<br>Recent advances in theory and practice, have introduced a wide variety of tools from machine learning that can be applied to data intensive chemical engineering problems. This thesis covers applications of statistical learning spanning a range of relative importance of data versus existing detailed theory. In each application, the quantity and quality of data available from experimental systems are used in conjunction with an understanding of the theoretical physical laws governing system behavior to the extent they are available. A detailed generative parametric model for optical spectra of multicomponent mixtures is introduced. The application of interest is the quantification of uncertainty associated with estimating the relative abundance of mixtures of carbon nanotubes in solution. This work describes a detailed analysis of sources of uncertainty in estimation of relative abundance of chemical species in solution from optical spectroscopy. In particular, the quantification of uncertainty in mixtures with parametric uncertainty in pure component spectra is addressed. Markov Chain Monte Carlo methods are utilized to quantify uncertainty in these situations and the inaccuracy and potential for error in simpler methods is demonstrated. Strategies to improve estimation accuracy and reduce uncertainty in practical experimental situations are developed including when multiple measurements are available and with sequential data. The utilization of computational Bayesian inference in chemometric problems shows great promise in a wide variety of practical experimental applications. A related deconvolution problem is addressed in which a detailed physical model is not available, but the objective of analysis is to map from a measured vector valued signal to a sum of an unknown number of discrete contributions. The data analyzed in this application is electrical signals generated from a free surface electro-spinning apparatus. In this information poor system, MAP estimation is used to reduce the variance in estimates of the physical parameters of interest. The formulation of the estimation problem in a probabilistic context allows for the introduction of prior knowledge to compensate for a high dimensional ill-conditioned inverse problem. The estimates from this work are used to develop a productivity model expanding on previous work and showing how the uncertainty from estimation impacts system understanding. A new machine learning based method for monitoring for anomalous behavior in production oil wells is reported. The method entails a transformation of the available time series of measurements into a high-dimensional feature space representation. This transformation yields results which can be treated as static independent measurements. A new method for feature selection in one-class classification problems is developed based on approximate knowledge of the state of the system. An extension of features space transformation methods on time series data is introduced to handle multivariate data in large computationally burdensome domains by using sparse feature extraction methods. As a whole these projects demonstrate the application of modern statistical modeling methods, to achieve superior results in data driven chemical engineering challenges.<br>by Mark Christopher Molaro.<br>Ph. D.

Models which describe the performance of physical process are essential for quality prediction, experimental planning, process control and optimization. Engineering models developed based on the underlying physics/mechanics of the process such as analytic models or finite element models are widely used to capture the deterministic trend of the process. However, there usually exists stochastic randomness in the system which may introduce the discrepancy between physics-based model predictions and observations in reality. Alternatively, statistical models can be used to develop models to obtain predictions purely based on the data generated from the process. However, such models tend to perform poorly when predictions are made away from the observed data points. This dissertation contributes to model enhancement research by integrating physics-based model and statistical model to mitigate the individual drawbacks and provide models with better accuracy by combining the strengths of both models. The proposed model enhancement methodologies including the following two streams: (1) data-driven enhancement approach and (2) engineering-driven enhancement approach. Through these efforts, more adequate models are obtained, which leads to better performance in system forecasting, process monitoring and decision optimization. Among different data-driven enhancement approaches, Gaussian Process (GP) model provides a powerful methodology for calibrating a physical model in the presence of model uncertainties. However, if the data contain systematic experimental errors, the GP model can lead to an unnecessarily complex adjustment of the physical model. In Chapter 2, we proposed a novel enhancement procedure, named as "Minimal Adjustment", which brings the physical model closer to the data by making minimal changes to it. This is achieved by approximating the GP model by a linear regression model and then applying a simultaneous variable selection of the model and experimental bias terms. Two real examples and simulations are presented to demonstrate the advantages of the proposed approach. Different from enhancing the model based on data-driven perspective, an alternative approach is to focus on adjusting the model by incorporating the additional domain or engineering knowledge when available. This often leads to models that are very simple and easy to interpret. The concepts of engineering-driven enhancement are carried out through two applications to demonstrate the proposed methodologies. In the first application where polymer composite quality is focused, nanoparticle dispersion has been identified as a crucial factor affecting the mechanical properties. Transmission Electron Microscopy (TEM) images are commonly used to represent nanoparticle dispersion without further quantifications on its characteristics. In Chapter 3, we developed the engineering-driven nonhomogeneous Poisson random field modeling strategy to characterize nanoparticle dispersion status of nanocomposite polymer, which quantitatively represents the nanomaterial quality presented through image data. The model parameters are estimated through the Bayesian MCMC technique to overcome the challenge of limited amount of accessible data due to the time consuming sampling schemes. The second application is to calibrate the engineering-driven force models of laser-assisted micro milling (LAMM) process statistically, which facilitates a systematic understanding and optimization of targeted processes. In Chapter 4, the force prediction interval has been derived by incorporating the variability in the runout parameters as well as the variability in the measured cutting forces. The experimental results indicate that the model predicts the cutting force profile with good accuracy using a 95% confidence interval. To conclude, this dissertation is the research drawing attention to model enhancement, which has considerable impacts on modeling, design, and optimization of various processes and systems. The fundamental methodologies of model enhancement are developed and further applied to various applications. These research activities developed engineering compliant models for adequate system predictions based on observational data with complex variable relationships and uncertainty, which facilitate process planning, monitoring, and real-time control.

