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Advances in digital design tools enable exploration and generation of dynamic building facades. However, some processes are formally prescribed and manually driven to only visualize the design concepts. The purpose of this paper is to present a proactive framework for integrating parametric design thinking, paying particular attention to building facade patterning.

This work developed the PatternGen^© add-on in Autodesk® Revit which utilizes an analytical image data (AID) overlay approach as a data source to dynamically pattern the building facade. The add-on was used to manipulate the placement rules of curtain panels on facade surface geometry. As means of validating this research model, a real-life design project has been chosen to illustrate the practical application of this approach. Feedback and observations from a short end-user questionnaire assessed qualitatively the facade patterning and panelization approach.

The proposed merge (or overlay) of AID images can be used as a parametric thinking method rather than just theory to generate and articulate dynamic facade design. The facade panelization responds to an AID that resembles design-performance data (e.g. solar exposure, interior privacy importance and aesthetics).

This work identifies a form of parametric thinking defined as the expression of geometrical relationships and its configuration dependent on the AID pixel Red Green Blue color source values. In this type of thinking, it explores the impact of the digital process and parametric thinking utility when driven by an AID overlay. The framework highlighted the practical application of AID pixel approach within a digital process to benefit both designers and computational tools developer on emerging design innovations.

The purpose of this research is to test the effectiveness of integrating Grasshopper 3D and measuring attractiveness by a categorical based evaluation technique (M-MACBETH) for building energy simulation analysis within a virtual environment. Set of energy retrofitting solutions is evaluated against performance-based criteria (energy consumption, weight and carbon footprint), and considering the preservation of the cultural value of the building, its architectural and spatial configuration.

This research addresses the building energy performance analysis before and after the design of retrofitting solutions in extreme climate environments (2030–2100). The proposed model integrates data obtained from an advanced parametric tool (Grasshopper) and a multi-criteria decision analysis (M-MACBETH) to score different energy retrofitting solutions against energy consumption, weight, carbon footprint and impact on architectural configuration. The proposed model is tested for predicting the performance of a traditional timber-framed dwelling in a historic parish in Lisbon. The performance of distinct solutions is compared in digitally simulated climate conditions (design scenarios) considering different criteria weights.

This study shows the importance of conducting building energy simulation linking physical and digital environments and then, identifying a set of evaluation criteria in the analysed context. Architects, environmental engineers and urban planners should use computational environment in the development design phase to identify design solutions and compare their expected impact on the building configuration and performance-based behaviour.

The unavailability of local weather data (EnergyPlus Weather File (EPW) file), the high time-resource effort, and the number/type of the energy retrofit measures tested in this research limit the scope of this study. In energy simulation procedures, the baseline generally covers a period of thirty, ten or five years. In this research, due to the fact that weather data is unavailable in the format required in the simulation process (.EPW file), the input data in the baseline is the average climatic data from EnergyPlus (2022). Additionally, this workflow is time-consuming due to the low interoperability of the software. Grasshopper requires a high-skilled analyst to obtain accurate results. To calculate the values for the energy consumption, i.e. the values of energy per day of simulation, all the values given per hour are manually summed. The values of weight are obtained by calculating the amount of material required (whose dimensions are provided by Grasshopper), while the amount of carbon footprint is calculated per kg of material. Then this set of data is introduced into M-MACBETH. Another relevant limitation is related to the techniques proposed for retrofitting this case study, all based on wood-fibre boards.

The proposed method for energy simulation and climate change adaptation can be applied to other historic buildings considering different evaluation criteria and context-based priorities.

Context-based adaptation measures of the built environment are necessary for the coming years due to the projected extreme temperature changes following the 2015 Paris Agreement and the 2030 Agenda. Built environments include historical sites that represent irreplaceable cultural legacies and factors of the community's identity to be preserved over time.

This study shows the importance of conducting building energy simulation using physical and digital environments. Computational environment should be used during the development design phase by architects, engineers and urban planners to rank design solutions against a set of performance criteria and compare the expected impact on the building configuration and performance-based behaviour. This study integrates Grasshopper 3D and M-MACBETH.

Material-based computation has been recently introduced in architectural education, where parameters and rules related to materials are integrated into algorithmic thinking. The authors aim to identify affordances of material-based computation in terms of supporting the understanding of parametric design, informing the process of parametric form finding in an educational setup and augmenting student learning outcomes.

