Hasil untuk "Structural engineering (General)"

Chunxiang Qian, Wenxiang Du, Yudong Xie et al.

With the growing demand for large-scale infrastructure development in China—such as deep-sea, deep-underground, and urban subsurface projects—combined with the widespread use of general-purpose raw materials, there is an urgent need for more precise crack control technologies in concrete. This need stems from the imperative to reduce unnecessary material consumption and environmental impact caused by excessive safety margins. To address this, a set of governing equations that account for the mutual feedback between temperature and humidity was first proposed. A non-constant form of the diffusion coefficient was introduced, alongside latent heat terms and unsteady-state heat source terms, to establish a hygrothermal coupling model. This model was further enhanced by incorporating the effects of creep relaxation, reinforcement constraint, structural restraint, and thermal conduction characteristics of formwork, thereby forming a comprehensive multi-field coupling evaluation framework that encompasses the temperature field, moisture content field, strain field, and cracking index field. Subsequently, the proposed theoretical framework was applied to representative engineering scenarios, including large-scale concrete foundation slabs, bridge bearing platforms, large-area long-span side walls and prefabricated tunnel segments. The accuracy and reliability of the model were validated through comparisons between simulation results and field-monitored data. The results demonstrate that this method effectively overcomes the technical limitations of traditional concrete crack prediction models, particularly those relying on constant parameter assumptions and decoupled field interactions. It offers a practical and robust approach for engineering applications, providing a novel perspective for precision crack control in concrete and contributing to the broader goals of sustainability and resource efficiency.

Tânia Feiri, Til Lux, Udo Wiens et al.

Annex A of EN 1992-1-1:2023—recently revised and amended in the context of the Second Generation of Eurocodes—introduces a method to adjust partial safety factors for the resistance side alongside a set of factors for different conditions and design situations, both for new and existing structures. The method proposed in Annex A is complemented by a set of stochastic models for relevant basic variables and forms a rather simple and objective format to adjust the partial safety factors from the default values offered in EN 1990:2023. Yet, over the last few years, advanced reliability-based methods aligned with modern computational tools have proved to enable rather robust and efficient structural reliability assessments. A thorough comparative analysis is imperative to understand how distinct reliability-based methods can be applied to adjust partial safety factors in the design of new structural components composed of steel-reinforced concrete. This analysis sheds light on the use of different methods to derive partial safety factors for the resolution of common engineering problems and offers inferences regarding possible implications in terms of safety and economic efficiency of design solutions.

Junwei Yu, Mufeng Yang, Yepeng Ding et al.

The proliferation of AI-powered search engines has shifted information discovery from traditional link-based retrieval to direct answer generation with selective source citation, creating new challenges for content visibility. While existing Generative Engine Optimization (GEO) approaches focus primarily on semantic content modification, the role of structural features in influencing citation behavior remains underexplored. In this paper, we propose GEO-SFE, a systematic framework for structural feature engineering in generative engine optimization. Our approach decomposes content structure into three hierarchical levels: macro-structure (document architecture), meso-structure (information chunking), and micro-structure (visual emphasis), and models their impact on citation probability across different generative engine architectures. We develop architecture-aware optimization strategies and predictive models that preserve semantic integrity while improving structural effectiveness. Experimental evaluation across six mainstream generative engines demonstrates consistent improvements in citation rate (17.3 percent) and subjective quality (18.5 percent), validating the effectiveness and generalizability of the proposed framework. This work establishes structural optimization as a foundational component of GEO, providing a data-driven methodology for enhancing content visibility in LLM-powered information ecosystems.

Jinlong Zheng, Xin-Yue Pan, Xiaomin Huang et al.

Abstract To enhance the electrochemical performance of MXene-based materials for energy storage devices, the componential modification related to the electrode capacity and the structural engineering related to the electrode stability are general strategies. Herein, a well-designed three-dimensional (3D) MXene-based aerogel (NiCo2-LDHs@MXene/rGO) on composition and structure is constructed by integrating MXene, NiCo2-LDHs, and reduced graphene oxide via a two-step method including hydrothermal and wet chemical techniques. This aerogel exhibits ultra-light nature, high theoretical capacity of LDHs, unblocked ion/electron channels of hierarchical structure, and good electrical conductivity of MXene and rGO networks, contributing to outstanding energy and power density, favorable capacity loss, and excellent stability as battery-type cathode material for supercapacitor. Most importantly, this aerogel delivers a remarkable specific capacity of 332.2 mAh g−1 at 1 A g−1 and a good durability of 87.5% after 5000 cycles at 5 A g−1 in a three-electrode system. Furthermore, a typical hybrid supercapacitor (HSC) device fabricated with NiCo2-LDHs@MXene/rGO as the cathode and MXene/rGO as the anode (NiCo2-LDHs@MXene/rGO//MXene/rGO) provides a superior energy density of 65.3 Wh kg−1 at a power density of 700 W kg−1, and maintains the capacity retention rate of 92.8% after 10,000 cycles at 5 A g−1. This work supplies a promising strategy to prepare MXene-based electrodes for assembling high-performance and low-cost energy storage devices.

