Optimal Design Alternatives Research Articles

Optimization of BIPV Utilization with Parametric Approach: Case Study on Nearly Zero Carbon Building Studio Design in Medan, Indonesia

Buildings significantly consume fossil energy, leading to substantial carbon emissions contributing to global warming and rising sea levels. These environmental impacts pose serious challenges, necessitating a shift towards sustainable building designs. By integrating clean energy solutions into building architecture, we can reduce the environmental burden of emissions. This approach mitigates adverse effects on living organisms and ecosystems and promotes long-term sustainability and resilience in urban development. Building-Integrated Photovoltaic (BIPV) systems represent one such approach for substituting fossil energy. However, implementing BIPV effectively necessitates comprehensive analysis considering numerous parameters and variables. This paper introduces building design strategies employing the BIPV approach. The decision-making process in designing alternatives utilizes parametric methods, recognized for their ability to yield optimization results for various variables through the development of design algorithms. The study focuses on the design of a studio building at the University of North Sumatra. Comparative analysis of the results obtained through parametric design aims to identify the most favorable design outcomes. The research includes developing a parameterized design algorithm, workflow, and assessment results for selecting optimal design alternatives. The results indicate that the placement of PV panels on the side area (PVOS) has more potential than placement on the top area (PVOT). Despite the PVOT area being observed to be 0.57% smaller than PVOS, PVOS shows a significant increase in total Incident Radiation (IR), average IR, and total Energy Generated by 137.14%, 128.40%, and 132.97%, respectively.

Optimal Design Alternatives of Guide Signs for Expressway Segments with Consecutive Dense Exits

Optimal Design Alternatives of Guide Signs for Expressway Segments with Consecutive Dense Exits

DfMA Integrated Assessment Model for Selecting Optimal Design Alternatives in OSC Projects

To select the optimal design alternative in off-site construction (OSC) projects, the building industry has turned to design for manufacturing and assembly (DfMA). However, most DfMA developments in the OSC field until now have been on improving the production process in OSC projects and guideline strategies on how to apply them. The application of DfMA guidelines only provides background knowledge to designers on how to design. However, it cannot inspect whether the DfMA concept is fully reflected in a design draft to examine the suitability to the OSC production environment, and it cannot determine the optimal alternative from among multiple design alternatives. Thus, this study developed an integrated assessment model of OSC-DfMA consisting of the OSC-DfMA production suitability assessment model and the OSC-DfMA production efficiency assessment model to support decision-making for selecting the optimal design alternative of an OSC project. In this study, the scope of the main research was limited to precast concrete (PC)-based OSC projects. Firstly, we developed an OSC-DfMA production suitability assessment model to review whether design drafts are suitable in the OSC production environment by applying checklist and matrix techniques. Secondly, we developed an OSC-DfMA production efficiency assessment model to select an optimal alternative in terms of production efficiency among multiple design drafts. Thirdly, we conducted a case study to validate the usefulness of the OSC-DfMA assessment model developed in this study. Finally, we discuss the possibility of using AI technology to consider the facility capacity and resource constraints during the production of OSC building components. The study results are of practical value in providing the basis for expanding the applicability of DfMA by proposing a DfMA assessment model suitable for OSC contexts.

Aided design decision-making framework for engineering projects considering cost and social benefits

PurposeCurrently, engineering project design lacks a design framework that fully combines subjective experience and objective data. This study develops an aided design decision-making framework to automatically output the optimal design alternative for engineering projects in a more efficient and objective mode, which synthesizes the design experience.Design/methodology/approachA database of design components is first constructed to facilitate the retrieval of data and the design alternative screening algorithm is proposed to automatically select all feasible design alternatives. Then back propagation (BP) neural network algorithm is introduced to predict the cost of all feasible design alternatives. Based on the gray relational degree-particle swarm optimization (GRD-PSO) algorithm, the optimal design alternative can be selected considering multiple objectives.FindingsThe case study shows that the BP neural network-cost prediction algorithm can well predict the cost of design alternatives, and the framework can be widely used at the design stage of most engineering projects. Design components with low sensitivity to design objectives have been obtained, allowing for the consideration of disregarding their impacts on design objectives in such situations requiring rapid decisions. Meanwhile, design components with high sensitivity to design objective weights have also been obtained, drawing special attention to the effects of changes in the importance of design objectives on the selection of these components. Simultaneously, the framework can be flexibly adjusted to different design objectives and identify key design components, providing decision reference for designers.Originality/valueThe framework proposed in this paper contributes to the knowledge of design decision-making by emphasizing the importance of combining objective data and subjective experience, whose significance is ignored in the existing literature.