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.<br>Includes bibliographical references (p. 77-79).<br>by Frederick George Walls.<br>M.Eng.

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.<br>Includes bibliographical references (p. 88-93).<br>by Luis Carlos Maas III.<br>M.S.

In this thesis, there are generally three contributions to the Ranking and Selection problem in discrete-event simulation area. Ranking and selection is an important problem when people want to select single or multiple best designs from alternative pool. There are two different types in discrete-event simulation: terminating simulation and steady-state simulation. For steady-state simulation, there is an initial trend before the data output enters into the steady-state, if we cannot start the simulation from steady state. We need to remove the initial trend before we use the data to estimate the steady-state mean. Our first contribution regards the application to eliminate the initial trend/initialization bias. In this thesis, we present a novel solution to remove the initial trend motivated by offline change detection method. The method is designed to monitor the cumulative absolute bias from the estimated steady-state mean. Experiments are conducted to compare our procedure with other existing methods. Our method is shown to be at least no worse than those methods and in some cases much better. After removing the initialization bias, we can apply a ranking and selection procedure for the data outputs from steady-state simulation. There are two main approaches to ranking and selection problem. One is subset selection and the other one is indifference zone selection. Also by employing directed graph, some single-best ranking and selection methods can be extended to solve multi-best selection problem. Our method is designed to solve multi-best ranking and selection. And in Chapter 3, one procedure for ranking and selection in terminating simulation is extended based full sequential idea. It means we compare the sample means among all systems in contention at each stage. Also, we add a technique to do pre-selection of the superior systems at the same time of eliminating inferior systems. This can accelerate the speed of obtaining the number of best systems we want. Experiments are conducted to demonstrate the pre-selection technique can save observation significantly compared with the procedure without it. Also compared with existing methods, our procedure can save significant number of observations. We also explore the effect of common random number. By using it in the simulation process, more observations can be saved. The third contribution of this thesis is to extend the procedure in Chapter 3 for steady-state simulation. Asymptotic variance is employed in this case. We justify our procedure in asymptotic point of view. And by doing extensive experiments, we demonstrate that our procedure can work in most cases when sample size is finite

