Hasil untuk "Architectural engineering. Structural engineering of buildings"

Xuehan Xiong, A. Adán, B. Akinci et al.

J. Bauer, S. Hengsbach, I. Tesari et al.

Yuvaraj K, Sanam Narayana Reddy

This paper presents a substrate-dependent performance evaluation of a defected ground structure (DGS)-integrated multiband hexagonal microstrip patch antenna operating in the 22–28 GHz millimetre-wave band for 5G FR2 applications. To examine the influence of dielectric properties on electromagnetic behaviour, the same antenna geometry is implemented on three commonly used substrates—Duroid (relative permittivity εr ≈ 2.2, loss tangent tanδ ≈ 0.0009), Rogers (εr ≈ 2.94, tanδ ≈ 0.0012), and FR4 (εr ≈ 4.4, tanδ ≈ 0.02). A controlled substrate-based comparison is conducted with respect to the reflection coefficient, impedance bandwidth, gain, and radiation efficiency. The results indicate that substrate characteristics significantly affect resonance depth, impedance stability, and radiation performance at millimetre-wave frequencies. The Duroid-based configuration achieves S₁₁ below −32 dB, peak gain of 8–8.5 dBi, and high radiation efficiency due to reduced dielectric loss. The Rogers substrate exhibits stable multiband behaviour with moderate gain, whereas the FR4-based design shows reduced resonance depth and lower gain due to increased dielectric dissipation. By maintaining identical geometry across all substrates, the study isolates the direct impact of dielectric constant and loss tangent on modal excitation and efficiency degradation in the 22–28 GHz band. The presented analysis supports informed substrate selection for compact multiband mmWave antenna designs in next-generation wireless systems.

Débora Souza, Patrícia Machado

LLM-based agents are becoming central to software engineering tasks, yet evaluating them remains fragmented and largely model-centric. Existing studies overlook how architectural components, such as planners, memory, and tool routers, shape agent behavior, limiting diagnostic power. We propose a lightweight, architecture-informed approach that links agent components to their observable behaviors and to the metrics capable of evaluating them. Our method clarifies what to measure and why, and we illustrate its application through real world agents, enabling more targeted, transparent, and actionable evaluation of LLM-based agents.

H. Sinan Bank, Daniel R. Herber, Thomas H. Bradley

Engineering system design -- whether mechatronic, control, or embedded -- often proceeds in an ad hoc manner, with requirements left implicit and traceability from intent to parameters largely absent. Existing specification-driven and systematic design methods mostly target software, and AI-assisted tools tend to enter the workflow at solution generation rather than at problem framing. Human--AI collaboration in the design of physical systems remains underexplored. This paper presents Design-OS, a lightweight, specification-driven workflow for engineering system design organized in five stages: concept definition, literature survey, conceptual design, requirements definition, and design definition. Specifications serve as the shared contract between human designers and AI agents; each stage produces structured artifacts that maintain traceability and support agent-augmented execution. We position Design-OS relative to requirements-driven design, systematic design frameworks, and AI-assisted design pipelines, and demonstrate it on a control systems design case using two rotary inverted pendulum platforms -- an open-source SimpleFOC reaction wheel and a commercial Quanser Furuta pendulum -- showing how the same specification-driven workflow accommodates fundamentally different implementations. A blank template and the full design-case artifacts are shared in a public repository to support reproducibility and reuse. The workflow makes the design process visible and auditable, and extends specification-driven orchestration of AI from software to physical engineering system design.

Andrei Crișan, M. Pepe, D. Costantino et al.

Cultural heritage conservation demands interdisciplinary and complex documentation and analysis while facing increasing pressure to adopt sustainable and productive practices. This paper bridges these gaps by proposing a methodology and a set of requirements for Building Information Modeling (BIM) models aligned with European directives for sustainability and productivity in the Architecture, Engineering, and Construction (AEC) sector. Leveraging 3D scanning and intelligent models, we establish information needs specific to conservation, encompassing material properties, historical data, and decay analysis. Interoperability, compatibility with advanced analytical tools, and open-source formats are emphasized for seamless data integration and accessibility. We further introduce two use cases for BIM-enabled heritage conservation, illustrating the application of our proposed methodology in real-world scenarios. These cases exemplify how BIM models cater to the specific needs of cultural heritage sites, from their initial condition assessment to ongoing preservation efforts. Through these examples, we demonstrate the adaptability of BIM technology in capturing and managing the complex information associated with heritage conservation, including structural details, material characteristics, and historical significance. Our work highlights the potential of BIM to revolutionize heritage conservation practices, offering a digital backbone for documentation, analysis, and management that aligns with sustainability and productivity goals.

