Designing a student dorm wayfinding sign system

This study estimated availability of an air compression system and a nitrogen generation system in liquefied natural gas—floating production storage and offloading unit (LNG-FPSO) with different design stages to investigate the gap between the availability at the early design stage and that at the late design stage. Although availability estimation in the early design stage is more important than the late design stage, it is difficult to estimate the availability accurately in the early design stage. The design stage was divided into three depending on the design progress. Monte Carlo simulation technique was employed for the availability estimation. The results of the availability estimation showed that there was 0.434% difference between the early and late design stages. This meant that the availability in the early design stage was underestimated due to limited information. A sensitivity analysis was performed to investigate critical factors affecting the results. The investigated factors were failure rate, repair time, redundant equipment, and modified preventive maintenance schedule. The most critical factor was redundant equipment. It increased 0.486% availability.

Effective design collaboration during the early design stages in architecture is a condition for effective overall design and construction. This can be facilitated, supported and promoted through visual technological means. Collaborative design protocols implemented within the engineering fields (electrical and mechanical) can be adapted to the built environment design with emerging augmented reality technologies being the enablers for that shift. Architectural design requires a strongly visual approach to communication and means through which multiple disciples can collaborate effectively. Therefore, the aim of the research is to investigate principles and protocols to assist early design stage collaborative design among multidisciplinary teams, through visual and tactile user interfaces. This paper explores the current paradigm in AEC (architecture, engineering and construction), design collaboration and multidisciplinary group dynamics during the early design and conceptual stages in architecture. Afterwards, a design protocol is suggested, literature review regarding collaborative design and design protocols is summarized and further research is proposed on optimization and standardization of early design collaborative design in AEC.

Life Cycle Assessment (LCA) is used and accepted as a method to assess environmental impacts and resource use of buildings. In practice, LCA is typically used in stages where the design of the building is already finalized. However, LCA-calculations from early design stages can be used actively in design and optimization of the building. One of the obstacles to early stage LCA is that extensive data input on precise material types and amounts is needed, which is limited in early design stages. The simplifications needed for a designer in an early design LCA is addressed in a research project, where an extensive library of predefined building components and installations were developed and integrated into the existing Danish LCAbyg tool. The library assists the user in establishing a full building inventory by simple inputs of geometry of the building and a selection from the library of building element layers. However, the simplified approach to LCA of a building at early design stages inevitably affects results compared with results of a calculation made at later design stages where more, specific data is available. This paper presents an evaluation of building cases, modelled with the same background database and life cycle stages, using the simplified early design LCA approach and a detailed LCA approach. The evaluation includes testing of how well the predefined components in the early design approach fit with the case buildings and comparisons of the total material input and precision of the final LCA results.

Supplier involvement in automotive component design: are there really large US Japan differences?