This master’s thesis work was performed at Optimum Biometric Labs, OBL, located in Karlskrona, Sweden. Optimum Biometric Labs perform independent scenario evaluations to companies who develop biometric devices. The company has a product Optimum preConTM which is surveillance and diagnosis tool for biometric systems. This thesis work’s objective was to develop a conceptual model and implement it as an additional layer above the biometric layer with intelligence about the biometric users. The layer is influenced by the general procedure of biometrics in a multimodal behavioural way. It is working in an unsupervised way and performs in an unsupervised manner. While biometric systems are increasingly adopted the technologies have some inherent problems such as false match and false non-match. In practice, a rejected user can not be interpreted as an impostor since the user simply might have problems using his/her biometric feature. The proposed methods in this project are dealing with these problems when analysing biometric usage in runtime. Another fact which may give rise to false rejections is template aging; a phenomenon where the enrolled user’s template is too old compared towards the user’s current biometric feature. A theoretical approach of template aging was known; however since the analysis of template aging detection was correlated with potential system flaws such as device defects or human generated risks such as impostor attacks this task would become difficult to solve in an unsupervised system but when ignoring the definition of template aging, the detection of similar effects was possible. One of the objectives of this project was to detect template aging in a predictive sense; this task failed to be carried out because the absence of basis performing this kind of tasks. The developed program performs abnormality detection at each incoming event from a biometric system. Each verification attempt is assumed to be from a genuine user unless any deviation according to the user&apos;s history is found, an abnormality. The possibility of an impostor attack depends on the degree of the abnormality. The application makes relative decisions between fraud possibilities or if genuine user was the source of what caused the deviations. This is presented as an alarm with the degree of impostor possibility. This intelligent layer has increased Optimum preCon´s capacity as a surveillance tool for biometrics. This product is an efficient complement to biometric systems in a steady up-going worldwide market.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 209-220).<br>An outstanding challenge in many problems throughout science and engineering is to succinctly characterize the relationships among a large number of interacting entities. Models based on graphs form one major thrust in this thesis, as graphs often provide a concise representation of the interactions among a large set of variables. A second major emphasis of this thesis are classes of structured models that satisfy certain algebraic constraints. The common theme underlying these approaches is the development of computational methods based on convex optimization, which are in turn useful in a broad array of problems in signal processing and machine learning. The specific contributions are as follows: -- We propose a convex optimization method for decomposing the sum of a sparse matrix and a low-rank matrix into the individual components. Based on new rank-sparsity uncertainty principles, we give conditions under which the convex program exactly recovers the underlying components. -- Building on the previous point, we describe a convex optimization approach to latent variable Gaussian graphical model selection. We provide theoretical guarantees of the statistical consistency of this convex program in the high-dimensional scaling regime in which the number of latent/observed variables grows with the number of samples of the observed variables. The algebraic varieties of sparse and low-rank matrices play a prominent role in this analysis. -- We present a general convex optimization formulation for linear inverse problems, in which we have limited measurements in the form of linear functionals of a signal or model of interest. When these underlying models have algebraic structure, the resulting convex programs can be solved exactly or approximately via semidefinite programming. We provide sharp estimates (based on computing certain Gaussian statistics related to the underlying model geometry) of the number of generic linear measurements required for exact and robust recovery in a variety of settings. -- We present convex graph invariants, which are invariants of a graph that are convex functions of the underlying adjacency matrix. Graph invariants characterize structural properties of a graph that do not depend on the labeling of the nodes; convex graph invariants constitute an important subclass, and they provide a systematic and unified computational framework based on convex optimization for solving a number of interesting graph problems. We emphasize a unified view of the underlying convex geometry common to these different frameworks. We describe applications of both these methods to problems in financial modeling and network analysis, and conclude with a discussion of directions for future research.<br>by Venkat Chandrasekaran.<br>Ph.D.

