Hasil untuk "Architectural engineering. Structural engineering of buildings"

Patricia G. F. Matsubara, Tayana Conte

Over twenty years ago, the Software Engineering (SE) research community have been involved with Evidence-Based Software Engineering (EBSE). EBSE aims to inform industrial practice with the best evidence from rigorous research, preferably from systematic literature reviews (SLRs). Since then, SE researchers have conducted many SLRs, perfected their SLR procedures, proposed alternative ways of presenting their results (such as Evidence Briefings), and profusely discussed how to conduct research that impacts practice. Nevertheless, there is still a feeling that SLRs' results are not reaching practitioners. Something is missing. In this vision paper, we introduce Evidence to Decision (EtD) frameworks from the health sciences, which propose gathering experts in panels to assess the existing best evidence about the impact of an intervention in all relevant outcomes and make structured recommendations based on them. The insight we can leverage from EtD frameworks is not their structure per se but all the relevant criteria for making recommendations to practitioners from SLRs. Furthermore, we provide a worked example based on an SE SLR. We also discuss the challenges the SE research and practice community may face when adopting EtD frameworks, highlighting the need for more comprehensive criteria in our recommendations to industry practitioners.

Bonifasius Sumardiyanto, Cecillia Diana Lelyta Marsonia

Slums are one of the threats to Cultural Heritage Areas (KCB) which consist of traditional houses, especially those with living museum status such as KCB Kotagede in Yogyakarta. One of the main causes of slums is the inability of the owner (heir) to provide funds to preserve the building, which requires large costs. Assistance from the government or other parties, which is often incidental, is not a sustainable solution. For this reason, efforts are needed to optimize the potential of KCB so that it can generate sustainable conservation costs. One effort is to adapt traditional houses into homestays that offer cultural experiences for tourists. The study was carried out in 4 (four) residential buildings which were selected using purposive sampling. The study method begins with identifying initial plans of traditional houses and identifying plans for developing adapted designs. Next, using the Form - Function - Meaning structural approach, an analysis was carried out to what extent the architectural meaning of a traditional house was maintained in its adaptation into a homestay. This study reveals that the adaptation of a traditional house into a homestay can be done while maintaining its architectural meaning.

Philippa Boyd, Debs Harding

The uptake of generative artificial intelligence (GenAI) tools has implications for doctoral research and academic publication practices within both construction management and the wider academic context. Unless these implications are understood, GenAI tools have the potential to disrupt traditional relationships between doctoral researchers and their academic supervisors. Rather than exploring the technical competence and reach of GenAI tools, this study explores the nature of these challenges. GenAI is explored from both supervisor and doctoral perspectives for how its integration into doctoral research processes might shift relationships and affect practice. Informed by structuration theory, the research uses mixed methods to map shifts in agency and structure resulting from the adoption of GenAI tools. Findings highlight that the often-unacknowledged use of GenAI in doctoral research can confer undue agency on the technology that disrupts traditional relationships in an unacknowledged way. The rapid but often unacknowledged uptake of GenAI within doctoral research comes with a lack of consideration of the emotional support ascribed by students to the technology. It is concluded that GenAI tools should be openly incorporated into research and practice in a transparent, integrated approach. Practice relevance This research has relevance to the academic community both within the built environment disciplines and more general pedagogical implications. The identification of concerns over the reach and rapidity of GenAI adoption exposes potential changes to relationships and practices. Academics will be able to understand the shifts in relationships between stakeholders and the possible ramifications. The research exposes an unacknowledged proliferation of GenAI use in doctoral research and its underlying role in providing surrogate emotional support to doctoral students. By giving voice to stakeholders, this research exposes the lack of ethical frameworks around the use of GenAI and the need to consider its open and supported use, and its impact on developing the technical understandings and communication of doctoral researchers. The research uncovers some of the debates, concerns and possibilities that GenAI can bring to doctoral research practice, so that they can be intentionally addressed.

