Architecture Scenarios Research Articles

Application of natural fibre pultruded profiles in diverse lightweight structures and architectural scenarios

AbstractReevaluating the materials that shape our built environment holds significant importance for sustainable construction. This research introduces newly developed natural fibre pultruded profiles, composed of flax fibres and bio-resin, customised for specific properties and targeted applications. Engineered to withstand both bending and compression loads, these profiles have been subjected to rigorous mechanical testing to demonstrate their compression and flexural strength, as well as flexibility. The emphasis lies on the bottom-up design approach, guiding the creation of applications suitable for this innovative material in various lightweight structures. The paper presents a series of case studies showcasing the use of biocomposite profiles in diverse design and structural contexts. The initial focus was on active-bending structures, highlighting the material’s flexibility, showcased at a ten-metre span structure, the first large-scale demonstrator. However, given the material’s versatile properties, it is suitable for a wide range of other applications. Key case studies discussed include reciprocal, tensegrity and deployable structures, as well as modular planar or space frame systems. These profiles offer a sustainable and versatile alternative to traditional materials and composites, providing innovative and eco-friendly construction solutions while contributing to industry sustainability goals.

Co-design in somewhere: the interpretations of interior design students on future architectural scenarios in metaverse

ABSTRACT With the development of technology, many sectors are being designed and produced from physical to digital form. Physical environments, in accordance with Maslow's hierarchy of needs, have started to be produced in digital environments and the metaverse is examined within the study. The aim of the study is to provide insights into the future conjuncture of the metaverse. A comprehensive case analysis was undertaken by focusing on analysing the design process and products of interior design students from Generation Z. According to the findings, the students designed metaverse environments as an individual and co-design activity. Also, the metaverse environment should not be perceived as completely isolated from physical environments in the real world, yet the priority given to human needs shows the difference in the metaverse environment compared to Maslow’s theory. These findings demonstrate the significance of incorporating individual and co-design decisions into the design of the metaverse environment by effectively addressing the human needs of its users.

DeepCraft: imitation learning method in a cointelligent design to production process to deliver architectural scenarios

The recent advancements in digital technologies and artificial intelligence in the architecture, engineering, construction, and operation sector (AECO) have induced high demands on the digital skills of human experts, builders, and workers. At the same time, to satisfy the standards of the production-efficient AECO sector by reducing costs, energy, health risk, material resources, and labor demand through efficient production and construction methods such as design for manufacture and assembly (DfMA), it is necessary to resolve efficiency-related problems in mutual human‒machine collaborations. In this article, a method utilizing artificial intelligence (AI), namely, generative adversarial imitation learning (GAIL), is presented then evaluated in two independent experiments related to the processes of DfMA as an efficient human‒machine collaboration. These experiments include a) training the digital twin of a robot to execute a robotic toolpath according to human gestures and b) the generation of a spatial configuration driven by a human's design intent provided in a demonstration. The framework encompasses human intelligence and creativity, which the AI agent in the learning process observes, understands, learns, and imitates. For both experimental cases, the human demonstration, the agent's training, the toolpath execution, and the assembly configuration process are conducted digitally. Following the scenario generated by an AI agent in a digital space, physical assembly is undertaken by human builders as the next step. The implemented workflow successfully delivers the learned toolpath and scalable spatial assemblies, articulating human intelligence, intuition, and creativity in the cocreative design.

An Event-Driven Link-Level Simulator for the Validation of AFDX and Ethernet Avionics Networks

Aircraft are composed of many electronic systems: sensors, displays, navigation equipment, and communication elements. These elements require a reliable interconnection, which is a major challenge for communication networks since high reliability and predictability requirements must be verified for safe operation. In addition, their verification via hardware deployments is limited because these are costly and it is difficult to try different architectures and configurations, thus delaying design and development in this area. Therefore, verification at early stages in the design process is of great importance and must be supported with simulation. In this context, this work presents an event-driven link-level framework and simulator for the validation of avionics networks. The tool presented supports communication protocols commonly used in avionics, such as Avionics Full-Duplex Switched Ethernet (AFDX), as well as Ethernet, which is used with static routing. Also, the simulator provides accurate results by employing realistic models for various devices. The proposed platform was evaluated in the Clean Sky’s Disruptive Cockpit for Large Passenger Aircraft architecture scenario, showing the capabilities of the simulator. Verification speed is a key factor in its application, so the computational cost was analyzed, proving that the execution time is linearly dependent on the number of messages sent and that the increase in the number of nodes has few quadratic components.