A multi-criteria decision-making approach for pressurized water reactor based on hesitant fuzzy-improved cumulative prospect theory and 2-additive fuzzy measure

As one of the world's largest energy consumers, China has paid much attention to the development of non-fossil energy sources. The nuclear power is regarded as the top priority for development due to its remarkable ecological and economic advantages. Given the large investment, long lifecycle, and rigorous quality control, the conceptual design plays a critical role in the pressurized water reactor development. Multitudinous design alternatives are presented at this stage and it is essential to develop an advanced evaluation approach. Hence, this work proposes a multi-criteria decision-making approach for pressurized water reactor based on hesitant fuzzy-improved cumulative prospect theory and 2-additive fuzzy measure. Firstly, considering the inherent uncertainty and cognitive biases of nuclear power experts, cumulative prospect values are calculated for design alternatives by adopting dual prospect reference points and two-tuple entropy measure under a hesitant fuzzy environment. Secondly, a linear programming model based on bidirectional projection measures is constructed to eliminate the discordance between the independent criteria assumption and interdependent evaluation information. This model helps to identify optimal 2-additive fuzzy measures, which serve as the basis for determining the Shapley importance and interaction indices of evaluation criteria. Then, taking Shapley interaction indices modification into account, a quadratic programming model based on the global criterion method is built. Finally, 2-additive Choquet integral-based TOPSIS method is proposed to select the optimal design alternative. A case study on the essential service water system is implemented to demonstrate the reliability and superiority of the proposed approach.

Optimization of urban water pipe network design using fast-messy genetic algorithms (fmGA)

Abstract To have an efficient water distribution network, optimal design alternatives need to be identified and analysed using combined hydraulic modelling and optimization. This paper reports on the application of the fast-messy genetic algorithm (fmGA) coupled with hydraulic modelling tool EPANET to assess and select an optimum design and operational alternative for a water distribution pipe network. The sole objective of the optimization modelling was to minimize network costs subject to hydraulic and design constraints. The fmGA was first tested using the benchmark case study of the Hanoi network and then applied to the real network of Maun, Botswana which is considered as the case study. We have compared our results of the fmGA model application with other optimization techniques applied to the Hanoi network. The findings of the test revealed that the fmGA is superior to other popular metaheuristic optimization methods in terms of processing speed with comparable accuracy and pressure constraints fulfilled for all nodes. It also provides the best solution. For the water distribution network of Maun, the best pipeline configuration and route were determined, in which Configuration B is found to be the best and least cost solution to improve the water supply situation in Maun.

Thermo-hydraulic characterization and design optimization of delta-shaped obstacles in solar water heating system using CRITIC-COPRAS approach

This study reports on the performance of delta-shaped obstacles in a solar water heating system (SWHS) by means of experimental analysis and optimization. The influence of different parameter combinations such as Reynolds number (200, 600, 1000, 1400, 1800), pitch ratio (0.5, 1, 1.5), blockage ratio (0.15, 0.20, 0.25), and angle of attack (45°) on Nusselt number, friction factor and thermo-hydraulic performance of SWHS were analyzed. For the combination of Reynolds number = 1800 and pitch ratio = 0.5, the Nusselt number remained highest for 0.25 of blockage ratio, whereas the friction factor remained lowest for a blockage ratio of 0.15. The maximum thermo-hydraulic efficiency was achieved using Reynolds number = 200, pitch ratio = 0.5, and blockage ratio = 0.20. The obtained results were intensely dependent on parameter combinations without any pronounced trend. Therefore, criteria importance through inter-criteria correlation (CRITIC) and complex proportional assessment (COPRAS) approach was implemented to find optimal design alternative. The results of the hybrid CRITIC-COPRAS approach showed that the combination of Reynolds number = 1800, pitch ratio = 0.5, blockage ratio = 0.20, and angle of attack = 45° is the best alternative for maximum thermal enhancement in SWHS. The sensitivity analysis proved the robustness of the results that the first-ranked alternative is the most dominant in all scenarios.

