Hasil untuk "Engineering (General). Civil engineering (General)"

S. Vadyala, S. N. Betgeri, J. Matthews et al.

The recent development of machine learning (ML) and Deep Learning (DL) increases the opportunities in all the sectors. ML is a significant tool that can be applied across many disciplines, but its direct application to civil engineering problems can be challenging. ML for civil engineering applications that are simulated in the lab often fail in real-world tests. This is usually attributed to a data mismatch between the data used to train and test the ML model and the data it encounters in the real world, a phenomenon known as data shift. However, a physics-based ML model integrates data, partial differential equations (PDEs), and mathematical models to solve data shift problems. Physics-based ML models are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear equations. Physics-based ML, which takes center stage across many science disciplines, plays an important role in fluid dynamics, quantum mechanics, computational resources, and data storage. This paper reviews the history of physics-based ML and its application in civil engineering.

Pengcheng Cao, Kai Wang, Ting Zhang et al.

Accurate extrapolation of multiphase flow behaviour in offshore pipelines is hindered by limited field data, simulator bias, and strong nonlinearities. A multi-fidelity surrogate approach with stacking ensemble is proposed to address these challenges, in which a field-trained high-fidelity expert and a simulation-trained expert are adaptively fused through a k-nearest-neighbours (k-NN) competence metric and a Lipschitz-continuous convex combiner. This design ensures mean-squared-error dominance, such that the fused predictor never underperforms the better expert and variance is suppressed in transitional regimes. Data efficiency is further enhanced by a hybrid active learning strategy (ZECR Sampling) that integrates geometric coverage with uncertainty-driven refinement. When applied to a real offshore pipeline dataset containing more than 5,700 samples, the proposed method achieves an R2 of 0.740 and reduces RMSE by over 20% compared with the best baseline. These results indicate that the framework functions not only as a fast surrogate but also as a spatially aware risk controller, enabling reliable extrapolative prediction and supporting automated, real-time decision-making in multiphase flow pipeline systems.

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.

Semonti Banik, Sajal Chandra Banik, Sarker Safat Mahmud

ABSTRACT The essential factor in developing multi‐robot systems is the generation of an optimal path for task completion by multiple robots. To ensure effective path planning, this paper studies the recent publications and provides a detailed review of the path planning approaches to avoid collisions in uncertain environments. In this article, path‐planning approaches for multiple robots are categorized primarily into classical, heuristic, and artificial intelligence‐based methods. Among the heuristic approaches, bio‐inspired approaches are mostly employed to optimize the classical approaches to enhance their adaptability. The articles are analyzed based on static and dynamic scenarios, real‐time experiments, and simulations involving hybrid solutions. The increasing focus on using hybrid approaches in dynamic environments is found mostly in the papers employing heuristic and AI‐based approaches. In real‐time applications, AI‐based approaches are highly implemented in comparison to heuristic and classical approaches. Moreover, the findings from this review, highlighting the strengths and drawbacks of each algorithm, can help researchers select the appropriate approach to overcome the limitations in designing efficient multi‐robot systems.

Mengshang Liang, Changxin Xu, Mingxian Li et al.

With the deceleration of China’s economic growth, the crude economic model will progressively diminish in its competitive edge, thereby posing challenges for state-level economic and technological development zones (ETDZs) in terms of transitioning their development model and grappling with low levels of total factor productivity (TFP). This study aims to evaluate the TFP of prominent cities in China, examine the influence of the establishment of state-level ETDZs on urban TFP, and investigate the moderating effect of transportation infrastructure on this relationship. The results show that the aggregate TFP of Chinese urban areas declined from 1999 to 2020, a trend linked to structural economic adjustments and persistent underutilization of capital in several regions. The establishment of state-level ETDZs has been found to exert a notable positive influence on regional TFP. The presence of transportation infrastructure plays a moderating role in facilitating state-level ETDZs, thereby enhancing regional TFP. Among various modes of transportation, highways and railways are particularly prominent in this regard. These conclusions provide a theoretical basis and decision-making reference for further unleashing the policy potential of development zones in China.

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.

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.

N. Lagaros

In recent years, artificial neural networks (ANN) and artificial intelligence (AI), in general, have garnered significant attention with respect to their applications in several scientific fields, varying from big data management to medical diagnosis [...]

Pavel Pereslavtsev, Christian Bachmann, Joelle Elbez-Uzan et al.

There is widespread use of nuclear radiation for medical imagery and treatments. Worldwide, almost 40 million treatments are performed per year. There are also applications of radiation sources in other commercial fields, e.g., for weld inspection or steelmaking processes, in consumer products, in the food industry, and in agriculture. The large number of neutrons generated in a fusion reactor such as DEMO could potentially contribute to the production of the required radioactive isotopes. The associated commercial value of these isotopes could mitigate the capital investments and operating costs of a large fusion plant. The potential of producing various radioactive isotopes was studied from material pieces arranged inside a DEMO equatorial port plug. In this location, they are exposed to an intensive neutron spectrum suitable for a high isotope production rate. For this purpose, the full 3D geometry of one DEMO toroidal sector with an irradiation chamber in the equatorial port plug was modeled with an MCNP code to perform neutron transport simulations. Subsequent activation calculations provide detailed information on the quality and composition of the produced radioactive isotopes. The technical feasibility and the commercial potential of the production of various isotopes in the DEMO port are reported.

Xinyue Wang, Weifan Lin, Weiting Zhang et al.

