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This paper explores the use of data-driven approximation algorithms, often called surrogate modeling, in the early-stage design of structures. The use of surrogate models to rapidly evaluate design performance can lead to a more in-depth exploration of a design space and reduce computational time of optimization algorithms. While this approach has been widely developed and used in related disciplines such as aerospace engineering, there are few examples of its application in civil engineering. This paper focuses on the general use of surrogate modeling in the design of civil structures and examines six model types that span a wide range of characteristics. Original contributions include novel metrics and visualization techniques for understanding model error and a new robustness framework that accounts for variability in model comparison. These concepts are applied to a multi-objective case study of an airport terminal design that considers both structural material volume and operational energy consumption. Key Words: surrogate modelling, machine learning, approximation, structural design

This paper presents a data analysis framework to uncover relationships between health conditions, age and sex for a large population of patients. We study a massive heterogeneous sample of 1.7 million patients in Brazil, containing 47 million of health records with detailed medical conditions for visits to medical facilities for a period of 17 months. The findings suggest that medical conditions can be grouped into clusters that share very distinctive densities in the ages of the patients. For each cluster, we further present the ICD-10 chapters within it. Finally, we relate the findings to comorbidity networks, uncovering the relation of the discovered clusters of age densities to comorbidity networks literature., Author summary Age and sex of a patient can be directly related to susceptibilities to certain medical conditions. We present a method to generate clusters of human phenotype, based on the age of the population. This method helps extract knowledge on age and sex from the data. The age and sex correlations with disease conditions can help in a task of predicting the susceptibility of incoming patients to conditions.

Our research focuses on the storage decision in a semi-automated storage system. In a semi-automated storage system, the inventory is stored on mobile storage pods. In a typical system, each storage pod carries a mixture of items, and the inventory of each item is spread over multiple storage pods. The storage pods are mobile in that a pod can be lifted and transported by a robotic drive. These storage pods are stored within a storage zone that has stationary stations for picking and stowing on its boundary. The robotic drives transport the pods to these stations at which operators conduct pick or stow operations. The storage decision is to decide to which storage location within the storage zone to return a pod upon the completion of a pick or stow operation. The storage decision has a direct impact on the total travel time, and hence the workload of the robotic drives. We develop a fluid model to analyze the performance of velocity-based storage policies. With this model, we can characterize the possible improvement from applying a velocity-based storage policy in comparison to the random storage policy that returns the pod to a randomly-chosen storage location. Within the category of velocity-based storage, we show that class-based storage with two or three classes can achieve most of the benefits from full-velocity storage. We show that the benefits from velocity-based storage increase with greater variation in the pod velocities. To validate the fluid model we build a discrete-time simulator with real industry data. We observe an 8% to 11% reduction in the travel distance with 2-class or 3-class storage system, depending on the parameter settings. From a sensitivity analysis we establish the robustness of the class-based storage policies as they continue to perform well under a broad range of warehouse settings including different zoning strategies, resource utilization levels and space utilization levels.

The voltages at which microelectromechanical actuators and sensors become unstable, known as pull-in and lift-off voltages, are critical parameters in microelectromechanical systems (MEMS) design. The state-of-the-art MEMS simulators compute these parameters by simply sweeping the voltage, leading to either excessively large computational cost or to convergence failure near the pull-in or lift-off points. This paper proposes to simulate the behavior at pull-in and lift-off employing two continuation-based algorithms. The first algorithm appropriately adapts standard continuation methods, providing a complete set of static solutions. The second algorithm uses continuation to trace two kinds of curves and generates the sweep-up or sweep-down curves, which can provide more intuition for MEMS designers. The algorithms presented in this paper are robust and suitable for general-purpose industrial MEMS designs. Our algorithms have been implemented in a commercial MEMS/integrated circuits codesign tool, and their effectiveness is validated by comparisons against measurement data and the commercial finite-element/boundary-element (FEM/BEM) solver CoventorWare.

Persuasion is at the core of norm creation, emergence of collective action, and solutions to ‘tragedy of the commons’ problems. In this paper, we show that the directionality of friendship ties affect the extent to which individuals can influence the behavior of each other. Moreover, we find that people are typically poor at perceiving the directionality of their friendship ties and that this can significantly limit their ability to engage in cooperative arrangements. This could lead to failures in establishing compatible norms, acting together, finding compromise solutions, and persuading others to act. We then suggest strategies to overcome this limitation by using two topological characteristics of the perceived friendship network. The findings of this paper have significant consequences for designing interventions that seek to harness social influence for collective action.