Liqing Hao, Yuexiang Li, Dongfang Zhang

Construction and demolition waste (C&D) and tyre garbage, in particular, are becoming urgent problems on a worldwide scale. One possible solution to this problem is to substitute man-made aggregates like recycled coarse aggregate (RCA) from C&D and crumb rubber (CR) from old tyres in newly-made building materials. The goal of this study is to determine whether machine learning and regression-based methods are best for predicting flexural strength (fs) in FRRAC, or fiber-reinforced rubberized recycled aggregate concrete. The Least Squares Support Vector Regression (LSSVR) was developed for this purpose. Hyperparameters are vital in this simulation, which use the LSSVR in conjunction with the Chimp optimisation algorithm (ChOA) and the Artificial rabbit optimisation algorithm (AROA) processes to identify the optimal set. Regression models were developed and tested to forecast fs's purpose using a portion of the study dataset (102 samples). A quarter (25 samples) were used for evaluation and seventy-five percent (77 samples) for instruction out of the 102 samples included in the database. The estimation process took into account several factors. Based on these metrics outcome numbers which provided in this study, the LSSVR(A) outperformed the LSSVR(C) in order to predicted predicting flexural strength (fs) in FRRAC.

Dewi Mariana, Ethan Abner Farrell Dauna, Andi

Feng Shui posits that proximity to hospitals may introduce unfavorable energy into residential and commercial environments. Nevertheless, in practice, several food and beverage (F&B) establishments located near hospitals continue to thrive, suggesting that certain spatial factors may contribute to their sustained success. This study aims to evaluate the environmental quality and spatial comfort of F&B outlets situated near three major hospitals in Tasikmalaya. Employing a qualitative case study methodology, the research integrates on-site observations with photographic documentation. Each establishment was analyzed through the lens of five geomantic principles Long, Sha, Xue, Shui, and Xiang as well as three spatial comfort dimensions: physical, visual, and haptic. The findings reveal that three long-standing establishments along Rumah Sakit Street demonstrate strong alignment with the Long, Xue, Shui, and Xiang factors, in addition to exhibiting a high degree of spatial comfort. Conversely, establishments on Otto Iskandardinata Street perform poorly across most geomantic and spatial parameters, except for haptic qualities, primarily due to suboptimal environmental configurations. On HZ Mustofa Street, two well-established outlets benefit from favorable Long, Xue, Shui elements and overall spatial comfort. These results underscore that the viability of sites near hospitals is contingent upon the nuanced interplay between Feng Shui principles and spatial comfort conditions, necessitating a context-specific approach to spatial assessment.

Gokturk Aytug Akarlar

Large language models show promise as autonomous decision-making agents, yet their deployment in high-stakes domains remains fraught with risk. Without architectural safeguards, LLM agents exhibit catastrophic brittleness: identical capabilities produce wildly different outcomes depending solely on prompt framing. We present Chimera, a neuro-symbolic-causal architecture that integrates three complementary components - an LLM strategist, a formally verified symbolic constraint engine, and a causal inference module for counterfactual reasoning. We benchmark Chimera against baseline architectures (LLM-only, LLM with symbolic constraints) across 52-week simulations in a realistic e-commerce environment featuring price elasticity, trust dynamics, and seasonal demand. Under organizational biases toward either volume or margin optimization, LLM-only agents fail catastrophically (total loss of \$99K in volume scenarios) or destroy brand trust (-48.6% in margin scenarios). Adding symbolic constraints prevents disasters but achieves only 43-87% of Chimera's profit. Chimera consistently delivers the highest returns (\$1.52M and \$1.96M respectively, some cases +\$2.2M) while improving brand trust (+1.8% and +10.8%, some cases +20.86%), demonstrating prompt-agnostic robustness. Our TLA+ formal verification proves zero constraint violations across all scenarios. These results establish that architectural design not prompt engineering determines the reliability of autonomous agents in production environments. We provide open-source implementations and interactive demonstrations for reproducibility.