Buildings account for nearly 40% of the global process and industrial greenhouse gas emissions, and within the building sector. And within the building sector, nearly 72% of the CO2 emissions are due operational carbon demand in the building sector. To meet the goals set out in the Paris Accord, we need to halve carbon emissions by the end of this decade: an aggressive and ambitious timeline. It has become an immediate and urgent need to design and construct buildings that are minimally intrusive in their impact on the environment and operate efficiently. Improvement of the building's energy performance must begin at the early design stage as the potential for sustainable intervention in the project's early phases is higher. The opportunity to improve building performance as the design progresses, is constantly reducing, while the cost of optimization is constantly increasing. One of the major design variables impacting the performance at the early phase is the building morphology/form and its associated variables. Sensible optioneering of the building form and its variables at the early stages of design - especially the conceptual design stage - can help improve the performance of the design early on in the design process at minimum cost. However, for effective early-phase design optimisation, there is a need to develop tools/methods that allow instantaneous evaluation of a large design sample space without much “lag” between the design and performance evaluation workflows. Also, in regular early phase sustainable design workflows, multiple stakeholders - like the designer, the performance analyst, etc. - are involved, which leads to a cognitive divide in the design process. For instance, while the architect designs the initial conceptual massing with a functional design intuition, the performance analysts suggest design interventions with a performance oriented design intuition. This cognitive divide leads to a dipartite design cognition in early phase design. The underlying premise of this research is to support the notion of blurring the dichotomy between conflicting design intuitions through exploring Human + Artificial Intelligence (AI) synergies and their underlying foundational technological requirements with respect to early phase design, to enable intuitive high performance design scenarios with a centralized design cognition where there are no other stakeholders other than the designer and the design environment. With the paradigm shift of other industries towards Machine Learning (ML) and AI, recent research advances in ML and the increasing availability of Big-data in the AEC industry have bolstered multivariate problems like building performance evaluation to a great extent. Design optioneering with instantaneous performance feedback through a ML method called Surrogate Modelling is an upcoming and promising methodology, which delivers feedback based on knowledge through available data, rather than simulation. Also, paradigms in Concurrent Human Machine Interaction (HMI) have explored avenues of augmenting the architectural design process through data-driven approaches. The type of data (input-output pairs) forming the basis for these paradigms depend on the target problem at hand. For early phase performance optimization - especially energy use optimization - Building Energy Model (BEM) forms the primary data. The US Department of Energy defines BEM as a versatile, multipurpose tool that is used in new building and retrofit design, code compliance, green certification, qualification for tax credits and utility incentives, and even real-time building control. BEM is also used in large-scale analyses to develop building energy-efficiency codes and inform policy decisions. A comprehensive BEM dataset that is large and accessible can support the growing interest in ML and HMI research in the high performance building design field. However, common techniques to acquire large BEM datasets like manual 3D energy modeling and simulation, 3D scanning, etc. are very tedious and time consuming. Also, existing datasets are usually incomplete, inconsistent and very difficult to access, owing to data privacy and open-access issues. And lastly, these datasets are fixed, static and cant be easily reproduced for different use case scenarios, nor scalable to adapt to needs. This research aims to tackle this problem by introducing a novel framework to generate custom problem-specific synthetic BEM dataset that generates a user defined amount of context specific 3D early phase building geometries and their associated BEM models, that is suitable for ML and HMI research. Synthetic datasets generated with this framework offer flexibility and customization in the generation process, making these datasets modular, reproducible and scalable. The framework uses the concepts of Generative Design, Geometry Manipulation, Simulation and Computational Automation to build the dataset. The dataset is qualified through the concepts of Preservationism and Sustainability, which are discussed in further sections of this article. The research identifies the importance and multifacetedness of the impact of building geometry on its performance, and demonstrates the application of generated synthetic BEM datasets by developing an enactive, conversational design environment that allows the designer to make real-time sustainable design decisions based on instantaneous machine feedback, for an intuitive, centralized design intuition.

Whole-life carbon emissions (WLCE) studies are critical in assessing the environmental impact of buildings and promoting sustainable design practices. However, existing methods for estimating WLCE are time-consuming and data-intensive, limiting their usefulness in the early building design stages. In response to this, this research introduces a novel approach by harnessing various machine learning algorithms to predict WLCE and WLCE intensity (normalised by floor area) for buildings. To evaluate the suitability of machine learning algorithms, we conducted an experiment involving ten algorithms to build the prediction models. These models were trained using data from 150 typical residential properties in Cornwall, UK, along with 28 features obtained from a comprehensive survey, including floor area, heating type, and occupant characteristics. The ten algorithms include Multiple Linear Regression, and non-linear algorithms such as Decision Tree, Random Forest. Performance evaluation metrics, such as coefficient of determination (R2), mean absolute error (MAE), means squared error (MSE), root-mean-square error (RMSE), and elapsed time, were employed. Our research contributes to the field by showcasing the effectiveness of machine learning models in predicting building WLCE. We reveal that all the tested machine learning algorithms have the capability to predict WLCE and WLCE intensity, non-linear models outperform linear ones, and the Random Forest (RF) model demonstrates superior performance in terms of accuracy, stability, and efficiency. This research encourages the integration of life cycle studies into the early design stage, even within tight building design schedules, offering practical guidance to architects and designers. Furthermore, these results also benefit a wide range of stakeholders, not only the architects but also the engineers, policymakers, and life cycle assessment (LCA) researchers, contributing to the advancement of data-driven sustainability approaches within the building sector.

Design for energy efficiency in early stages: A top-down method for new product development

Some important parameters of the machine tool layout have to be selected in the early stages of design when a shortage of information makes application of the finite element method impractical. As a computer-aided tool in these stages, the form-shaping function (FSF)-based approach can be used. It allows evaluation of the kinematic and geometric errors and compliance issue static deformations, which contribute significantly to the total machining error as applied to precision machine tools. The FSF-based methodology gives important information allowing layouts’ comparison and structure optimization under uncertainty in requirements and limitations relating to the designed machine under strong time constraints. Six different layouts of a turning center are investigated to compare the effect of geometrical errors and compliance of machine structure on part accuracy in the range of less than five microns. Motion errors of machine tool components were measured by laser equipment during machine tool assembly and inspection. Compliance-induced errors are included in the evaluation to take into consideration cutting forces. Obtained data were used as the input for simulation of machining a sample workpiece—a sphere whose size, position, and form errors are used as criteria for layout comparison. Since the FSF-based results correlate well with the experimental data, the developed approach can be considered as an effective tool for application in the early stages of machine design.