Automated modular housing design using a module configuration algorithm and a coupled generative adversarial network (CoGAN)

A modular housing design entails an expensive and time-consuming process including iterative modification steps for satisfying various project and modular construction requirements. In addition, the fulfillment of all functional requirements within a limited budget in the modular housing design process remains elusive. A lack of a systematic approach for module configuration is an another critical obstacle making the design procedure more arduous and complicated. To ameliorate these knowledge and practice gaps, this study provides a new coupled generative adversarial network (CoGAN)-based framework for automated modular housing design generation. Furthermore, this approach encompasses a new module configuration algorithm that structurally modularize a generated housing design layout. This framework is expected to contribute to establishing the body of knowledge in a generative design of modular housing for mass building production and help architects and relevant stakeholders facilitate their design processes by yielding feasible, constructible, and optimal modular housing design alternatives.

Digital design and stability simulation for large underground powerhouse caverns with parametric model based on BIM-based framework

Large infrastructures involving underground engineering like the underground powerhouse caverns constitute multi-medium, complicated and interaction systems, which require geotechnical data management, parametric model construction, optimal design alternatives, and real-time safety assessment. However, even though the data needed for numerical analysis is recorded in the Building Information Modeling (BIM) parametric models, the transformation of geometric characteristics between BIM and numerical models is cumbersome. Therefore, an automatic numerical simulation analysis framework was proposed, which can not only convert 3D parametric structural geometry model by indirect method, but also realize automatic pre-treatment and analysis, such as mechanical properties definition, boundary conditions assignment and mesh generation. In this study, the digital model of underground powerhouse caverns accompanying with cavern support system could be constructed automatically via secondary development on CATIA software. Reorganized borehole logs at geological profiles accompanying with topographic-geologic map are also used to define the 3D subsoil model. Python codes are programmed for pre-treatment of numerical model to enhance the calculation efficiency and alleviate the manual interventions by linking differ-format data files. Furthermore, a case study of Suki Kinari underground powerhouse caverns was exemplified the advantages of this framework in terms of modeling and pre-treatment efficiency, and error-free geometric information translation. The proposed framework provides a fundamental process for automatic design-to-analysis, hence mitigates user intervention and error-free structural calculation for design-making.

Integrating Universal Design Standards and Building Information Modeling at the Conceptual Design Stage of Buildings

A projection of the Canadian population shows that in 2024 one in five Canadians will be over 65 years old. This shift forces designers to consider the entire lifetime of occupants during the design of new buildings. Universal Design (UD), which is a design that accommodates all people to the greatest extent possible and aging in place design that is deeply rooted in the principles of UD, aim to house people irrespective of their age, ability, and chronic health conditions. Building Information Modeling (BIM) significantly helps advance the development of the Architecture, Engineering, and Construction (AEC) industry in a more collaborative and automated way. Integrating BIM and UD allows designers to incorporate UD standards easily and efficiently at the conceptual design stage of buildings by using the functionalities and capabilities of BIM tools. Therefore, this study presents the development of an automated computer model to facilitate the adoption of UD standards and processes. The novelty highlighted in this model resides in the creation of an automated method that employs a newly created plug-in and databases to assist designers to incorporate UD standards at the conceptual stage in a timely and cost-effective manner. Furthermore, the study introduces the methodology consisting of collecting, categorizing, and storing data from various universal design and accessible design guidelines in the developed databases and developing new plug-ins in BIM tool to link the developed databases in order to automate the process of retrieving necessary information and components to help designers and owners select optimal design alternatives based on their predefined criteria.

Evaluating Mine Design Alternatives for Social Risks Using Discrete Choice Analysis

As with other engineering design tasks, mine design involves setting design objectives and constraints (the feasible solution space) and finding the optimal design alternative. Mine engineers often struggle to incorporate the preferences of local community members into their evaluation of mine design alternatives because the mining literature lacks tools to quantify such risks during mine planning. This paper presents an approach to evaluate community acceptance (i.e., community preferences for the alternatives) using discrete choice models and decision-based design during mine planning. Using discrete choice models and a rigorous framework, engineers can estimate the cost of social risks as a function of the probability that individuals in the host community will prefer a particular design alternative. They can then estimate the overall utility of a particular design alternative to the project proponents. This paper illustrates the proposed approach with a strategic mine planning exercise for a gold mine. The framework can be a useful tool for designing mines for sustainability, if combined with effective community engagement and management’s commitment to creating shared value.