This thesis provides a comparison of two adaptive image magnification algorithms selected from the literature. Following implementation and experimentation with the algorithms, a number of improvements are proposed. The first selected algorithm takes an edge-directed approach by using an estimation of the edge map of the high-resolution image to guide the interpolation process. It was found that this algorithm suffered from certain inaccuracies in the edge detection stage. The proposed improvements focus on methods for increasing the accuracy of edge detection. The second selected algorithm takes a statistical approach by modelling the high-resolution image as a Gibbs-Markov random field and solving with the maximum a posteriori estimation technique. It was found that this algorithm suffered from blurring caused by the general way in which the clique potentials are applied to every sample. The proposed improvements introduce a set of weights to prevent smoothing across discontinuities. The two selected algorithms are compared to the enhanced versions to demonstrate the merit of the proposed improvements. Results have shown significant improvements in the quality of the magnified test images. In particular, blurring was reduced and edge sharpness was enhanced.

Both the disciplines of design and statistics have promoted projects and research with clear objectives in their field, but for the other discipline, they have been difficult or challenging to fully understand.&#x0D; &#x0D; In design, there are a large number of projects that provoke a reaction in spectators or users as they have a spectacular scope and impact, but at a statistical level, their results add little value. On the other hand, in some of the models and applications that are often used in statistics, the requirements are highly complex and numerous. This makes it difficult to put theory into practice since experiences or experiments that are so complex and difficult to manage cannot be carried out. In addition, the subsequent reporting process for non-experts is difficult to understand due to a large amount of information as well as on poorly designed presentations at times.&#x0D; &#x0D; After understanding the limitations that the two disciplines face, their ability to work together and turn one another’s weaknesses into a more complete and holistic solution is evident. Kansei engineering is also a good example since it is a complex design tool, and the only way to advance it incorporating the use of data is through collaboration between designers and statisticians. In this paper, the Data Collection Toolkit is presented as a result of applying Kansei Engineering to unite these two disciplines including some methodologies, resources, and tools for designers for each step.

This paper presents the use of statistically rigorous algorithms combined with active-sensing impedance methods for damage identification in engineering systems. In particular, we propose to use statistical pattern recognition methods to address damage classification and data mining issues associated with the examination of large numbers of impedance signals for health monitoring applications. The impedance-based structural health monitoring technique, which utilizes electromechanical coupling properties of piezoelectric materials, has shown feasibility for use in a variety of damage identification applications. Relying on high frequency local excitations (typically &amp;gt; 30 kHz), this technique is very sensitive to minor changes in structural integrity in the near field of piezoelectric sensors. In this study, in order to diagnosis damage with levels of statistical confidence, the impedance-based monitoring is cast in the context of an outlier detection framework. A modified autoregressive model with exogenous inputs (ARX) in the frequency domain is developed. The damage sensitive feature is then computed by differentiating the measured impedance and the output of the ARX model. Furthermore, because of the non-Gaussian nature of the feature distribution tails, extreme value statistics (EVS) are employed to develop a robust damage classifier. By incorporating EVS, we establish a rigorous impedance-based health monitoring algorithm, which is able to provide structural systems with self-contained and self-diagnostic components. This paper concludes with a numerical example on a 5 degree-of-freedom system and an experimental investigation on a multi-story building model to demonstrate the performance of the proposed concept.

This report presents the latest methods of optimisation under uncertainties investigated in the ExaQUte project, and their applications to problems related to civil and wind engineering. The measure of risk throughout the report is the conditional value at risk. First, the reference method is presented: the derivation of sensitivities of the risk measure; their accurate computation; and lastly, a practical optimisation algorithm with adaptive statistical estimation. Second, this method is directly applied to a nonlinear relaxation oscillator (FitzHugh–Nagumo model) with numerical experiments to demonstrate its performance. Third, the optimisation method is adapted to the shape optimisation of an airfoil and illustrated by a large-scale experiment on a computing cluster. Finally, the benchmark of the shape optimisation of a tall building under a turbulent flow is presented, followed by an adaptation of the optimisation method. All numerical experiments showcase the open-source software stack of the ExaQUte project for large-scale computing in a distributed environment.

