Hasil untuk "Structural engineering (General)"

Nidhal Selmi, Jean-michel Bruel, Sébastien Mosser et al.

Decision-making is a core engineering design activity that conveys the engineer's knowledge and translates it into courses of action. Capturing this form of knowledge can reap potential benefits for the engineering teams and enhance development efficiency. Despite its clear value, traditional decision capture often requires a significant amount of effort and still falls short of capturing the necessary context for reuse. Model-based systems engineering (MBSE) can be a promising solution to address these challenges by embedding decisions directly within system models, which can reduce the capture workload while maintaining explicit links to requirements, behaviors, and architectural elements. This article discusses a lightweight framework for integrating decision capture into MBSE workflows by representing decision alternatives as system model slices. Using a simplified industry example from aircraft architecture, we discuss the main challenges associated with decision capture and propose preliminary solutions to address these challenges.

Muhammad Tayyab Khan, Zane Yong, Lequn Chen et al.

Accurate extraction of key information from 2D engineering drawings is crucial for high-precision manufacturing. Manual extraction is slow and labor-intensive, while traditional Optical Character Recognition (OCR) techniques often struggle with complex layouts and overlapping symbols, resulting in unstructured outputs. To address these challenges, this paper proposes a novel hybrid deep learning framework for structured information extraction by integrating an Oriented Bounding Box (OBB) detection model with a transformer-based document parsing model (Donut). An in-house annotated dataset is used to train YOLOv11 for detecting nine key categories: Geometric Dimensioning and Tolerancing (GD&T), General Tolerances, Measures, Materials, Notes, Radii, Surface Roughness, Threads, and Title Blocks. Detected OBBs are cropped into images and labeled to fine-tune Donut for structured JSON output. Fine-tuning strategies include a single model trained across all categories and category-specific models. Results show that the single model consistently outperforms category-specific ones across all evaluation metrics, achieving higher precision (94.77% for GD&T), recall (100% for most categories), and F1 score (97.3%), while reducing hallucinations (5.23%). The proposed framework improves accuracy, reduces manual effort, and supports scalable deployment in precision-driven industries.

Allysson Allex Araújo, Marcos Kalinowski, Maria Teresa Baldassarre

In recent years, Software Engineering (SE) scholars and practitioners have emphasized the importance of integrating soft skills into SE education. However, teaching and learning soft skills are complex, as they cannot be acquired passively through raw knowledge acquisition. On the other hand, hackathons have attracted increasing attention due to their experiential, collaborative, and intensive nature, which certain tasks could be similar to real-world software development. This paper aims to discuss the idea of hackathons as an educational strategy for shaping SE students' soft skills in practice. Initially, we overview the existing literature on soft skills and hackathons in SE education. Then, we report preliminary empirical evidence from a seven-day hybrid hackathon involving 40 students. We assess how the hackathon experience promoted innovative and creative thinking, collaboration and teamwork, and knowledge application among participants through a structured questionnaire designed to evaluate students' self-awareness. Lastly, our findings and new directions are analyzed through the lens of Self-Determination Theory, which offers a psychological lens to understand human behavior. This paper contributes to academia by advocating the potential of hackathons in SE education and proposing concrete plans for future research within SDT. For industry, our discussion has implications around developing soft skills in future SE professionals, thereby enhancing their employability and readiness in the software market.

Jonathan Ullrich, Matthias Koch, Andreas Vogelsang

With the advent of generative LLMs and their advanced code generation capabilities, some people already envision the end of traditional software engineering, as LLMs may be able to produce high-quality code based solely on the requirements a domain expert feeds into the system. The feasibility of this vision can be assessed by understanding how developers currently incorporate requirements when using LLMs for code generation-a topic that remains largely unexplored. We interviewed 18 practitioners from 14 companies to understand how they (re)use information from requirements and other design artifacts to feed LLMs when generating code. Based on our findings, we propose a theory that explains the processes developers employ and the artifacts they rely on. Our theory suggests that requirements, as typically documented, are too abstract for direct input into LLMs. Instead, they must first be manually decomposed into programming tasks, which are then enriched with design decisions and architectural constraints before being used in prompts. Our study highlights that fundamental RE work is still necessary when LLMs are used to generate code. Our theory is important for contextualizing scientific approaches to automating requirements-centric SE tasks.