Leila Ismail, Abdelmoneim Abdelmoti, Arkaprabha Basu et al.

With the increasing complexity of industrial systems, there is a pressing need for predictive maintenance to avoid costly downtime and disastrous outcomes that could be life-threatening in certain domains. With the growing popularity of the Internet of Things, Artificial Intelligence, machine learning, and real-time big data analytics, there is a unique opportunity for efficient predictive maintenance to forecast equipment failures for real-time intervention and optimize maintenance actions, as traditional reactive and preventive maintenance practices are often inadequate to meet the requirements for the industry to provide quality-of-services of operations. Central to this evolution is digital twin technology, an adaptive virtual replica that continuously monitors and integrates sensor data to simulate and improve asset performance. Despite remarkable progress in digital twin implementations, such as considering DT in predictive maintenance for industrial engineering. This paper aims to address this void. We perform a retrospective analysis of the temporal evolution of the digital twin in predictive maintenance for industrial engineering to capture the applications, middleware, and technological requirements that led to the development of the digital twin from its inception to the AI-enabled digital twin and its self-learning models. We provide a layered architecture of the digital twin technology, as well as a taxonomy of the technology-enabled industrial engineering applications systems, middleware, and the used Artificial Intelligence algorithms. We provide insights into these systems for the realization of a trustworthy and efficient smart digital-twin industrial engineering ecosystem. We discuss future research directions in digital twin for predictive maintenance in industrial engineering.

Mahdi Jaberzadeh Ansari, Ann Barcomb

Although it has been more than four decades that the first components-based software development (CBSD) studies were conducted, there is still no standard method or tool for component selection which is widely accepted by the industry. The gulf between industry and academia contributes to the lack of an accepted tool. We conducted a mixed methods survey of nearly 100 people engaged in component-based software engineering practice or research to better understand the problems facing industry, how these needs could be addressed, and current best practices employed in component selection. We also sought to identify and prioritize quality criteria for component selection from an industry perspective. In response to the call for CBSD component selection tools to incorporate recent technical advances, we also explored the perceptions of professionals about AI-driven tools, present and envisioned.

Andrei Jipa, B. Dillenburger

Concrete is the most used human-made material in the world, and it is responsible for around 8% of the total greenhouse gas emissions worldwide. Hence, efficient concrete construction methods are one of the main foci of research in architecture, civil engineering, and material science. One recent development that promises to achieve this goal is the use of digital fabrication for building components. Most investigations focus on direct extrusion 3D printing with concrete, which has already been covered in several review articles. Conversely, this article reviews a different approach, which focuses on the indirect digital fabrication of concrete through 3D printed formworks. This approach is under investigation for structural and nonstructural, as well as for on- and off-site applications, with a number of projects having already been built, but a comprehensive review of 3D printed formworks has not yet been compiled to synthesize the findings. This article provides a comprehensive map of the state-of-the-art of five different 3D printing technologies used for the fabrication of formworks so far. The aim is to serve as a fundamental reference for future research, provide a basis for consistent language in this field, and support the development of construction standards. The article further discusses the new geometric possibilities with 3D printed formworks and their potential for making concrete construction more sustainable. In addition, the opportunities and challenges of 3D printed formworks are evaluated in the context of other traditional and digital fabrication tools. A synthetic classification in five functional typologies is proposed and illustrated with 30 representative case studies. Finally, the article concludes with a brief reflection on the role of 3D printing in the broader context of formwork innovation and a possible outlook for this technology.