An event-driven link-level simulator for validation of AFDX and Ethernet avionics networks

Aircraft are composed of many electronic systems: sensors, displays, navigation equipment and communication elements. These elements require a reliable interconnection, which is a major challenge for communication networks as high reliability and predictability requirements must be verified for safe operation. In addition, their verification via hardware deployments is limited because these are costly and make difficult to try different architectures and configurations, thus delaying the design and development in this area. Therefore, verification at early stages of the design process is of great importance that has to be supported via simulation. In this context, the present work presents an event-driven link level framework and simulator for the validation of avionics networks. The presented tool supports communication protocols such as Avionics Full-Duplex Switched Ethernet (AFDX), which is a common protocol in avionics, as well as Ethernet, which is used with static routing in such scenarios. The simulator also uses realistic element models to provide accurate results. The proposed platform is evaluated in Clean Sky’s Disruptive Cockpit for Large Passenger Aircraft architecture scenario. The speed of the verification is a key factor, so the computational cost is analyzed, proving that the execution time is linearly dependent on the number of messages sent.

Research on MEC computing offload strategy for joint optimization of delay and energy consumption.

The decision-making process for computational offloading is a critical aspect of mobile edge computing, and various offloading decision strategies are strongly linked to the calculated latency and energy consumption of the mobile edge computing system. This paper proposes an offloading scheme based on an enhanced sine-cosine optimization algorithm (SCAGA) designed for the "edge-end" architecture scenario within edge computing. The research presented in this paper covers the following aspects: (1) Establishment of computational resource allocation models and computational cost models for edge computing scenarios; (2) Introduction of an enhanced sine and cosine optimization algorithm built upon the principles of Levy flight strategy sine and cosine optimization algorithms, incorporating concepts from roulette wheel selection and gene mutation commonly found in genetic algorithms; (3) Execution of simulation experiments to evaluate the SCAGA-based offloading scheme, demonstrating its ability to effectively reduce system latency and optimize offloading utility. Comparative experiments also highlight improvements in system latency, mobile user energy consumption, and offloading utility when compared to alternative offloading schemes.

Application of Graphic Statics and Strut-and-Tie Models Optimization Algorithm in Innovative Timber Structure Design

Timber has long been extensively employed within the construction industry as a famous, environmentally friendly, and low-carbon material. Considering that construction constitutes one of the most significant contributors to carbon emissions throughout the entire life-cycle of a building, there is an urgent desire to incorporate timber into this domain. Nevertheless, the use of timber faces inherent challenges stemming from its anisotropic nature, a result of the natural growth of timber fibers, which makes it challenging for it to function as a primary load-bearing material in coping with the various complex stresses inherent in architectural applications. Numerous designers have attempted to address this limitation through over-sized members and reinforcement at joints; however, none have satisfactorily resolved this issue in an economical manner. In this article, we introduce the Strut-and-Tie models (STM) from Graphic Statics (GS) and a topological optimization algorithm. This algorithm has the capability to generate a ‘load-minimizing path’ STM based on external load support conditions and the maximum structural path span. Regardless of the complexity of the initial external loads, each load transfer path in the optimized STM bears loads in only one direction, representing an optimal solution with minimal internal loads that align seamlessly with the characteristics of timber. Consequently, we endeavor to adopt this optimization algorithm to propose a structural design methodology, with the aspiration of designing structural systems that harness the unique attributes of timber perfectly and applying them to various architectural scenarios. Ultimately, we conclude that structural systems designed based on optimized STM are adaptable to diverse architectural contexts, and when applied to small-scale buildings, this method can save approximately 20% of material consumption compared to conventional timber frame structures, while in the case of mid-rise to high-rise buildings, it can lead to a material savings of approximately 5%.

A fuzzy preference programming and weighted influence non-linear gauge system for mission architecture assessment at NASA

The selection of a mission architecture is complex due to conflicting and intertwined mission operation criteria. In addition, the uncertainties inherent in human exploration missions make the evaluation process more challenging. This study presents a combination of improved fuzzy preference programming (FPP) and weighted influence non-linear gauge system (WINGS) to efficiently and effectively weigh conflicting and intertwined decision criteria and prioritize mission architecture scenarios at NASA. The improved FPP determines the weights of decision criteria, and the WINGS method considers criteria interdependencies and evaluates the alternative mission architecture scenarios. A numerical example demonstrates the proposed approach’s performance compared to three multi-criteria methods. A case study at the Johnson Space Center shows the applicability and efficacy of the proposed approach.