Émilie Savoie, J. P. Sapinski, Anne-Marie Laroche

This study investigates the preservation through adaptive reuse of derelict heritage buildings at risk of demolition in urban settings in New Brunswick, Canada. Despite the demonstrated benefits of adaptive reuse in balancing heritage preservation and contemporary urban needs, small cities face significant challenges: financial constraints, regulatory barriers and technical limitations. Using a multiple-case study approach, adaptive reuse projects in Moncton, Fredericton and Saint John are examined to identify key factors contributing to their success. Findings reveal that prioritising structural adaptability, cultural value and long-term sustainability over profit-driven redevelopment models is essential. Successful adaptive reuse projects rely on collaborative governance frameworks, phased financial strategies, early involvement of technical expertise and active community engagement. This approach is critical to overcoming challenges such as hazardous material management, regulatory barriers and funding limitations. This study demonstrates that adaptive reuse can transform neglected heritage buildings into functional spaces, contributing to urban regeneration, cultural preservation and sustainability, while offering a framework for future adaptive reuse initiatives in similar contexts. Practice relevance The findings highlight key implications for advancing adaptive reuse as a strategy for heritage preservation and sustainability. Prioritising building location, adaptability and cultural value over profit-driven approaches is essential to fostering adaptive reuse initiatives. Establishing clear governance frameworks can align public, private and community efforts, facilitating collaboration to overcome common challenges. Financial incentives, such as grants or tax relief, can address issues such as hazardous material management, while adaptive regulatory processes can streamline approvals. Addressing expertise shortages through targeted training programmes and cross-regional collaboration is particularly important for smaller regions. Additionally, integrating sustainability principles and promoting material reuse within adaptive reuse projects can enhance environmental performance and urban resilience. These measures demonstrate how adaptive reuse can revitalise neglected heritage buildings into functional, purposeful spaces that contribute to cultural continuity, community identity and sustainable urban development.

Mohammed Saleh Alshaikh

The performance of Organic Solar Cells (OSCs) is intrinsically linked to the molecular, electronic, and structural properties of donor and acceptor materials. This study employs various machine learning techniques, namely the Generalized Regression Neural Network (GRNN), Support Vector Machine (SVM), and Tree Boost, to predict key performance metrics of OSCs, including power conversion efficiency (PCE), short-circuit current density (JSC), open-circuit voltage (VOC), and fill factor (FF). The models are trained and evaluated using an experimentally reported dataset compiled by Sahu et al. Correlation analysis demonstrates that material characteristics such as polarizability, bandgap, dipole moment, and charge transfer are statistically associated with OSC performance. The predictive performance of the GRNN model is compared with that of the SVM and Tree Boost models, showing consistently lower prediction errors within the considered dataset. In addition, sensitivity analysis is performed to assess the relative importance of the predictor variables and to examine the influence of kernel functions on GRNN performance. The results indicate that machine learning models, particularly GRNN, can serve as effective data-driven tools for predicting the performance of organic solar cells and for supporting computational screening studies.

Mamiko Numata

ABSTRACT The purpose of this study is to gain knowledge about the coexistence of monument protection and energy in southern Germany's Old Town through resident intentions, laws, and permit decision criteria. First, there is a movement to allow rooftop PV on buildings in the Old Town. Next, there are two requirements for rooftop PV installation under the Old Town Protection Law: (1) building evaluation of monument protection and impact on surrounding buildings, etc. and (2) visibility from public spaces. It has become clear that the expansion of the interpretation of “public space” as a viewpoint is protecting historic buildings.

Suman Lata Yadav

The concept of life skills is related to the way of life that emphasises the mutual exchange of knowledge, attitudes, and interpersonal skills in education. Its objective is to develop diverse skills among students and prepare them to face life’s challenges with determination. The World Health Organization has defined life skills as “the positive behaviours and tendencies that enable a person to adapt in day-to-day life.” Life skills are the abilities that enable a person to adapt and exhibit positive behaviour, allowing them to deal effectively with the problems and challenges of daily life. Life is a unique gift. Therefore, by equipping life with various skills, happiness, peace, and prosperity are created. In this research, with the objectives of the study in mind, an analytical examination of life skills among secondary-level students has been conducted. This research study examines the effects of living conditions, gender, and social class on students’ life skills and presents the findings. Future researchers can build upon this, and other factors affecting the research can also be explored.