X. Tan, Yuanzhe Li

Amid the shift away from fossil fuels, third-generation perovskite solar cells (PSCs) have become pivotal due to their high efficiency and low production costs. This review concentrates on semi-transparent perovskite solar cells (ST-PSCs), highlighting their power conversion efficiency (PCE) and average visible transmittance (AVT). We address strategies to optimize ST-PSC performance, tackling inherent challenges, such as optical losses from reflection, parasitic absorption, and thermalization loss, which impact the operational efficiency under variable environmental conditions. ST-PSCs are distinguished by their lightweight, flexible, and translucent properties, allowing for diverse applications in urban building integration, agricultural greenhouses, and wearable technology. These cells integrate seamlessly into various settings, enhancing energy harnessing without compromising on aesthetic or structural elements. However, the scalability of ST-PSCs involves challenges related to stability and efficiency in large-scale deployments. The tropical urban landscape of Singapore provides a unique case study for ST-PSC application, blending architectural aesthetics with high solar irradiance to optimize energy efficiency. While the potential for ST-PSCs to contribute to sustainable urban development is immense, significant technological hurdles must be overcome to realize their full potential. Continued advancements in material science and engineering are essential to address these challenges, ensuring the scalability and long-term deployment of ST-PSCs in global energy solutions.

Jungang Jiang, H. Oguzlu, Feng Jiang

Abstract Lightweight yet strong cellular structural materials have long been sought for varied engineering applications due to the outstanding performance, and have been existing in numerous nature’s designs such as wood, bone, and honeycomb. Cellulose is considered an ideal building block for such structure due to its natural abundance, low density, and high mechanical properties derived from the hydrogen bonded crystalline structure. However, restructuring pristine cellulose into 3D architectures with outstanding mechanical properties has always been a challenge. Here, a lightweight (∼90 mg/cm3) and super-strong (16.6 MPa compressive Young’s modulus) honeycomb structure is constructed by 3D printing of all-cellulose ink. The 3D printed all-cellulose structure demonstrates switchable high elasticity (to withstand varied repetitive elastic deformation) at the wet state and high rigidity (to support over 15,800 of its own weight) at dry state. Such superb printing and mechanical properties can be ascribed to the controlled dissolution and regeneration of cellulose, as well as shear induced cellulose alignment during printing. In addition, the 3D printed all-cellulose honeycomb structure demonstrates good thermal insulation properties after filling with cellulose nanofibrils aerogel.

Haoxiang Zhang, Shi Chang, Arthur Leung et al.

The rise of Foundation Models (FMs) like Large Language Models (LLMs) is revolutionizing software development. Despite the impressive prototypes, transforming FMware into production-ready products demands complex engineering across various domains. A critical but overlooked aspect is performance engineering, which aims at ensuring FMware meets performance goals such as throughput and latency to avoid user dissatisfaction and financial loss. Often, performance considerations are an afterthought, leading to costly optimization efforts post-deployment. FMware's high computational resource demands highlight the need for efficient hardware use. Continuous performance engineering is essential to prevent degradation. This paper highlights the significance of Software Performance Engineering (SPE) in FMware, identifying four key challenges: cognitive architecture design (i.e., the structural design that defines how AI components interact, reason, and interface with classical software components), communication protocols, tuning and optimization, and deployment. These challenges are based on literature surveys and experiences from developing an in-house FMware system. We discuss problems, current practices, and innovative paths for the software engineering community.

Daman Arora, Atharv Sonwane, Nalin Wadhwa et al.

A common method to solve complex problems in software engineering, is to divide the problem into multiple sub-problems. Inspired by this, we propose a Modular Architecture for Software-engineering AI (MASAI) agents, where different LLM-powered sub-agents are instantiated with well-defined objectives and strategies tuned to achieve those objectives. Our modular architecture offers several advantages: (1) employing and tuning different problem-solving strategies across sub-agents, (2) enabling sub-agents to gather information from different sources scattered throughout a repository, and (3) avoiding unnecessarily long trajectories which inflate costs and add extraneous context. MASAI enabled us to achieve the highest performance (28.33% resolution rate) on the popular and highly challenging SWE-bench Lite dataset consisting of 300 GitHub issues from 11 Python repositories. We conduct a comprehensive evaluation of MASAI relative to other agentic methods and analyze the effects of our design decisions and their contribution to the success of MASAI.

Yuan Huang, Yinan Chen, Xiangping Chen et al.