The authors propose a material-informed holistic systems design framework for parametric form finding. The authors develop a pedagogical approach that employs material-based computation focusing on the interplay between the physical and the digital in a parametrically driven façade design exercise. The approach comprises two phases: (1) enabling physical exploration with different materials to arrive at the design logic of a panel prototype and (2) deducing embedded and controlled parameters, based on the interplay of materials and deriving strategies for pattern propagation of the panel on a façade composition using variation and complexity.

The results confirmed the initial hypothesis, where the more explicit the material exploration and identification of physical rules and relations, the more nuanced the parametrically driven process, where students expressed a clear goal oriented generative logic and utilized parametric design to inform form finding as a bottom-up approach.

Most precedent approaches developed to teach parametric design concepts in architectural education have focused on universal strategies that often result in fixating students on following standard blindly followed scripts and procedures, thus defying the purpose of a bottom-up form finding framework. The approach expands the pedagogical strategies employed to address parametric design as a form finding process.

This paper aims to represent the results of a case study to establish a building information model (BIM)-enabled workflow to capture and retrieve facility information to deliver integrated handover deliverables.

The Building Handover Information Model (BHIM) framework proposed herein is contextualized given the Construction Operation Information Exchange (COBie) and the level of development schema. The process uses Autodesk Revit as the primary BIM-authoring tool and Dynamo as an add-in for extending Revit’s parametric functionality, BHIM validation, information retrieval and documentation in generating operation and maintenance (O&M) deliverables in the end-user requested format.

Given the criticality of semantics for model elements in the BHIM and for appropriate interoperability in BIM collaboration, each discipline should establish model development and exchange protocols that define the elements, geometrical and non-geometrical information requirements and acceptable software applications early in the design phase. In this case study, five information categories (location, specifications, warranty, maintenance instructions and Construction Specifications Institute MasterFormat division) were identified as critical for model elements in the BHIM for handover purposes.

Design- and construction-purposed BIM is a standard platform in collaborative architecture, engineering and construction practice, and the models are available for many recently constructed facilities. However, interoperability issues drastically restrict implementation of these models in building information handover and O&M. This study provides essential input regarding BIM exchange protocols and collaborative BIM libraries for handover purposes in collaborative BIM development.

The goal is to derive a numerical method for computing parametric reduced-order models (PROMs) from finite-element (FE) models of microwave structures that feature geometrical parameters.

First, a parameter-dependent FE mesh is constructed by a topology-preserving mesh-morphing algorithm. Then, multivariate polynomial interpolation is employed to achieve explicit geometrical parameterization of all FE matrices. Finally, a PROM based on parameter-dependent projection matrices is constructed by means of interpolation and state transformation techniques.

The resulting PROMs are of low dimension and fast to evaluate. Moreover, the method features high rates of convergence, and the number of FE solutions required for constructing the PROM is small. The accuracy of the PROM is only limited by that of the underlying FE model and can be controlled by varying the PROM dimension.

Since the method uses topology-preserving mesh-morphing algorithms to instantiate FE models at a number of interpolation points in geometrical parameter space, there are limitations to the amount of deformation that can be handled.

PROM evaluations are computationally cheap. In many cases they can be evaluated hundreds or even thousands of times per second. Therefore, PROMs are very well-suited for parametric studies or numerical optimization.

The presented methodology employs a new way of constructing parameter-dependent interpolation matrices, based on interpolation and space transformations. The proposed methodology yields better accuracy and higher rates of convergence than previous approaches.

This paper aims to introduce the take-off and landing performance analysis modules of the software library named Java toolchain of Programs for Aircraft Design (JPAD), dedicated to the aircraft preliminary design. An overview of JPAD is also presented.

The calculation of the take-off and landing distances has been implemented using a simulation-based approach. This expects to solve an appropriate set of ordinary differential equations, which describes the aircraft equations of motion during all the take-off and landing phases. Tests upon two aircraft models (ATR72 and B747-100B) have been performed to compare the obtained output with the performance data retrieved from the related flight manuals.

The tool developed has proven to be very reliable and versatile, as it performs the calculation of the required performance with almost no computational effort and with a good accuracy, providing a less than the 5 per cent difference with respect to the statistical trend and a difference from the flight manual or public brochure data around 10 per cent.