A surrogate-assisted optimization framework for microclimate-sensitive urban design practice

Simulations can often benefit microclimate-sensitive urban design by offering insightful abstractions of stochastic urban system behaviors, yet many of them are difficult to use and generally consume significant computational resources. We adapt an advanced surrogate-assisted evolutionary optimization algorithm instead of other empirical multi-objective evolutionary algorithms commonly used to search for optimal design alternatives to confront the challenge. Moreover, a parametric design module is hybridized with this surrogate-assisted evolutionary optimization algorithm to create a working scheme for mathematically modeling microclimate-sensitive urban design problems. This seven-step scheme is tested using a hypothetical case, a spatial planning problem in a residential block, to search for design proposals that maximize project development profits and facilitate the needed creation of a comfortable wind environment. Moreover, by utilizing three optimization solvers, we obtained a near-optimal site plan with a wind velocity ratio of 0.36, a wind velocity Gini index of 0.31, and a gross profit of 4.05 × 108 RMB. Also, the case study results show that the proposed optimization framework, which consists of a global surrogate (additive Gaussian process model) and a local surrogate (gradient boosted regression trees model), converges faster and provides better optimal solutions to a high-dimensional design problem compared to the algorithm which only uses a single surrogate. Built with a flexible structure, we believe the proposed framework can address the emerging demands for a wide range of microclimate-sensitive design tasks, especially those with costly simulations and small experimental datasets.

Optimal design alternatives of advance guide signs of closely spaced exit ramps on urban expressways

Advance guide signs for exit ramps along urban expressways are increasingly critical, enhancing safety and mobility by improving the flow of vehicles exiting urban expressways. However, research has devoted scant attention to advance guide signs for exit ramps. This study aimed to identify and propose optimal design alternatives for exit ramp advance guide signs for different types of exit spacing. This study conducted a driving simulation experiment consisting of five design alternatives of advance guide signs and two exit ramp spacing variation. Eight indicators were measured. The repeated-measure analysis of variances (ANOVA) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) were performed for the influence analysis and efficiency evaluation of different schemes. Influence analysis results showed better design alternatives in five schemes of advance guide signs, enabling drivers to more easily locate destination exits and change lanes fewer times, in addition to reducing drivers’ need to decelerate, and improving traffic flow in the key influence range of destination exit ramps. The percentage of drivers successfully locating the destination exits also increased with optimal design alternatives of advance guide signs. When the exit ramp spacing tightened, on the other hand, drivers had to make more lane changes and accelerate and decelerate more frequently in the key influence range. As a result, a lower percentage of drivers successfully located destination exits. Efficiency evaluation results were also obtained. In tight spacing, three advance guide signs are recommended to be placed at 1 km, 0.5 km and 0 km prior to the beginning of the tapered deceleration lane. If conditions are limited, at least two advance guide signs should be used. With greater spacing, four advance guide signs are recommended, located at 2 km, 1 km, 0.5 km, and 0 km prior to the beginning of the tapered deceleration lane. If road conditions are limited, three advance guide signs should be used.

Multi-objective crashworthiness optimization of lattice structure filled thin-walled tubes