Hayato Ishida, Amal Elsokary, Maria Aslam et al.

Realisation of significant advances in capabilities of sensors, computing, timing, and communication enabled by quantum technologies is dependent on engineering highly complex systems that integrate quantum devices into existing classical infrastructure. A systems engineering approach is considered to address the growing need for quantum-secure telecommunications that overcome the threat to encryption caused by maturing quantum computation. This work explores a range of existing and future quantum communication networks, specifically quantum key distribution network proposals, to model and demonstrate the evolution of quantum key distribution network architectures. Leveraging Orthogonal Variability Modelling and Systems Modelling Language as candidate modelling languages, the study creates traceable artefacts to promote modular architectures that are reusable for future studies. We propose a variability-driven framework for managing fast-evolving network architectures with respect to increasing stakeholder expectations. The result contributes to the systematic development of viable quantum key distribution networks and supports the investigation of similar integration challenges relevant to the broader context of quantum systems engineering.

Muneera Bano, Hashini Gunatilake, Rashina Hoda

Large language models (LLMs) have rapidly gained popularity and are being embedded into professional applications due to their capabilities in generating human-like content. However, unquestioned reliance on their outputs and recommendations can be problematic as LLMs can reinforce societal biases and stereotypes. This study investigates how LLMs, specifically OpenAI's GPT-4 and Microsoft Copilot, can reinforce gender and racial stereotypes within the software engineering (SE) profession through both textual and graphical outputs. We used each LLM to generate 300 profiles, consisting of 100 gender-based and 50 gender-neutral profiles, for a recruitment scenario in SE roles. Recommendations were generated for each profile and evaluated against the job requirements for four distinct SE positions. Each LLM was asked to select the top 5 candidates and subsequently the best candidate for each role. Each LLM was also asked to generate images for the top 5 candidates, providing a dataset for analysing potential biases in both text-based selections and visual representations. Our analysis reveals that both models preferred male and Caucasian profiles, particularly for senior roles, and favoured images featuring traits such as lighter skin tones, slimmer body types, and younger appearances. These findings highlight underlying societal biases influence the outputs of LLMs, contributing to narrow, exclusionary stereotypes that can further limit diversity and perpetuate inequities in the SE field. As LLMs are increasingly adopted within SE research and professional practices, awareness of these biases is crucial to prevent the reinforcement of discriminatory norms and to ensure that AI tools are leveraged to promote an inclusive and equitable engineering culture rather than hinder it.

Tandeep Singh, Mojtaba Mahmoodian, Shasha Wang

Building information modelling (BIM) is an appreciated technology in the field of architecture and construction management. Collaboration of information in BIM has not been fully utilized in the structural engineering stream as many engineers keep on working with previous prevailing design approaches. Failure to adequately facilitate automation in design could lead to structural defects, construction rework, or even structural clashes, with major financial implications. Given the inherent complexity of large-scale construction projects, the ‘manual design and detailing’ of structure is a challenging task and prone to human errors. Against this backdrop, this study developed a 4D building information management approach to facilitate automated structural models for professionals designing all the elements required in reinforced concrete (RC) structures like slabs, beams, and columns. The main contribution of this study is to obtain structural models directly from architecture models automatically, which reduces effort and possible errors in the previous prevailing approaches. The framework enables execution of all the model design works automatically through coding. This is achieved by executing a script which is beneficial for integrated project delivery (IPD). The 3D structural model in BIM software presented in this study extracts and transfers the geometrical data and links these data in Industry Foundation Classes (IFC) files using integration facilitated by Python 3.6 and IFCopenshell. The developed automated programme framework offers a cost-effective and accurate methodology to address the limitations and inefficiencies of traditional methods of structural modelling, which had been carried out manually. The authors have developed a novel tool to extract structural models from architectural models without proprietary software, greatly benefiting BIM managers by enhancing 3D BIM models. This advancement toward Open BIM, crucial for the architecture, engineering, and construction (AEC) industry’s future, is accessible to educators and beginners and highlights BIM’s effectiveness in improving structural analysis and productivity. The core finding of this study is to generate a structural model from an architecture model by automating the script with Python integration of IFC and IFCopenshell. The merits of the developed framework are reduced clashes, more economical structural modelling, and fully automated smart work as functions of the IPD.