The summative external evaluation report described the program's impact on faculty and students participating in recitation sessions and active teaching professional development sessions over two years. Student persistence and retention in engineering courses continue to be a challenge in undergraduate education, especially for students underrepresented in engineering disciplines. The program's goal was to use peer-facilitated instruction in core engineering courses known to have high attrition rates to retain underrepresented students, especially women, in engineering to diversify and broaden engineering participation. Knowledge generated around using peer-facilitated instruction at two-year colleges can improve underrepresented students' success and participation in engineering across a broad range of institutions. Students in the program participated in peer-facilitated recitation sessions linked to fundamental engineering courses, such as engineering analysis, statics, and dynamics. These courses have the highest failure rate among women and underrepresented minority students. As a mixed-methods evaluation study, student engagement was measured as students' comfort with asking questions, collaboration with peers, and applying mathematics concepts. SPSS was used to analyze pre-and post-surveys for statistical significance. Qualitative data were collected through classroom observations and focus group sessions with recitation leaders. Semi-structured interviews were conducted with faculty members and students to understand their experiences in the program. Findings revealed that women students had marginalization and intimidation perceptions primarily from courses with significantly more men than women. However, they shared numerous strategies that could support them towards success through the engineering pathway. Women and underrepresented students perceived that they did not have a network of peers and faculty as role models to identify within engineering disciplines. The recitation sessions had a positive social impact on Hispanic women. As opportunities to collaborate increased, Hispanic womens' social engagement was expected to increase. This social engagement level has already been predicted to increase women students' persistence and retention in engineering and result in them not leaving the engineering pathway. An analysis of quantitative survey data from students in the three engineering courses revealed a significant effect of race and ethnicity for comfort in asking questions in class, collaborating with peers outside the classroom, and applying mathematical concepts. Further examination of this effect for comfort with asking questions in class revealed that comfort asking questions was driven by one or two extreme post-test scores of Asian students. A follow-up ANOVA for this item revealed that Asian women reported feeling excluded in the classroom. However, it was difficult to determine whether these differences are stable given the small sample size for students identifying as Asian. Furthermore, gender differences were significant for comfort in communicating with professors and peers. Overall, women reported less comfort communicating with their professors than men. Results from student metrics will inform faculty professional development efforts to increase faculty support and maximize student engagement, persistence, and retention in engineering courses at community colleges. Summative results from this project could inform the national STEM community about recitation support to further improve undergraduate engineering learning and educational research.

Computational tools based on probabilistic fracture mechanics have been developed to enable reliability-based fitness-for-service assessments of flawed girth welds. The same tools are readily adapted for establishing maximum allowable defect sizes to achieve targeted weld reliability. Sensitivity studies have shown that of the various input parameter uncertainties, measured defect height often has the greatest impact on the probabilities of both fracture and plastic collapse. A reduction in sizing uncertainty should thus dramatically improve predicted reliabilities. The increasing use of mechanized ultrasonic testing (UT) in pipeline construction, driven by the demands of engineering critical assessment (ECA) -based acceptance criteria, highlights the need to quantify this uncertainty, particularly for systems incorporating pulse-echo (P/E) and time-of-flight diffraction (TOFD) methods and phased-array (PA) technology. EWI collected third-party independent data and statistically characterized the systematic and random errors in girth weld defect sizing, as measured by mechanized UT using P/E and TOFD methods, as well as PA ultrasonic technology, in support of pipeline reliability assessments.