Abstract Thin-walled tubes have been mostly used in passive vehicle safety systems as crash energy absorber. With the use of additive manufacturing technology, it is possible to produce novel filler materials to further enhance the crashworthiness performance of thin-walled tubes. In this study, optimal designs of novel lattice structure filled square thin-walled tubes are investigated under axial impact loading by using a compromise programming based multi-objective crashworthiness optimization procedure. Types of filler lattice structures (i.e., body-centered cubic, BCC and body-centered cubic with vertical strut, BCC-Z), diameter of lattice member, number of lattice unit cells and tube thickness are considered as design parameters, and the optimum values of these design parameters are sought for minimizing the peak crash force (PCF) and maximizing the specific energy absorption (SEA) values. The validated finite element models are utilized in order to construct the sample design space and carrying out results verification; an artificial neural network is employed for predicting values of the objective functions; the weighted superposition attraction algorithm is used to generate design alternatives and searching for their optimal combination. The compromise programming approach is used to combine multi-objectives and to produce various optimal design alternatives. The optimization results showed that the proposed approach is able to provide good solutions with high accuracy and proper selection of design parameters can effectively enhance the crashworthiness performance of the lattice structure filled thin-walled tubes. The optimum results revealed that BCC hybrid designs have generally superior crashworthiness performance compared to that of their BCC-Z counterparts for the same compromise solutions. In particular, the PCF value of the optimized BCC-Z hybrid structures is up to 44% higher than that of BCC hybrid structures while these structures have similar energy absorption performances. The compromise solutions also show that the SEA of BCC and BCC-Z hybrid structures increases respectively by 29% and 51% depending on the selected weight factors for the design objectives.

Optimization of electricity use in office buildings under occupant uncertainty

Coupling building performance simulations with mathematical optimization offers a promising solution to minimize energy use in buildings by identifying optimal design alternatives. However, uncertainties due to occupant behaviour and building operations are rarely considered in this process. To address this issue, we combined stochastic occupant behaviour modelling with building performance optimization. A single-story office building model was simulated in EnergyPlus using two occupant modelling approaches: (1) deterministic schedules as specified in the National Energy Code of Canada for Buildings, and (2) stochastic occupant behaviour and building operation models. The genetic algorithm was then applied to minimize electricity use following each approach, and later to simultaneously increase design robustness by minimizing variations in electricity use when stochastic models were used. Results highlighted the differences between optimal design alternatives for these two simulation approaches, especially when design robustness was added to the optimization objectives.

A generative design technique for exploring shape variations

Because innovative and creative design is essential to a successful product, this work brings the benefits of generative design in the conceptual phase of the product development process so that designers/engineers can effectively explore and create ingenious designs and make better design decisions. We proposed a state-of-the-art generative design technique (GDT), called Space-filling-GDT (Sf-GDT), for the creation of innovative designs. The proposed Sf-GDT has the ability to create variant optimal design alternatives for a given computer-aided design (CAD) model. An effective GDT should generate design alternatives that cover the entire design space. Toward that end, the criterion of space-filling is utilized, which uniformly distribute designs in the design space thereby giving a designer a better understanding of possible design options. To avoid creating similar designs, a weighted-grid-search approach is developed and integrated into the Sf-GDT. One of the core contributions of this work lies in the ability of Sf-GDT to explore hybrid design spaces consisting of both continuous and discrete parameters either with or without geometric constraints. A parameter-free optimization technique, called Jaya algorithm, is integrated into the Sf-GDT to generate optimal designs. Three different design parameterization and space formulation strategies; explicit, interactive, and autonomous, are proposed to set up a promising search region(s) for optimization. Two user interfaces; a web-based and a Windows-based, are also developed to utilize Sf-GDT with the existing CAD software having parametric design abilities. Based on the experiments in this study, Sf-GDT can generate creative design alternatives for a given model and outperforms existing state-of-the-art techniques.

Development of operational complexity measure for selection of optimal layout design alternative

Characterising existing approaches to operational complexity, it can be stated that for those metrics are characterful different factors used as variables such as product structure, machine composition, number of technological functions performed by machine, and others. Moreover, the complexity metrics using the information content as a basis can be divided into two groups: those which define complexity as an absolute entropy quantity, and metrics defining complexity as the relative entropy magnitude. Our view on the operational complexity is based on an assumption that process complexity value obtained as a sum of the partial complexities is not so important than a balanced operational complexity value expressing relative quantity to equilibrium levels. Accordingly, this paper introduces the novel operational complexity measure that initially identifies operational complexities of individual machines based on the number of parts, machines and operations. In the subsequent steps, these sub-measures are used to define summary complexity measure involving two balanced operational complexity characteristics. The novel measure can be effectively used to find the most suitable layout design alternative. For the purpose to prove its effectiveness, on two practical cases where tested its practicability by comparing it to the complexity indicator expressing the sum of the partial complexities.

