Developing fully parametric building models for performance-based generative design tasks often requires proficiency in many advanced 3D modeling and visual programming software, limiting its use for many building designers. Moreover, iterations of such models can be time-consuming tasks and sometimes limiting depending on the the design stage, as major changes in the layout design may result in remodeling the entire parametric definition. To address these challenges, we introduce a novel automated generative design system, which takes a basic floor plan sketch as an input and provides a parametric model prepared for multi-objective building optimization as output. In addition, the user-designer can assign various design variables for its desired building elements by using simple annotations in the drawing. We take advantage of a asymmetric convolutional module combined with a parametrizer to allow real-time parametric sketch-retrieval for a performance-based generative workflow. The system would recognize the corresponding element and define variable constraints to prepare for a multi-objective optimization problem. We illustrate the the use case of our proposed system by running a real-time structural optimization form-finding study. Our findings indicate the system can be utilized as a promising generative design tool for novice users.

Design problems are complex and not well-defined in the early stages of projects. To gain an insight into these problems, designers envision a space of various alternative solutions and explore various performance trade-offs, often manually. To assist designers with rapidly generating and exploring a design space, researchers introduced the concept of design synthesis methods. These methods promote innovative thinking and provide solutions that can augment a designer's abilities to solve problems. Recent advances in technology push the boundaries of design synthesis methods in various ways: a vast number of novel solutions can be generated using high-performance computing in a timely manner, complex geometries can be fabricated using additive manufacturing, and integrated sensors can provide feedback for the next design generation using the Internet of things (IoT). Therefore, new synthesis methods should be able to provide designs that improve over time based on the feedback they receive from the use of the products. To this end, the objective of this study is to demonstrate a design synthesis approach that, based on high-level design requirements gathered from sensor data, generates numerous alternative solutions targeted for additive manufacturing. To demonstrate this method, we present a case study of design iteration on a car chassis. First, we installed various sensors on the chassis and measured forces applied during various maneuvers. Second, we used these data to define a high-level engineering problem as a collection of design requirements and constraints. Third, using an ensemble of topology and beam-based optimization techniques, we created a number of novel solutions. Finally, we selected one of the design solutions and because of some manufacturability constraints we, 3D-printed a prototype for the next generation of design at one third scale. The results show that designs generated from the proposed method were up to 28% lighter than the existing design. This paper also presents various lessons learned to help engineers and designers with a better understanding of challenges applying new technologies in this research.

The increase in global environmental concerns as well as the advancement of computational tools and methods have had significant impacts on the way in which buildings are being designed. Building professionals are increasingly expected to improve energy performance of their design. To achieve a high level of energy performance, multidisciplinary simulation-based optimization can be utilized to help designers in exploring more design alternatives and making informed decisions. Because of the high complexity in setting up a building model for multi-objective design optimization, there is a great demand of utilizing and integrating the advanced modeling and simulation technologies, including BIM, parametric modeling, cloud-based simulation, and optimization algorithms, as well as a new user interface that facilitates the setup of building parameters (decision variables) and performance fitness functions (design objectives) for automatically generating, evaluating, and optimizing multiple design options. This paper presents an integrated framework for building information modeling (BIM)-based performance optimization, BPOpt. This framework enables designers to explore design alternatives using an open-source, visual programming user interface on the top of a widely used BIM platform, to generate models of building design options, assess the environmental performance of the models through cloud-based simulation, and search for the most appropriate design alternatives. This paper details the process of the development of BPOpt and also provides a case study to show its application. The case study demonstrates the use of BPOpt in minimizing the energy consumption while maximizing the appropriate daylighting level for a residential building. Finally, strengths, limitations, current adoption by academia and industry, and future improvements of BPOpt for high-performance building design are discussed. (C) 2015 Elsevier B.V. All rights reserved.

Building energy performance assessments are complex multi-criteria problems. Appropriate tools that can help designers explore design alternatives and assess the energy performance for choosing the most appropriate alternative are in high demand. In this paper, we present a newly developed integrated parametric Building Information Modeling (BIM)-based system to interact with cloud-based whole building energy performance simulation and daylighting tools to optimize building energy performance using a Multi-Objective Optimization (MOO) algorithm. This system enables designers to explore design alternatives using a visual programming interface, while assessing the energy performance of the design models to search for the most appropriate design. A case study of minimizing the energy use while maximizing the appropriate daylighting level of a residential building is provided to showcase the utility of the system and its workflow.