Keng-Te Lin, Jihong Han, K. Li et al.

Abstract This review article aims to provide a comprehensive understanding of radiative cooling technology and their applications, especially on the integration of radiative coolers with devices. Over the past decades, radiative coolers and their applications have been intensively investigated because of their outstanding features for energy saving. The fundamental mechanism and characteristics of radiative cooling, in particular, atmospheric influences, and photothermal manipulation through structural and materials engineering, play essential roles in most of the practical applications. In general, these main factors concomitantly influence the cooling performance of a radiative cooler. However, comprehensive review investigating these main parameters simultaneously remains elusive. In this article, the fundamental features of radiative coolers are discussed, especially the influences of atmospheric conditions at different locations on the radiative coolers, and the photothermal manipulation capability and cooling performance of different types of radiative coolers. The applications, challenges faced in this field and the future trends are also discussed. This article will provide guidance towards integration of radiative coolers with functional devices for both academic researchers and engineers in the fields of energy harvesting, fluidic cooling, energy efficient clothing, and architecture.

N. Wu, Bin Bao, Quan Wang

Abstract Nowadays, with more electrical devices developed and applied in wide fields, efficient electrical energy generation is always one of the front-end and practical topics in engineering research. To disclose the possibility of obtaining high-power output from piezoelectric mechanism in the engineering structures, this study conducts a review of piezoelectric energy harvesting techniques from a structural design point of view. Piezoelectric power generation of these techniques can be significantly enhanced by increasing the operation frequency of piezoelectric materials and the strain level on them (up to Watt level). In this paper, following the general introduction of new energy solution requirements and piezoelectric mechanisms, novel ideas on piezoelectric energy harvesting from ambient vibration and natural resources are briefly reviewed. We then summarized and discussed the methodologies and mechanisms used to increase piezoelectric power generation and energy harvesting efficiency in detail. Following these methods, current studies on high-power piezoelectric harvesters are reviewed, described, and summarized to demonstrate the potential high-power generation from piezoelectricity. Taking advantage of the simple mechanism and design flexibility of the piezoelectric energy generation devices, additive manufacturing can be used to fabricate specially shaped piezoelectric meta-materials to further increase the strain and frequency applied to piezoelectric elements for obtaining higher energy harvesting efficiency in the near future.

S. Yang, Debiao Meng, Hengfei Yang et al.

In structural reliability analysis, it is a major challenge to develop a general method that can ensure high computational accuracy and low computational cost for low failure probability and high-dimensional problems. In this study, a novel enhanced simulation method named as enhanced Soft Monte Carlo Simulation coupled with Support Vector Regression (EMCS-SVR) is proposed. Firstly, a generalized Enhanced Simulation (ES) scaling formula is proposed as an improved scheme. Furthermore, the soft Monte Carlo simulation is combined with generalized ES scaling formula and support vector regression model for evaluating the failure probability. The efficiency and accuracy of the ESMCS-SVR are verified by comparing with existing popular method in four numerical examples and three engineering examples.

Liyun Zeng, Xuankai Huang

Bilingual teaching resources are insufficient in applied universities across the Southwest China. This study constructs a Structural Equation Model (SEM) based on four latent variables: student factors, teacher factors, external factors, and teaching effects. Data were collected through a questionnaire administered to 550 undergraduates majoring in specific disciplines of architecture and civil engineering at applied universities in Southwest China. Quantitative analysis yielded the following key findings: (1) external factors have a direct and positive influence on both teacher and student variables, with teaching resources exerting the strongest effect among external factors; (2) teacher factors positively affect student factors, with teachers’ attitudes playing the most critical role; and (3) both teacher and student factors significantly impact bilingual teaching effectiveness, with student quality being the most influential component among student-related variables. By integrating external, teacher, and student dimensions, the study proposes targeted strategies to improve bilingual education outcomes. The study provides new insights into the key determinants of bilingual teaching effectiveness and fills a research gap by applying SEM to quantitatively analyze bilingual education in the context of applied universities. It also offers valuable implications for educational administrators and government policymakers seeking to enhance the quality of bilingual education in Southwest China.

Zhimin Luo, Jianhua Chen, Yongjie Zhang et al.