Convergence of Software-Defined Vehicular Cloud and 5G Enabling Technologies: A Survey

Vehicular cloud computing (VCC) and connected vehicles have prompted the intensive investigation of communication and computing solutions. As an important enabler, software-defined network (SDN) broadly changes the design of vehicle services, from resource allocation to ambitious autonomous cars. However, current VCC architectures face challenges that hinder the vision of providing reliable services to connected vehicles. As a result, deploying VC services using SDN network has emerged as a viable option. Therefore, software-defined VC architecture (SDVC) dynamically manages the control and resource utilization of VC by centralizing the overall knowledge. In addition, SDN stands as the representative technique of virtual resources and network function virtualization (NFV). NFV is integrated into SDVC frameworks to design extended SDVC (ESDVC) for dynamic, adaptive VC maintenance, VC network slicing management, and to meet constraint requirements such as network latency and reliable connectivity. This paper presents and discusses: (1) the architecture scenario of both SDVC and ESDVC; (2) the effective deployment methods enabling NFV and network slicing (NS) frameworks to customize VC frameworks; (3) challenges and future concepts of more VC services based on ESDVC architecture. From this survey, we believe readers would find relevant methods for realigning information dispersed across the SDVC, fifth generation (5G)-based VC, and NS domains and comprehending the relationships between these technologies while encouraging further debate on the fusion of 5G enabling technologies over SDVC to enable VC network slicing.

Structure shape measurement method based on an optical fiber shape sensor

In industry and architecture, real-time and complete measurement of structures has become a research frontier since it is directly related to the safety of life and property. Optical fibers have the potential to monitor constructions due to advantages such as small size, anti-electromagnetic interference, etc. However, existing research on optical fiber sensors pays attention to the sensing fiber itself and ignores the layout of the optical fibers. As a result, currently available fiber sensors are only suitable for monitoring one-dimensional rigid structures such as surgical robots and puncture needles. They may face challenges when measuring the freeform surfaces encountered in industrial and architectural scenarios. In this paper, a cross-orthogonal measurement method based on optical fiber shape sensors is proposed to meet the shape measurement requirements of freeform surfaces. The cross-orthogonal network allows global measurement and performs surface reconstructions more easily as compared to single-fiber arrays. It also divides the sensing fiber into several segments that are reconstructed separately (with different initial frames), avoiding the cumulative error at the end of the sensing fiber. Results of verification experiments using four-core fibers indicate that the presented method can improve measurement accuracy.

Designing Asymptotic Geodesic Hybrid Gridshells

Fabrication and assembly of freeform shells can be simplified significantly when controlling the curvature of structural elements during the design phase. This approach has produced fundamental insights to bending-active construction, using the elastic property of elements to form efficient load-bearing structures. This paper is focused on gridshells that are built from straight and flat slats. The slats are combined in two orientations, tangential and normal to the design surface, to create robust and versatile triangulated grids. For this purpose, we generate hybrid webs of asymptotic and geodesic paths on freeform surfaces. The research combines geometric computing with architectural building practice. We present a computational workflow for the design and interactive modification of hybrid asymptotic geodesic webs. At its core are discrete models that are based on concepts of differential geometry and allow to compute constrained structures within an optimization framework. The resulting webs are tested for architectural applications. We derive a strategy for the elastic erection process, in which geodesic lamellas are used as a guide and bracing of the spatial structure. Two architectural scenarios, a timber roof and a steel facade are presented. Their feasibility for construction is verified through prototypical joints and physical models. The research introduces a new class of networks and related surfaces and offers insights into the practical challenges of freeform construction from elastic slats.

Performance Analysis of Evolved RAN Architectures with Open Interfaces

The paper presents a short overview of the evolution of the Radio Access Network (RAN) towards virtualized, open and intelligent RAN architectures. Major KPIs used for performance analysis in the process of the evolution of the RAN are identified. KPIs that are considered of primary importance for ensuring high-quality multimedia communications (MMC) in a scenario of wireless Radio Access Networks based on 4G/5G Open-RAN architectures are measured and analyzed. Different split scenarios according to the O-RAN specifications are considered, as well as relevant to MMC KPIs, such as round trip time, delay jitter and packet loss. Although the variation in the results from the evaluation of these parameters in different RAN architecture scenarios is not drastic, in the context of MMC and future near to-real time services, the results show advantages of these scenarios over legacy RAN architectures. In addition, we propose a network slicing model for optimizing the network performance and enhancing the quality of MMC, by implementing optimal utilization of network resources.