The use of a simulation-based approach to have a more accurate estimation of the ground performance with respect to classic semi-empirical equations. Although performing the simulation of the aircraft motion, the approach shown is very time-saving and can be easily implemented in an optimization cycle.

Modular construction is widely adopted and used in the construction industry to improve construction performance with respect to both efficiency and productivity. The evaluation of design options for modular construction can be iterative, and thus automation is required to develop design alternatives. This research aims to explore the potential of utilizing the generative design approach to automate modular construction for residential building structures in urban areas such as New York City.

The proposed research methodology is investigated for a systematic approach to parametrize design parameters for modular construction layout design as well as incorporate design rules/parameters into modularizing design layouts in a Building Information Modeling (BIM) environment. Based on current building codes and necessary inputs by the user, the proposed approach enables providing recommendations in a generative design method and optimizes construction processes by performing analytical calculations.

The generative design has been found to be efficient in generating layout designs for modular construction based on parametric design. The integration of BIM and generative design can allow industry practitioners to fast generate design layout with evaluations from constructability perspectives.

This paper has proposed a new approach of incorporating generative design with BIM technologies to solve module layout generations by considering design and constructability constraints. The method can be further extended for evaluating modular construction design from manufacturability and assembly perspectives.

The purpose of this paper is to develop an artificial neural network (ANN) based prediction model via integration with data envelopment analysis (DEA) to provide the means of predicting incremental performance goals. The findings confirm the usefulness of the herein developed prediction approach, based on the results of analyses of time series data from the smartphone industry.

A two-stage hybrid model was developed, incorporating sequential measurement and prediction capability. In the first stage, a Chames, Cooper, and Rhodes DEA model is the preprocessor, generating efficiency scores (ES) of decision-making units (DMUs). In the second or follow-on stage, the ANN prediction module utilizes knowledge variables and ES to predict the change in performance needed for a desired level of improvement.

This combined approach effectively captured the information contained in the industry’s turbulent characteristics, and subsequently demonstrated an adaptive prediction capability. The back propagating neural network successfully predicted the incremental performance targets of DMUs, which translated the desired improvement levels into actionable performance goals, e.g., revenue and operating income.

This paper presents an incremental prediction approach that supports better practice benchmarking. This study differentiates itself from previous research by introducing an adaptive prediction method which generates relevant quantity outputs based upon desired improvement levels. The proposed modeling approach integrates performance measurement with a prediction framework and advances benchmarking practices to enable better performance prediction.

This paper assesses the awareness and use of information and communication technologies (ICT) systems within the Turkish construction industry. The findings will assist in identifying the future directions and priorities for how to use ICT as an enabler in this country. The research has been carried out via 22 semi‐structured interviews with senior construction professionals within government and private organizations. It investigated the usage and applicability of current information systems and technologies. The interviews then explored the appropriateness of some of the newly emerging technologies to the industry in Turkey. The findings are reported under three categories of: ICT infrastructures and strategies, the use of information systems, and views on emerging technologies. The last item has been expanded and discussed in more detail, in the paper.

This study aimed to propose a performance-oriented approach of automatically generative design and optimization of hospital building layouts in consideration of public health emergency, which intended to conduct reasonable layout design of hospital building to meet different performance requirements for both high efficiency during normal periods and low risk in the pandemic.

The research design follows a sequential mixed methodology. First, key points and parameters of hospital building layout design (HBLD) are analyzed. Then, to meet the requirements of high efficiency and low risk, adjacent preference score and infection risk coefficient are constructed as constraints. On this basis, automatic generative design is conducted to generate building layout schemes. Finally, multi-objective deviation analysis is carried out to obtain the optimal scheme of hospital building layouts.

Automatic generative design of building layouts that integrates adjacent preferences and infection risks enables hospitals to achieve rapid transitions between normal (high efficiency) and pandemic (low risk) periods, which can effectively respond to public health emergencies. The proposed approach has been verified in an actual project, which can help systematically explore the solution for better decision-making.

The form of building layouts is limited to rectangles, and future work can explore conducting irregular layouts into optimization for the framework of generative design.

The contribution of this paper is the developed approach that can quickly and effectively generate more hospital layout alternatives satisfying high operational efficiency and low infection risk by formulating space design rules, which is of great significance in response to public health emergency.