Defects in pipes adversely affect both the jacking construction process and long-term operational safety, yet their specific impacts on mechanical properties remain unclear. This study investigates pipe jacking segments under deflection, using the Changsha Meixi Lake project as a case study. Similar model tests combined with digital image correlation were employed to examine the evolution of stress and deformation under various deflection angles and defect conditions. The reliability of the laboratory tests was verified through theoretical stress calculations under the non-deflection condition. The credibility of the laboratory test results was further enhanced by employing a numerical model and normalized parameters. Key findings reveal that stress distribution characteristics are jointly determined by the deflection mode and load. Co-directional deflection exhibits a more significant stress concentration effect; under identical load and angle conditions, it results in higher stress levels due to a superposition effect, whereas diagonal deflection shows a weakening effect. Joint deformation progresses through three distinct stages. The linear growth stage exhibits an initial linear strain–load relationship under stable deflection (load < 2 kN). The accelerated deformation stage is characterized by nonlinear strain growth with a slowing deformation rate (2–4 kN). The deformation deceleration stage finally shows a slow linear strain increment (load > 4 kN). Increasing load and deflection angle significantly amplify axial deformation, particularly revealing a “thick-in-the-middle, thin-at-the-sides” compression characteristic in the 45° vault zones. Furthermore, segment defects markedly exacerbate stress non-uniformity. Defect angles ≥ 60° substantially increase the frequency and amplitude of compressive stress in the vault, accelerate the decay of tensile stress at the bottom, and critically reduce structural stability. These new findings provide significant insights for deflection control and structural safety assessment in pipe jacking engineering. The experimental framework provides fundamental insights into construction operations in upper-soft and lower-hard strata tunneling.

GAO Xin, FENG Shijie, ZHANG Lianqing

［Objective］ By establishing a numerical seepage analysis model that aligns with real drainage systems and introducing the concept of a ′virtual permeability coefficient′ for secondary lining, the objective is to delve into the correlation between numerical methods and theoretical formulas, with expectation to leverage the efficiency and practicality of theoretical formulas in predicting external water pressure. ［Method］ Based on the principle of equivalent stable drainage volume in underwater tunnels, the concept of a ′virtual permeability coefficient′ for the secondary lining is introduced. On this basis, key factors, including the spacing of circumferential drainage blind pipes, the thickness of geotextiles, and their permeability coefficients, are selected as primary research factors. By adjusting these factors, multiple numerical seepage analysis models consistent with real drainage systems are established. ［Result & Conclusion］ The actual external water pressure acting on the secondary lining exhibits significant spatial distribution characteristics. Longitudinally, the variation in external water pressure displays periodic fluctuations corresponding to the spacing of circumferential drainage blind pipes. Circumferentially, the closer the position is to the longitudinal drainage blind pipe, the lower the external water pressure, with maximum circumferential water pressure occurring at the arch vault, followed by the inverted arch, and the smallest pressure on sidewalls. The reduction coefficients of external water pressure calculated with theoretical formulas are generally smaller than those derived from numerical methods. The stronger the drainage capacity of the design parameters, the smaller the difference between the two calculation results. The reduction coefficient consistently follows a decreasing trend from the vault to the invert to the sidewalls. When applying theoretical formulas directly in quantitative engineering design, it is necessary to introduce a comprehensive correction factor greater than 1.0 to ensure engineering safety. The value of comprehensive correction factor should be determined based on the specific structural location, with zones divided by the sidewalls. For the upper structure, a range of 1.48-1.97 is recommended, while a proper range of 1.21-1.39 for the lower structure

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.

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.

G. Markou, N. Bakas, Savvas A Chatzichristofis et al.

Data-driven models utilizing powerful artificial intelligence (AI) algorithms have been implemented over the past two decades in different fields of simulation-based engineering science. Most numerical procedures involve processing data sets developed from physical or numerical experiments to create closed-form formulae to predict the corresponding systems’ mechanical response. Efficient AI methodologies that will allow the development and use of accurate predictive models for solving computational intensive engineering problems remain an open issue. In this research work, high-performance machine learning (ML) algorithms are proposed for modeling structural mechanics-related problems, which are implemented in parallel and distributed computing environments to address extremely computationally demanding problems. Four machine learning algorithms are proposed in this work and their performance is investigated in three different structural engineering problems. According to the parametric investigation of the prediction accuracy, the extreme gradient boosting with extended hyper-parameter optimization (XGBoost-HYT-CV) was found to be more efficient regarding the generalization errors deriving a 4.54% residual error for all test cases considered. Furthermore, a comprehensive statistical analysis of the residual errors and a sensitivity analysis of the predictors concerning the target variable are reported. Overall, the proposed models were found to outperform the existing ML methods, where in one case the residual error was decreased by 3-fold. Furthermore, the proposed algorithms demonstrated the generic characteristic of the proposed ML framework for structural mechanics problems.