On the Performance of Partial RIS Selection vs. Partial Relay Selection for Vehicular Communications

Reconfigurable intelligent surface (RIS) has been introduced as a promising emerging paradigm to design a smart radio environment for improving wireless capacity. It has the potential to reshape wave propagation via its tunable and low-cost reflecting elements. Therefore, it is regarded as a propitious solution that provides alternative paths for the propagation of high-frequency signals, which are vulnerable to blockages and suffer from penetration losses. Because of those features, RIS has been considered to be integrated into the wireless vehicular network (WVN), which is characterized by high mobility and high quality of service (QoS) requirements. Hence, similar to relaying systems, a vehicle needs to select the best available RIS that maximizes the signal-to-noise ratio (SNR) at the receiver. Inspired by that, in this paper, we introduce the partial RIS selection for enhancing capacity and reducing error rate. We derive closed-form expressions for the ergodic capacity, symbol error probability (SEP), and energy efficiency for a partial RIS selection design. Besides, we compare its performances to the partial relaying design while considering: decode-and-forward (DF) and amplify-and-forward fixed gain (AF). Moreover, we consider a wireless attack scenario, where an eavesdropper intends to overhear the transmitted signal at the receiver’s end. Then, we compare partial RIS to partial relay selection from this security perspective, where we derive closed-form expressions for the average secrecy capacity. Furthermore, we compare the partial RIS selection to a single RIS architecture scenario from the literature, where our solution outperforms the related existing work.

Application Exploration of Scenario Logistics Ecosystem Based on beyond 5G and IoT Architecture

With the continuous expansion of my country's logistics market, the business scale and management scale of logistics enterprises are increasing day by day. Smart logistics enterprises realize the “three streams in one” of the logistics industry through informatization and intelligent management, which greatly improves the service efficiency and quality of logistics enterprises. However, there are still many problems in this intelligent logistics mode. In this context, we conduct research experiments on the logistics ecosystem of IoT architecture scenarios and draw conclusions through experiments: (1) logistics development in IoT building scenarios. The environment includes natural environment, economic environment, political environment, etc. The economic environment plays an important role in driving logistics demand and promoting logistics growth. The political environment helps to improve the logistics infrastructure and ensure the healthy development of logistics. (2) The most important cost of the logistics industry is that transportation costs account for 67% of the entire logistics cost, followed by cargo storage accounting for 16% of the cost, and smart express cabinets accounting for 13% of the cost. From this, we can see the highest cost out of the logistics system under the Internet of Things is the transportation cost. As a unity is closely linked to the economy and interdependent, logistics is the driving force to promote the development of economic integration. In order to ensure the sustainable development of the economy, it is necessary to continuously improve the service level of logistics, reduce logistics costs, improve operation efficiency, strengthen the linkage development with other industries, and enhance the competitiveness of logistics. We launched an investigation against this background and researched the logistics system under the scenario logistics ecosystem of beyond 5G and IoT architecture.

Journal Unique Visitors Forecasting Based on Multivariate Attributes Using CNN

Forecasting is needed in various problems, one of which is forecasting electronic journals unique visitors. Although forecasting cannot produce very accurate predictions, using the proper method can reduce forecasting errors. In this research, forecasting is done using the Deep Learning method, which is often used to process two-dimensional data, namely convolutional neural network (CNN). One-dimensional CNN comes with 1D feature extraction suitable for forecasting 1D time-series problems. This study aims to determine the best architecture and increase the number of hidden layers and neurons on CNN forecasting results. In various architectural scenarios, CNN performance was measured using the root mean squared error (RMSE). Based on the study results, the best results were obtained with an RMSE value of 2.314 using an architecture of 2 hidden layers and 64 neurons in Model 1. Meanwhile, the significant effect of increasing the number of hidden layers on the RMSE value was only found in Model 1 using 64 or 256 neurons.

LSTM-based Multivariate Time-Series Analysis: A Case of Journal Visitors Forecasting

Forecasting is the process of predicting something in the future based on previous patterns. Forecasting will never be 100% accurate because the future has a problem of uncertainty. However, using the right method can make forecasting have a low error rate value to provide a good forecast for the future. This study aims to determine the effect of increasing the number of hidden layers and neurons on the performance of the long short-term memory (LSTM) forecasting method. LSTM performance measurement is done by root mean square error (RMSE) in various architectural scenarios. The LSTM algorithm is considered capable of handling long-term dependencies on its input and can predict data for a relatively long time. Based on research conducted from all models, the best results were obtained with an RMSE value of 0.699 obtained in model 1 with the number of hidden layers 2 and 64 neurons. Adding the number of hidden layers can significantly affect the RMSE results using neurons 16 and 32 in Model 1.