In this paper, the Merkle-Transformer model is introduced as an innovative approach designed for financial data processing, which combines the data integrity verification mechanism of Merkle trees with the data processing capabilities of the Transformer model. A series of experiments on key tasks, such as financial behavior detection and stock price prediction, were conducted to validate the effectiveness of the model. The results demonstrate that the Merkle-Transformer significantly outperforms existing deep learning models (such as RoBERTa and BERT) across performance metrics, including precision, recall, accuracy, and F1 score. In particular, in the task of stock price prediction, the performance is notable, with nearly all evaluation metrics scoring above 0.9. Moreover, the performance of the model across various hardware platforms, as well as the security performance of the proposed method, were investigated. The Merkle-Transformer exhibits exceptional performance and robust data security even in resource-constrained environments across diverse hardware configurations. This research offers a new perspective, underscoring the importance of considering data security in financial data processing and confirming the superiority of integrating data verification mechanisms in deep learning models for handling financial data. The core contribution of this work is the first proposition and empirical demonstration of a financial data analysis model that fuses data integrity verification with efficient data processing, providing a novel solution for the fintech domain. It is believed that the widespread adoption and application of the Merkle-Transformer model will greatly advance innovation in the financial industry and lay a solid foundation for future research on secure financial data processing.

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.

A. Aydın, Oğuzhan Çelebi

This review presents the important applications of piezoelectric materials in civil engineering in recent years. Studies on the development of smart construction structures have been carried out by using materials such as piezoelectric materials around the world. Piezoelectric materials have attracted attention in many civil engineering applications, as a result of their capability of generating electrical power when subjected to a mechanical stress, or of generating mechanical stress when subjected to an electric field. In civil engineering applications, piezoelectric materials are used in energy harvesting not only in superstructures but also in substructures, control strategies, the creation of composite materials with cement mortar, and structural health monitoring systems. With this perspective, the civil engineering applications of the piezoelectric materials were reviewed and discussed, especially for their general properties and effectiveness. At the end, suggestions were made for future studies using piezoelectric materials.

Z. Babović, B. Bajat, Vladan Đokić et al.

This article presents a taxonomy and represents a repository of open problems in computing for numerically and logically intensive problems in a number of disciplines that have to synergize for the best performance of simulation-based feasibility studies on nature-oriented engineering in general and civil engineering in particular. Topics include but are not limited to: Nature-based construction, genomics supporting nature-based construction, earthquake engineering, and other types of geophysical disaster prevention activities, as well as the studies of processes and materials of interest for the above. In all these fields, problems are discussed that generate huge amounts of Big Data and are characterized with mathematically highly complex Iterative Algorithms. In the domain of applications, it has been stressed that problems could be made less computationally demanding if the number of computing iterations is made smaller (with the help of Artificial Intelligence or Conditional Algorithms), or if each computing iteration is made shorter in time (with the help of Data Filtration and Data Quantization). In the domain of computing, it has been stressed that computing could be made more powerful if the implementation technology is changed (Si, GaAs, etc.…), or if the computing paradigm is changed (Control Flow, Data Flow, etc.…).

W. Bogusz, T. Godlewski

The European standards, developed extensively over last 30 years, are driven by the need for continuous evolution and their Authors’ pursuit of better EU-wide quality in civil engineering – combining safety, economy, and sustainable development. The adoption of theory of reliability as the basis for design has played a major role in shaping current geotechnical practice. However, it requires from practitioners a greater understanding of underlying uncertainties. Furthermore, a number of alternative approaches, not generally used in structural design, are also allowed, as some situations in geotechnical engineering require an individual approach. Moreover, the current trends in geoengineering increase the importance of risk assessment and management. The paper presents general philosophy guiding the geotechnical design and pointing to some of the ideas introduced by Eurocode 7 and its requirements, in relation to preexisting practice of geotechnical design in civil engineering.

S. S. Sharifi, S. Fritsche, Christoph Holzinger et al.

Using the CMT (Cold Metal Transfer, F. Fronius, Upper Austria) welding process, wire arc additive manufacturing (WAAM) enables companies to fabricate steel components in a resource-saving manner (additive vs. subtractive) by properly reinforcing existing steel components. Two fundamental questions are discussed in the current work. The first focus is on the general geometric possibilities offered by this process. The influence of various parameters, such as wire feed speed, travel speed, and torch inclination on the seam shape and build-up rate are presented. The microstructure of the manufactured components is evaluated through metallography and hardness testing. Based on the first results, print strategies are developed for different requirements. Moreover, suitable process parameter sets are recommended in terms of energy input per unit length, weld integrity and hardness distribution. The second focus is on testing and determining joint properties by analyzing the microhardness of the welded structures. The chosen parameter sets will be investigated, and steel quality equivalents according to ÖNORM EN ISO 18265 will be defined.

Vagelis Plevris, N. Lagaros, A. Zeytinci

Blockchain is a technology that allows the recording of information in a way that it is difficult or practically impossible to alter, hack, or cheat. It is a new, promising technology, considered by many as a general-purpose technology (GPT). GPTs are technologies that have the potential to affect an entire economy, impacting economic growth and transforming both everyday life and the ways in which we conduct business. We present a bibliometric analysis of the relevant literature, followed by a discussion about monetary mediums and the evolution of bitcoin, as the first digital medium managing to solve the “double-spending” problem and the first successful implementation of blockchain technology. The computational operations involved in blockchain are presented, together with the cryptographic technologies associated with it, its unique characteristics, and the advantages it offers as a technology. A comprehensive literature review is provided, of the current state of the art in blockchain in the fields of civil engineering, architecture and the construction industry. Six important application areas are identified, and the relevant literature is investigated. Namely, building information modelling and computer aided design, contract management and smart contracts, construction project management, smart buildings and smart cities, construction supply chain management, and real estate. Finally, we discuss the future applications, the challenges and the opportunities that blockchain technology brings to these fields.