Animal models of middle ear surgery help us to explore disease processes and intervention outcomes in a manner not possible in patients. This review begins with an overview of animal models of middle ear surgery which outlines the advantages and limitations of such models. Procedures of interest include myringoplasty/tympanoplasty, mastoidectomy, ossiculoplasty, stapedectomy, and active middle ear implants. The most important issue is how well the model reflects the human response to surgery. Primates are most similar to humans with respect to anatomy; however, such studies are uncommon now due to expense and ethical issues. Conversely, small animals are easily obtained and housed, but experimental findings may not accurately represent what happens in humans. We then present a systematic review of animal models of middle ear surgery. Particular attention is paid to any distinctive anatomical features of the middle ear, the method of accessing the middle ear and the chosen outcomes. These outcomes are classified as either physiological in live animals,（e.g., behavioural or electrophysiological responses）, or anatomical in cadaveric animals,（e.g., light or electron microscopy）. Evoked physiological measures are limited by the disruption of the evoking air-conducted sound across the manipulated middle ear. The eleven identified species suitable as animal models are mouse, rat, gerbil, chinchilla, guinea pig, rabbit, cat, dog, sheep, pig and primate. Advantages and disadvantages of each species as a middle ear surgical model are outlined, and a suggested framework to aid in choosing a particular model is presented.

The effect of varying cement source on fresh and hardened concrete properties is studied under hot weather conditions. For the seven ASTM Type II cements studied here, the same mix proportioning was adopted at a mixing temperature of 95degreesF (35degreesC) with a constant dosage of water reducing and air entraining admixtures. Properties of fresh concrete including slump loss over an extended mixing period (EMP) of 90 minutes, air content, and setting times are reported. Also, hardened properties including compressive strength development and rapid chloride permeability test data are reported. Results indicate that the rate of slump loss and setting times are affected by the cement compound composition, calcium sulfate content and calcium sulfate type. The compressive strength, under hot mixing conditions, is found to be dependent on composition, fineness and morphology of cement compounds.

The short-term effects of pumping on concrete are well documented, although the long-term effects on concrete durability are not known. Pumping of concrete is widely used in large highway projects because of its convenience and economy of placement. Both types of effects were studied through collection and testing of 73 concrete samples from the Florida Department of Transportation (FDOT) bridge construction sites before and after pumping. The tests performed were air content, slump; unit weight, compressive strength, rapid chloride permeability, and water permeability. The air content and the slump of concrete decreased by about 1 percent and 13 mm (0.5 in.) on average, respectively, due to pumping. The unit weight and compressive strength of concrete increased by about 24 kg/m(3) (1.5 pcf) and 1.83 MPa (266 psi), respectively, due to pumping. Pumping decreased the water and chloride ion permeabilities in the majority of tested samples. Results show that pumping does not have detrimental effects on concrete properties; in fact, in many cases, it results in stronger, denser, and more durable concrete. Results indicate that pumping can be continued with confidence as a means of concrete placement in FDOT projects.

Pumping of concrete is widely used in large highway projects due to convenience and economy of placement. The short-term effects of pumping on the durability of concrete is well documented, whereas the long-term effects on concrete durability is not known. Both types of effects were studied herein through collection and testing of 73 concrete samples from the Florida Department of Transportation bridge construction sites before and after pumping. The tests performed were air content, slump, unit weight, compressive strength, rapid chloride permeability, and water permeability. The air content and the slump of concrete decreased by about 146 and 13 mm (0.5 in.) on average, respectively, due to pumping. The unit weight and compressive strength of concrete were found to increase by about 24 kg/m(3) (1.5 pcf) and 1.83 MPa (266 psi), respectively, due to pumping. Although test results are not statistically significant, pumping decreased the water and chloride ion permeabilities in the majority of tested samples. Results show that pumping does not have detrimental effects on concrete properties. In many cases, it results in stronger, denser, and more durable concrete. It is suggested that pumping be continued as a means of concrete placement in Florida Department of Transportation projects with confidence.