A hybrid metamodel-based method for quick energy prediction in the early design stage

Constructability is a concept with relative and not absolute value to increase optimization capacity of resources such as workforce, time, cost, quality, and working environment conditions. Given the growing complexity of projects and the increased number of failed and abandoned projects, the necessity to implement constructability in projects has become more tangible. Although the effects of lack of quantitative definition of constructability role in the traditional construction approaches are evident and have led to lack of coordination in performance of construction projects, so far no comprehensive quantitative approach has been considered to analyze the obstacles to constructability implementation. This study aims to identify and categorize the obstacles to facilitating the presence of contractors in the early stages of planning and design to implement constructability. In this study a comprehensive list of obstacles to constructability implementation is developed as a questionnaire. This questionnaire was presented to the experts, active in the field of construction. Finally, its results were analyzed using exploratory factor analysis method. Totally, 63 obstacles were questioned, then they were categorized by some of the experts of this industry into five categories of macro factors, including contractual, environmental, managerial, technical, and organizational. The significance of this study is due to this fact that identifying and categorizing the key obstacles to constructability implementation provides a useful reference for managers and owners of the construction industry to identify and develop solutions to resolve them. Identifying the obstacles to the presence of contractors in the planning and design stage and having a quantitative view toward this issue affects project implementability. In this regard, one can present more effective solutions to facilitate the presence of contractors in the early stages of design and also improves the effectiveness of constructability.

Introduction (problem statement and relevance). The need to increase the operating speeds and input torque of automobile transmissions because of their vibration is becoming a more and more urgent problem.The purpose of the study was to substantiate the applicability of harmonic analysis, which makes it possible to determine the actual values of vibration values during a steady oscillatory process, for the initial assessment of the gearbox vibration activity.Methodology and research methods. The calculation of the gearbox vibration activity was carried out by modal and harmonic analyses, implemented by the finite element method, and followed by the experimental study of gearbox vibration on a test bench.Scientific novelty and results. The presented experimental and calculated results of the research show that the difference between the experimental and calculated values is no more than 4.2%. This proves the possibility of applying modal and harmonic analysis to predict the gearbox vibration state at an early stage of product design at the stand.Practical significance. The given calculation algorithm makes it possible to predict, with a sufficient degree of accuracy, the vibration of the gearbox before the manufacture of a prototype in experimental production.

A performance-based generative design framework based on a design grammar for high-rise office towers during early design stage

Multivariate regression as an energy assessment tool in early building design

Life cycle assessment (LCA) is generally considered as a suitable methodology for the evaluation of environmental impacts of processes. However, it requires large amount and often inaccessible process data at early design stages. The present study provides an approach to streamline LCA for a broad set of biomass process chains. The proposed method breaks away from conventional LCA work in that the purpose is to support decision at early stages assuming minimal use of data available and points to most dominant LCA impacts, providing useful feedback to process design. The prediction mechanism employs decision trees, which form “if-then rules” using a set of critical parameters of the process chain with respect to various environmental impacts. The models classify products into three classes, namely having low, medium, and high environmental impact. Data for model development were obtained from early design stages and include descriptors of the molecular structure of the product and process chain-related variables corresponding to chemistry, complexity, and generic process conditions. Twenty-three LCA metrics were selected as target attributes, according to the ReCiPe and the cumulative energy demand (CED) methods. A broad set of process chains is derived from the work of Karka et al. (Int J Life Cycle Assess 22(9):1418–1440, 2017). Results demonstrate that the average classification error for the decision trees ranges between 13.4 and 43.8% for the various LCA metrics and multifunctionality approaches. Allocation approaches present a better classification performance (up to 25% error) compared with the substitution approach for LCA metrics, such as climate change, CED, and human health. For the majority of models, low- and high-output classes are characterized by better predictive performance compared with the medium class. The interpretability of selected decision trees is analyzed in terms of pruning levels and “irrational” branches. The results of the application of the decision tress for recently published case studies show for instance that 8 out of 13 cases were correctly classified for CED. The proposed approach provides a first generation of models in the form of computationally inexpensive and easily interpretable decision trees that can be used as pre-screening tools for the environmental assessment of bio-based production ahead of detailed design and conventional LCA approaches. The transparent structure of the decision trees facilitates the identification of critical decision variables providing insights for improvement in terms of process parameters, biomass feedstock, or even targeted product.

Visualized strategy for predicting buildings energy consumption during early design stage using parametric analysis