A. Massafra, Davide Prati, Riccardo Gulli

ABSTRACT Supported by advancements in 3D scanning and parametric modeling tools within the architecture, engineering, and construction sectors, reverse engineering processes for cultural heritage (CH) have recently gained popularity. While many studies have focused on simple 3D reconstructions to create virtual environments, a specialized subarea of this research field has targeted more specific digitization objectives, including, among many, structural analysis of building components. This emerging field has not yet been systematically developed due to the intrinsic challenges associated with CH. Within this context, this paper proposes and describes a reverse engineering-based method that utilizes terrestrial laser scanning and visual programming (VP) to analyze displacements and deformations that occurred over time in historical masonry buildings and applied it to the timber trusses, masonry facades, and columns of two selected Italian case study buildings. This method allows for comparing the surveyed condition of these components, considered the “deformed state”, with their ideal configuration, reconstructed using VP algorithms, considered the “original state”. The outcomes of this comparison facilitate the investigation of the components’ structural behavior and support joint considerations to assess the overall condition of the investigated building, providing helpful knowledge for guiding structural improvement interventions.

Yiğit Erkoç, Necdet Torunbalcı

Sergey Yu. Savin, Maria I. Stupak, Dmitry K. Mankov

The effect of service life of a reinforced concrete building frame on its robustness parameters in the case of sudden failure of the outermost column has been investigated. The reinforced concrete frame of a philharmonic hall was chosen as the study subject. In order to evaluate its robustness, a relative robustness index, which is related to the parameters of the failure load for a system with and without initial local failure, has been utilized. Quasi-static modeling using the finite element method taking into account physical and geometric nonlinearity was performed as a part of the study. The physical nonlinearity of concrete, considering long-term operation of the structure, was accounted for by modified bilinear constitutive models of the material. Such models differed for elements with different stress-strain states in long-term operation. The parameters of the constitutive models were obtained using the integral deformation modulus proposed by Bondarenko. This approach has been employed to analyze the deformations and forces in the elements of the load-bearing system in the scenario of the outermost column failure. The curves for the percentage of destroyed elements of the load-bearing structure versus the parameters of the failure load have been plotted for the models with and without initial local failure of the outermost column, as well as for short-term and long-term operation. It is shown that the values of the failure load parameter and the relative robustness index decrease when the service life of the structure is accounted for.

Marcus Kessel, Colin Atkinson

The ability of Generative AI (GAI) technology to automatically check, synthesize and modify software engineering artifacts promises to revolutionize all aspects of software engineering. Using GAI for software engineering tasks is consequently one of the most rapidly expanding fields of software engineering research, with over a hundred LLM-based code models having been published since 2021. However, the overwhelming majority of existing code models share a major weakness - they are exclusively trained on the syntactic facet of software, significantly lowering their trustworthiness in tasks dependent on software semantics. To address this problem, a new class of "Morescient" GAI is needed that is "aware" of (i.e., trained on) both the semantic and static facets of software. This, in turn, will require a new generation of software observation platforms capable of generating large quantities of execution observations in a structured and readily analyzable way. In this paper, we present a vision and roadmap for how such "Morescient" GAI models can be engineered, evolved and disseminated according to the principles of open science.

Roberto Casadei, Gianluca Aguzzi, Giorgio Audrito et al.