Comparison of multi-objective evolutionary algorithms in hybrid Kansei engineering system for product form design

Understanding the affective needs of customers is crucial to the success of product design. Hybrid Kansei engineering system (HKES) is an expert system capable of generating products in accordance with the affective responses. HKES consists of two subsystems: forward Kansei engineering system (FKES) and backward Kansei engineering system (BKES). In previous studies, HKES was based primarily on single-objective optimization, such that only one optimal design was obtained in a given simulation run. The use of multi-objective evolutionary algorithm (MOEA) in HKES was only attempted using the non-dominated sorting genetic algorithm-II (NSGA-II), such that very little work has been conducted to compare different MOEAs. In this paper, we propose an approach to HKES combining the methodologies of support vector regression (SVR) and MOEAs. In BKES, we constructed predictive models using SVR. In FKES, optimal design alternatives were generated using MOEAs. Representative designs were obtained using fuzzy c-means algorithm for clustering the Pareto front into groups. To enable comparison, we employed three typical MOEAs: NSGA-II, the Pareto envelope-based selection algorithm-II (PESA-II), and the strength Pareto evolutionary algorithm-2 (SPEA2). A case study of vase form design was provided to demonstrate the proposed approach. Our results suggest that NSGA-II has good convergence performance and hybrid performance; in contrast, SPEA2 provides the strong diversity required by designers. The proposed HKES is applicable to a wide variety of product design problems, while providing creative design ideas through the exploration of numerous Pareto optimal solutions.

Algorithmic process synthesis and optimisation for multiple time periods including waste treatment: Latest developments in p-graph studio software

Food, water and domestic energy demands changes seasonally as well as the availability of renewable energy sources. One of the challenging questions of utilizing renewable resources is its synchronization with the periodically varying demands. Only those methods can estimate the economic feasibility, which can explicitly take into account the pairing of demands with potential resources in multiple time horizons as well as the potential storage options. Moreover, waste treatment cannot be neglected in any long-term technology planning procedure. The P-graph framework provides appropriate formal and algorithmic basis for process synthesis, including structural modelling and model validation (Friedler et al., 1992), generation of alternative network structures (Friedler et al., 1995), determination of the optimal and N-best design alternatives (Friedler et al., 1996). By the help of the P-graph framework and its software implementation, i.e., the P-graph Studio developed at the University of Pannonia, process-network synthesis problems can be modelled and solved in general (Bertok et al., 2013), and optimization of multiperiod operation (Heckl et al., 2014), as well as multiperiod system design (Aviso et al., 2016) and waste treatment optimization (Friedler et al., 1994) in particular. Thanks to the built-in algorithm RCABB (Bartos et al., 2015) not just the best, but the list of N-best structurally different alternative network configurations can be generated utilizing the computational power of the computers executing. In the present work, recent additions to the software is to be introduced for multiperiod process-network synthesis including the design of waste treatment system, and computer aid for answering questions about the sensitivity of a configuration to the actual economic environment are to be shown.

THE EVALUATION OF REDUNDANCY FOR ROAD TRAFFIC NETWORKS

This paper presents two redundancy indices for road traffic network junctions and also an aggregated network redundancy index. The proposed redundancy indices could be implemented to identify optimal design alternatives during the planning stage of the network junctions whereas the aggregated network redundancy index could assess the best control and management policies under disruptive events. Furthermore, effective measures of network redundancy are important to policy makers in understanding the current resilience and future planning to mitigate the impacts of greenhouse gases. The proposed junction indices cover the static aspect of redundancy, i.e. alternative paths, and the dynamic feature of redundancy reflected by the availability of spare capacity under different network loading and service level. The proposed redundancy indices are based on the entropy concept, due to its ability to measure the system configuration in addition to being able to model the inherent uncertainty in road transport network conditions. Various system parameters based on different combinations of link flow, relative link spare capacity and Relative Link Speed (RLS) were examined. However, the two redundancy indices developed from the combined RLS and relative link spare capacity showed strong correlation with junction delay and volume capacity ratio of a synthetic road transport network of Delft city. Furthermore, the developed redundancy indices responded well to demand variation under the same network conditions and supply variations. Another case study on Junction 3A in M42 motorway near Birmingham demonstrated that the developed redundancy index is able to reflect the impact of the Active Traffic Management (ATM) scheme introduced in 2006.