The rapid development of deep learning techniques, improved computational power, and the availability of vast training data have led to significant advancements in pre-trained models and large language models (LLMs). Pre-trained models based on architectures such as BERT and Transformer, as well as LLMs like ChatGPT, have demonstrated remarkable language capabilities and found applications in Software engineering. Software engineering tasks can be divided into many categories, among which generative tasks are the most concern by researchers, where pre-trained models and LLMs possess powerful language representation and contextual awareness capabilities, enabling them to leverage diverse training data and adapt to generative tasks through fine-tuning, transfer learning, and prompt engineering. These advantages make them effective tools in generative tasks and have demonstrated excellent performance. In this paper, we present a comprehensive literature review of generative tasks in SE using pre-trained models and LLMs. We accurately categorize SE generative tasks based on software engineering methodologies and summarize the advanced pre-trained models and LLMs involved, as well as the datasets and evaluation metrics used. Additionally, we identify key strengths, weaknesses, and gaps in existing approaches, and propose potential research directions. This review aims to provide researchers and practitioners with an in-depth analysis and guidance on the application of pre-trained models and LLMs in generative tasks within SE.

Renan Lima Baima, Tiago Miguel Barao Caetano, Ana Carolina Oliveira Lima et al.

The primary objective is to emphasize the merits of active methodologies and cross-disciplinary curricula in Requirement Engineering. This direction promises a holistic and applied trajectory for Computer Engineering education, supported by the outcomes of our case study, where artifact-centric learning proved effective, with 73% of students achieving the highest grade. Self-assessments further corroborated academic excellence, emphasizing students' engagement in skill enhancement and knowledge acquisition.

R. Burdis, Xavier Barceló Gallostra, Daniel J Kelly

Scaffold‐free tissue engineering aims to recapitulate key aspects of normal developmental processes to generate biomimetic grafts. Although functional cartilaginous tissues are engineered using such approaches, considerable challenges remain. Herein, the benefits of engineering cartilage via the fusion of multiple cartilage microtissues compared to using (millions of) individual cells to generate a cartilaginous graft are demonstrated. Key advantages include the generation of a richer extracellular matrix, more hyaline‐like cartilage phenotype, and superior shape fidelity. A major drawback of aggregate engineering is that individual microtissues do not completely (re)model and remnants of their initial architectures remain throughout the macrotissue. To address this, a temporal enzymatic (chondroitinase‐ABC) treatment is implemented to accelerate structural (re)modeling and shown to support robust fusion between adjacent microtissues, enhance microtissue (re)modeling, and enable the development of a more biomimetic tissue with a zonally organized collagen network. Additionally, enzymatic treatment is shown to modulate matrix composition, tissue phenotype, and to a lesser extent, tissue mechanics. This work demonstrates that microtissue self‐organization is an effective method for engineering scaled‐up cartilage grafts with a predefined geometry and near‐native levels of matrix accumulation. Importantly, key limitations associated with using biological building blocks can be alleviated by temporal enzymatic treatment during graft development.

N. Sedira, Jorge Pinto, M. Ginja et al.

This study investigates the internal architecture of Asian hornet nests (AHNs) using advanced imaging techniques, such as CT scanning and X-ray radiography, to understand their construction and function. The primary objective and significance of this study centre on drawing inspiration from the creative way Asian hornets construct their nests, with a particular focus on the architecture, design, functionality, and building materials of these nests. The architectural principles governing the construction of these nests, such as the arrangement of hexagonal cells, pedicels for load bearing, and adhesive materials, serve as a source of inspiration for innovative and sustainable design practices. The pedicels in Asian hornet nests play a crucial role in transferring load and ensuring stability. Additionally, AHNs’ adhesion to tree branches is essential for preventing collapse, and the pedicels provide necessary structural support. The knowledge gained from studying AHNs’ internal architecture could be applied directly to the architecture and civil engineering fields to improve structure stability and durability. The microstructure analysis of the paper-like material that hornets produce to build their nests indicates a complex and heterogeneous structure, composed of various plant fragments and fibres. This unique composition creates intricate grooves and pores, which are essential for regulating temperature and humidity levels within the outer envelope of the nest. The study of Asian hornet nests’ internal structure demonstrated that nature’s engineering principles inspire the design of durable and resilient structures in the construction industry. Civil engineers can incorporate similar principles into their designs to enhance the structural integrity and performance of buildings, bridges, and other infrastructure.