Xitai Feng, Jiangbi Hu, Qingxin Hu

As an essential highway safety facility, roadside W-beam guardrails effectively prevent errant vehicles from entering hazardous zones or causing secondary collisions by blocking and redirecting them, thereby reducing accident severity. With the rapid development of the automotive industry, the front bumper height of small passenger cars generally ranges between 405 mm and 485 mm. However, the lower edge height of the current Chinese Class A W-beam guardrail is 444 mm above the ground, which leads to a high risk of “underride” during collisions, resulting in elevated occupant injury risks. To address this issue, this paper proposes an optimized guardrail structure composed of a double W-beam and a C-type beam, aiming to reduce the underride risk for small passenger cars while accommodating multi-vehicle protection needs. In this design, the double W-beam is installed at a height of 560 mm and the C-type beam at 850 mm, connected to circular posts using a regular hexagonal anti-obstruction block. The beam thickness is uniformly 3 mm, while the thickness of other components is 4 mm. To systematically evaluate the impact of material strength on both safety performance and cost, two material configurations are proposed: Scheme 1 uses Q235 carbon steel for all components; Scheme 2 reduces the thickness of the C-type beam to 2.5 mm and employs Q355 high-strength low-alloy steel, with the thickness of the connected anti-obstruction block reduced to 3.5 mm, while the other components retain Q235 steel and unchanged structural dimensions. Using finite element simulation, collisions involving small passenger cars, medium trucks, and buses are simulated, and performance comparisons are conducted based on vehicle trajectory and guardrail deformation. For the small passenger car scenario, risk quantification indicators—Acceleration Severity Index (ASI), Theoretical Head Impact Velocity (THIV), and Post-impact Head Deceleration (PHD)—are introduced to assess occupant injury. The results demonstrate that Scheme 2 not only meets the required protection level but also significantly reduces occupant risk for small passenger cars, lowering the injury rating from Class C to Class B. Moreover, the overall structural mass is reduced by approximately 1407 kg per kilometer, with material costs decreased by about RMB 10,129, demonstrating favorable economic efficiency. The proposed structural optimization not only effectively mitigates small car underride and improves multi-vehicle protection performance but also provides the industry with a novel guardrail geometric design directly applicable to engineering practice. The technical approach of enhancing material strength and reducing component thickness also offers a feasible reference for lightweight design, material savings, and cost optimization of guardrail systems, contributing significantly to improving the safety and sustainability of road transportation infrastructure.

WEI Huanwei, ZHAO Jizhang, ZHENG Xiao et al.