Optimization of Metro Central Air Conditioning Cold Source System Based on PCA-ANN Data Model

Due to the unique features of metro central air conditioning systems’ architectural design and application scenarios, systems demand a greater degree of energy-savings than standard buildings. The central air conditioning system is the major energy user in metro stations, with the cooling source system accounting for a substantial portion. As a consequence, enhancing the energy efficiency of the cold source system is critical for optimizing the energy efficiency of the central air conditioning system. After analyzing the potential for energy-savings, we propose an energy-saving control technique for cold source systems based on the PCA-ANN data model. Firstly, an operating condition simulation was performed using operational data and cold source system equipment specifications. The effective operating data in the operational data-base was then filtered using the simulation data. Additionally, principal component analysis was used to examine the chosen dates. Finally, the fitted and calibrated data model was utilized to optimize the functioning of the cold source system. August’s revised approach resulted in a 10.5 percent decrease in system energy consumption. In comparison to using non-optimized energy parameters, the suggested technique provides a variety of energy efficiency advantages.

Scenario-based Portfolio Management: Modelling Future Cost and Effect on Manufacturing

As a response to the increased demand for more customized products at a lower cost and with shorter lead times, many of today’s manufacturing companies have embarked on the journey of modularization in either their product portfolio, their manufacturing setup or in both. However, transitioning towards a modular setup generates uncertainties on where and what to modularize, as well as on the consequence. Some of these questions can be answered and clarified through the use of data models, making it possible to create and test different architectural scenarios and thereby make decisions based on actual data rather than best guess and tacit knowledge. In order to get access to actual ERP data and be able to test and validate the method in a real life environment, this research is made in collaboration with a large manufacturing company using the design science methodology. The purpose of this research is to present an approach for creating a data model based on standard ERP data that will allow engineers and system architects to create strategic architectural scenarios and evaluate these in terms of expected savings, impact on manufacturing and other relevant parameters.

InFeMo: Flexible Big Data Management Through a Federated Cloud System

This paper introduces and describes a novel architecture scenario based on Cloud Computing and counts on the innovative model of Federated Learning. The proposed model is named Integrated Federated Model , with the acronym InFeMo . InFeMo incorporates all the existing Cloud models with a federated learning scenario, as well as other related technologies that may have integrated use with each other, offering a novel integrated scenario. In addition to this, the proposed model is motivated to deliver a more energy efficient system architecture and environment for the users, which aims to the scope of data management. Also, by applying the InFeMo the user would have less waiting time in every procedure queue. The proposed system was built on the resources made available by Cloud Service Providers (CSPs) and by using the PaaS (Platform as a Service) model, in order to be able to handle user requests better and faster. This research tries to fill a scientific gap in the field of federated Cloud systems. Thus, taking advantage of the existing scenarios of FedAvg and CO-OP, we were keen to end up with a new federated scenario that merges these two algorithms, and aiming for a more efficient model that is able to select, depending on the occasion, if it “trains” the model locally in client or globally in server.

Realization of Dependable Digital Systems for Safety-Critical Computer Systems using FPGAs

Digital systems built based on field-programmable-gate-arrays (FPGAs) are increasingly being used in safety-critical computer systems. These systems typically require high levels of integrity, reliability, and performance to ensure the correct operation of the critical application in which the digital systems are embedded. To achieve the safety and functional objectives, many hardware architectural scenarios based on fault tolerance techniques were proposed. This research paper presents a new dependable digital system (DDS) that consists of fault tolerant blocks (FTBs), spare functional blocks (SFBs), synchronization units, and input output units. Markov-based chain have been used to model the correct operation of the proposed hardware system that is targeted to be realized on FPGA-based technology. This system was designed to be reliable against transient faults (TF), permanent faults (PF), and hardware common cause failures (CCF). The reliability analysis of the proposed system was accomplished with Markov modeling techniques which could express the regenerative behavior of the digital system. Certain states in the system represent system failure, while others represent fault-free behavior in the presence of faults. Finally, based on the MathWorks Simulink simulation results of modeling the DDS digital system, the system does meet its functionality and reliability requirements.