This report describes several advances in the cyclic failure assessment of silt soils with immediate and practical benefit to the geotechnical earthquake engineering profession. First, a database of cyclic loading test data is assembled, evaluated, and used to assess trends in the curvature of the CRR-N (cyclic resistance ratio - the number of equivalent cycles) relationship. This effort culminated in a plasticity index-dependent function which can be used to estimate the exponent b in the power law describing cyclic resistance, and may be used to estimate the cyclic resistance of silt soils as well as the number of equivalent loading cycles anticipated for subduction zone earthquakes. Statistical models for the cyclic resistance ratio and cyclic strength ratio are presented in this report. The SHANSEP (Stress History and Normalized Soil Engineering Properties)-inspired functional form of these models have been trained and tested against independent datasets and finalized using a combined dataset to provide reasonable estimates of resistance based on the available data. These models can be used to provide provisional estimates of the CRR-N and cyclic strength ratio power laws for cyclic shear strain failure criteria ranging from 1 to 10%, within certain stated limitations. The ground motion records within the NGA Subduction Project which have been released to the public to-date are implemented to examine the role of subduction zone earthquake characteristics on the number of equivalent loading cycles for a wide range of soils with exponents b ranging from 0.05 (moderate plasticity silt and clay) to 0.35 (dense sand). This analysis shows that the number of loading cycles for a given magnitude subduction zone earthquake is larger than those previously computed, whereas the corresponding magnitude scaling factors for use with the Simplified Method span a smaller range as a result of the ground motion characteristics. Owing to the large variability in the computed equivalent number of loading cycles, consideration of the uncertainty is emphasized in forward analyses. The work described herein may be used to estimate cyclic resistance of intact non-plastic and plastic silt soils and corresponding factor of safety against cyclic failure for a range in cyclic shear strain failure criteria, to plan cyclic laboratory testing programs, and to calibrate models for use in site response and nonlinear deformation analyses in the absence of site-specific cyclic test data. As with any empirical approach, the models presented herein should be revised when additional, high-quality cyclic testing data become available.

This paper reviews the current research status of fatigue performance of high-strength steels, introduces the basic theory of fatigue life and current design methods, the fatigue test data of a large number of high-strength steels are statistically collected, and the fatigue properties of high-strength steel base material and different connection structures are analyzed and evaluated. The research results show that high-strength steels with yield strength greater than 420MPa show better fatigue resistance, and the fatigue strength is significantly higher than the calculated value. The design curve of the code is conservative, which underestimates the fatigue resistance of the steel. The fatigue strength of the highstrength steel weld joints is increased by a small margin, and it is in good agreement with the AISC360 and EC3 code curves. Due to the influence of welding quality and welding process, the data is more discrete and requires more test data to support. Fatigue strength of bolted connections is affected by many factors such as bolt preload, friction surface treatment process, and hole formation methods. Studies have shown that AISC360 and BS7608 design curves are suitable for fatigue life analysis of Q460 bolted connections, and for fatigue performance calculations for Q690 steel bolted connections, the normative approach is conservative. With the increasing application of high-strength steels, the current code needs to be supplemented with the latest research results and engineering experience of new materials in a timely manner.

The key to the high-efficiency performance of the suspen-dome structure is to apply the pre-stressed design value to the structure accurately. However, engineering practice has found that the use of tensioning hoop cables to apply the pre-stress will produce noticeable pre-stressed friction loss (PFL), which significantly affects the safety performance of the structure. In this paper, based on a 1:10 scaled-down experiment model of a suspen-dome structure with rolling cable-strut joint installed, the random PFL (RPFL) effect of the suspen-dome on structure performance was analyzed through a probability statistics theory. First, aiming at the unequal tensioning force at both sides of the tensioned hoop cable during the tensioning process, a pre-stressed force calculation method is proposed that considers the unequal tensioning control force and RPFL at all cable–strut joints, and the reliability of this method is verified through a tension test. Then, based on the cable-joint tension test carried out in the early stage of the research group, a random mathematical model of the friction coefficient (FC) at the rolling cable–strut joint is established. And then, the cable force calculation method is used to establish the random finite element model, and independent and random changes in the FC at each rolling cable–strut joint can be considered. Subsequently, the Monte Carlo method is used to calculate the random mathematical characteristics of the mechanical performance parameters such as the member stress and joint deformation, and the obtained results are verified through a static loading experiment. In addition, to investigate the effect of random defects on structural stability, other random defects, such as the initial curvature and installation deviation, were continuously introduce based on the random finite element model. As such, we could obtain the law of the effect of multi-defect random variation coupling on the structure’s ultimate bearing capacity.