Ali Raza, Khaled Mohamed Elhadi, Muhammad Abid et al.

Waste tyre rubber has become an environmental and health concern that needs to be sustainably managed to avoid fire hazards and save natural resources. This research work aims to study the structural behavior of glass fiber reinforced polymer (glass-FRP) reinforced rubberized concrete (GRC) compressive elements under monotonic axial compression loads. Nine GRC circular compressive elements with different axial and crosswise reinforcement ratios were fabricated. All the elements were 300 mm in diameter and 1200 mm in height. A 3D nonlinear finite element equation (FEM) was suggested for the GRC compressive elements using a commercial package ABAQUS. A parametric study has been done to examine the effect of various parameters of GRC elements. The test outcomes revealed that the ductility of GRC elements ameliorated with the lessening in the spaces of glass-FRP ties. The addition of rubberized concrete improved the ductility of GRC elements. The damage to GRC elements occurred due to the vertical cracking along the height of the elements. The estimates of FEM were in close agreement with the test outcomes. The suggested empirical equation depending on the 600 test elements, which considered the lateral confinement effect of FRP ties, presented higher accuracy than previous equations.

NIU Zihao 1, 2, ZHU Zhende 3, QUE Xiangcheng 3, XIE Xinghua 4, JIN Kai 1, 2

With the construction and commissioning of major hydropower projects represented by Baihetan of Jinsha River, it is of great significance to clarify the mechanical and seepage characteristics of engineering rock mass under complex stress environment with high confining pressure and high water pressure. Based on the field survey data and the structural characteristics of the columnar jointed basalt of dam foundation, two kinds of columnar joint similar material model samples with different dip angles β, quadrangular prisms and hexagonal prisms, are prepared, and the true triaxial stress-seepage coupling tests are carried out. The test results show that the columnar jointed rock mass with different cross-section characteristics has strong permeability anisotropy, and the permeability coefficient k is positively correlated with β at different loading stages. During the true triaxial loading process, the volume strain εV of the sample can be used as an effective characterization parameter of k. At the volume compression stage, k shows a low level, and at the volume expansion stage k shows a rapid growth trend. The final failure mode of the samples exhibits three typical forms, and the most dangerous failure mode is the structural failure dominated by the shear slip failure of the joint surface, which mainly occurs in the samples with β=45°, 60°. Correspondingly, the lateral support of this kind of rock mass should be strengthened in the construction design of surrounding rock of tunnels and rock mass of dam foundation.

Mohamed E. Issa, Nasser F. El-Shafey, Ahmed T. Baraghith et al.

Abstract This study aims to explore the flexural behavior of crushed clay brick (CCB) lightweight concrete (LWC) beams strengthened with rubberized strain-hardening cementitious composite reinforced with glass fiber textile mesh layers (GFTM-RSHCC) at the tension side. For this purpose, an experimental investigation consisting of seven simply supported beams, including one un-strengthened specimen, was produced and tested using a monotonic 4-point loading scheme. All specimens had a 120 × 250 mm cross-section, a total length of 2400 mm, and a loaded span of 2200 mm. The studied parameters were the number of GFTM inside the RSHCC (1, 2, or 3) and the thickness of GFTM-RSHCC layer (30 or 40 mm). All the following aspects were tracked: crack pattern, ultimate load, mid-span defection, and ductility. The results show that increasing the number of layers of GFTM and the thickness of RSHCC generally leads to an increase in the ultimate loads and ductility, up to 68% and 83%, respectively, compared to the control beam. Finally, a proposed equation considering the contribution of the GFTM-RSHCC layer was developed to predict the flexural capacity of the strengthened beams. The proposed equation showed good agreement with the experimental results.

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.

Mehran Ebrahimi, Hyunmin Cheong, Pradeep Kumar Jayaraman et al.

In optimizing real-world structures, due to fabrication or budgetary restraints, the design variables may be restricted to a set of standard engineering choices. Such variables, commonly called categorical variables, are discrete and unordered in essence, precluding the utilization of gradient-based optimizers for the problems containing them. In this paper, incorporating the Gumbel-Softmax (GSM) method, we propose a new gradient-based optimizer for handling such variables in the optimal design of large-scale frame structures. The GSM method provides a means to draw differentiable samples from categorical distributions, thereby enabling sensitivity analysis for the variables generated from such distributions. The sensitivity information can greatly reduce the computational cost of traversing high-dimensional and discrete design spaces in comparison to employing gradient-free optimization methods. In addition, since the developed optimizer is gradient-based, it can naturally handle the simultaneous optimization of categorical and continuous design variables. Through three numerical case studies, different aspects of the proposed optimizer are studied and its advantages over population-based optimizers, specifically a genetic algorithm, are demonstrated.

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.