Today's distributed and pervasive computing addresses large-scale cyber-physical ecosystems, characterised by dense and large networks of devices capable of computation, communication and interaction with the environment and people. While most research focusses on treating these systems as "composites" (i.e., heterogeneous functional complexes), recent developments in fields such as self-organising systems and swarm robotics have opened up a complementary perspective: treating systems as "collectives" (i.e., uniform, collaborative, and self-organising groups of entities). This article explores the motivations, state of the art, and implications of this "collective computing paradigm" in software engineering, discusses its peculiar challenges, and outlines a path for future research, touching on aspects such as macroprogramming, collective intelligence, self-adaptive middleware, learning, synthesis, and experimentation of collective behaviour.

Valerio Terragni, Annie Vella, Partha Roop et al.

A paradigm shift is underway in Software Engineering, with AI systems such as LLMs playing an increasingly important role in boosting software development productivity. This trend is anticipated to persist. In the next years, we expect a growing symbiotic partnership between human software developers and AI. The Software Engineering research community cannot afford to overlook this trend; we must address the key research challenges posed by the integration of AI into the software development process. In this paper, we present our vision of the future of software development in an AI-driven world and explore the key challenges that our research community should address to realize this vision.

Arthur Pilone, Paulo Meirelles, Fabio Kon et al.

A central challenge for ensuring the success of software projects is to assure the convergence of developers' and users' views. While the availability of large amounts of user data from social media, app store reviews, and support channels bears many benefits, it still remains unclear how software development teams can effectively use this data. We present an LLM-powered approach called DeeperMatcher that helps agile teams use crowd-based requirements engineering (CrowdRE) in their issue and task management. We are currently implementing a command-line tool that enables developers to match issues with relevant user reviews. We validated our approach on an existing English dataset from a well-known open-source project. Additionally, to check how well DeeperMatcher works for other languages, we conducted a single-case mechanism experiment alongside developers of a local project that has issues and user feedback in Brazilian Portuguese. Our preliminary analysis indicates that the accuracy of our approach is highly dependent on the text embedding method used. We discuss further refinements needed for reliable crowd-based requirements engineering with multilingual support.

Dezhi Ran, Mengzhou Wu, Wei Yang et al.

By treating data and models as the source code, Foundation Models (FMs) become a new type of software. Mirroring the concept of software crisis, the increasing complexity of FMs making FM crisis a tangible concern in the coming decade, appealing for new theories and methodologies from the field of software engineering. In this paper, we outline our vision of introducing Foundation Model (FM) engineering, a strategic response to the anticipated FM crisis with principled engineering methodologies. FM engineering aims to mitigate potential issues in FM development and application through the introduction of declarative, automated, and unified programming interfaces for both data and model management, reducing the complexities involved in working with FMs by providing a more structured and intuitive process for developers. Through the establishment of FM engineering, we aim to provide a robust, automated, and extensible framework that addresses the imminent challenges, and discovering new research opportunities for the software engineering field.

S. Kitayama, O. Iuorio

Pablo N. Pizarro, L. Massone

Abstract In shear wall building design, the initial process requires the interaction between the architectural and structural engineering groups to define the adequate wall layout, usually done with a trial-and-error procedure to fulfill architectural and engineering needs, slowing down the design process. For the engineering analysis, first, the wall thickness and length are required to check the building deformation limits, base shear strength, among other parameters. For this reason, the present investigation develops a structural design platform for reinforced concrete wall buildings that uses a deep neural network to predict the wall’s thickness and length based on previous architectural and engineering projects. The study includes, in the first place, the surveying of the architectural and engineering plans for a total of 165 buildings constructed in Chile; the generated database has the geometric and topological definition of the walls and the slabs. As a second stage, a model was trained for the regression of the wall segments’ thickness and length, making use of a feature vector that models the variation between the architectural and the engineering plans for a set of conditions such as the thickness, connectivity (vertical and horizontal), area, wall density, the distance between elements, wall angles, foundation soil type, among other engineering parameters. The regression model results in terms of R2-value are 0.995 and 0.994 for the predicted wall thickness and length, respectively, proving to be a reliable method for the initial engineering wall definition.