ObjectiveWind energy plays a crucial role in achieving sustainable energy goals. As a critical structural component, the wind turbine foundation significantly influences the operational stability, safety, and long-term performance of wind turbine systems. However, structural health monitoring (SHM) of wind turbine foundations often faces challenges with data integrity due to environmental factors, sensor malfunctions, or data transmission issues. These missing data can severely impact the accuracy of structural health assessments, thereby affecting maintenance decisions and operational safety. To tackle the persistent data gaps in the monitoring system of adjustable wind turbine foundations, this study introduces a frequency-space-time domain attention network (FST-ATTNet). This model is designed to enhance the modeling capabilities for complex time-series data, improve the accuracy of missing data reconstruction, and ensure the reliability of health monitoring, ultimately guaranteeing wind turbines' safe and efficient operation. Moreover, it presents potential solutions for similar data reconstruction challenges across various engineering disciplines.MethodsThe FST-ATTNet model introduces an innovative data repair framework by integrating features from the frequency, time, and spatial domains. In the frequency domain, the model employs discrete cosine transform (DCT) to extract periodic and global patterns from time series data, effectively mitigating the high-frequency noise caused by the Gibbs phenomenon in traditional discrete Fourier transform (DFT). A frequency-domain attention mechanism is introduced to enhance this process, adaptively assigning weights to frequency components and prioritizing those most relevant for data reconstruction. In the time domain, Bidirectional Gated Recurrent Units (BiGRU) capture both forward and backward dependencies within the time series, ensuring a comprehensive understanding of local sequence patterns. The Kolmogorov-Arnold Network (KAN) incorporates a B-spline activation function, further enhancing the model's ability to capture complex nonlinear temporal changes. In the spatial domain, the Temporal Convolutional Network (TCN) models long-range dependencies by expanding causal convolutions, thereby capturing local and global spatial relationships. The Squeeze-and-Excitation Network (SENet) further boosts spatial feature extraction by dynamically adjusting the importance of different feature channels. By combining these various attention mechanisms, FST-ATTNet successfully integrates frequency, time, and spatial domain features, achieving superior modeling of complex time series patterns and robust reconstruction of missing data. The model is validated using monitoring data of the measured strain on a pile-based adjustable wind turbine foundation, and its performance is evaluated using the coefficient of determination (<italic>R</italic>²) and mean squared error (<italic>MSE</italic>) metrics.Results and Discussions　Validation experiments based on measured data show that FST-ATTNet has the following advantages: (1) Superior performance compared to traditional models: FST-ATTNet outperforms traditional sequence models in data reconstruction tasks, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional GRU (BiGRU), Temporal Convolutional Network (TCN), and Transformer, achieving excellent results with <italic>R</italic>² of 0.98 and <italic>MSE</italic> of 0.66. In contrast, the <italic>R</italic>² of LSTM and GRU models is only 0.86, and the <italic>MSE</italic> is 2.35 and 2.34, respectively, which are limited by their unidirectional or local feature extraction capabilities. The Transformer model performs the worst, with <italic>R</italic>² of 0.72 and <italic>MSE</italic> as high as 2.77, likely due to its inability to effectively capture local patterns and frequency domain features in multivariate time series data. FST-ATTNet, through deep integration of frequency, time, and spatial information, can capture complex patterns, including periodicity, local dynamics, and long-term dependencies, significantly improving reconstruction accuracy. (2) Robustness in high missing-rate scenarios: The model excels in handling severe data-missing scenarios. In the continuous data missing test, even with a missing rate as high as 40%, FST-ATTNet maintains high reconstruction accuracy with <italic>R</italic>² of 0.94 and <italic>MSE</italic> of 0.95. At the 45% missing rate, performance slightly declines, with <italic>R</italic>² of 0.87 and <italic>MSE</italic> of 1.31, but still outperforms other models. In the feature missing test, when 25% of the monitoring features are entirely missing, the model achieves <italic>R</italic>² of 0.91 and <italic>MSE</italic> of 0.75, demonstrating its ability to handle complex multi-feature missing scenarios commonly found in actual monitoring systems. (3) Insights from ablation experiments: Ablation experiments provide key insights into the contribution of each component of FST-ATTNet. After removing the frequency-domain enhanced attention mechanism, <italic>R</italic>² decreases to 0.92, and <italic>MSE</italic> increases to 1.13. After removing SENet, <italic>R</italic>² is 0.91, and <italic>MSE</italic> is 1.22, indicating that these attention mechanisms play a crucial role in feature enhancement. Removing all attention mechanisms results in further performance degradation, with <italic>R</italic>² of 0.90 and <italic>MSE</italic> of 1.20, highlighting their importance in the prioritization of selective features. Removing the KAN network results in <italic>R</italic>² of 0.92 and <italic>MSE</italic> of 1.09, indicating its contribution to modeling complex time series patterns. Using only frequency domain, time domain, or spatial information results in significant performance drops, with <italic>R</italic>² of 0.75, 0.68, and 0.86, respectively, and <italic>MSE</italic> of 1.98, 2.44, and 1.33, indicating the necessity of integrating frequency, time, or spatial information. Spatial information is especially critical in high-missing scenarios. (4) Applicability of the model: To evaluate the model's applicability, FST-ATTNet was applied to anchor cable monitoring data with a missing rate of up to 50%, achieving excellent results with <italic>R</italic>² of 0.92 and <italic>MSE</italic> of 0.80. The model achieved near-perfect reconstruction for datasets with strong periodicity at a 25% missing rate (<italic>R</italic>² = 0.97, <italic>MSE</italic> = 0.40). However, performance slightly declined at a 50% missing rate (<italic>R</italic>² = 0.92, <italic>MSE</italic> = 0.80), with deviations at the peaks primarily due to the training data not fully covering the entire cycle. Nonetheless, FST-ATTNet demonstrates adaptability across different monitoring scenarios and a unique ability to handle cyclic patterns in periodic data reconstruction.ConclusionsThe FST-ATTNet model offers a reliable and robust solution to the problem of continuous data loss in the health monitoring of pile-type adjustable wind turbine foundations. By deeply integrating frequency, time, and spatial domain information and incorporating advanced attention mechanisms, the model achieves exceptional reconstruction accuracy in high missing-rate scenarios, significantly outperforming traditional sequence models. Furthermore, the successful application of the model to other monitoring datasets (such as anchor cable data) demonstrates its versatility and broad applicability in structural health monitoring. FST-ATTNet not only enhances the reliability of wind turbine foundation monitoring but also provides innovative solutions to similar data repair challenges in other engineering domains, offering crucial support for the safety and efficiency of wind turbine systems.

ZENG Zhuo, ZHENG Honggang, XIANG Sheng et al.

As the bridge construction goes forward to the deep-water environments, the permanent floating bridge structures have attracted more and more attention from international scholars. The project cases of worldwide representative permanent floating bridges were presented. The structural systems of the built permanent floating bridges were summarized. The research and application advances regarding the mechanical features, the construction process, and the special configurations of the permanent floating bridges were introduced. Finally, from the perspectives of the engineering economy and environment applicability, the development prospect of permanent floating bridges was analyzed. The research shows that the permanent floating bridge structure has been applied in engineering around the world and has two types of structural systems, which are the continuous pontoon system and the discrete pontoon system. The permanent floating bridges adopted in deep-water environments have shown superior engineering economy. Based on further research and verifications, the permanent floating bridge structures can be applied in deep-water crossing projects.

Mohammad Abedi, Terje Kanstad, Stefan Jacobsen et al.

Cracks induced by concrete shrinkage may allow corrosive agents to penetrate steel reinforced concrete and deteriorate the reinforcement. Adding randomly distributed steel fiber to concrete can reduce shrinkage and prevent the initiation of cracking, along with improving tensile strength and ductility. Autogenous shrinkage and drying shrinkage are the most important types of shrinkage in steel fiber reinforced concrete (SFRC). There is a general lack of knowledge related to the autogenous and drying shrinkage of SFRC. In the paper, the main factors that affect the autogenous shrinkage and drying shrinkage of SFRC are thoroughly examined through literature review. These factors include fiber volume, fiber geometry, curing method, water binder ratio (w/b), expansive agent, shrinkage mitigation, supplementary cementitious material (SCM), admixtures and hybridizing different fiber materials. It is concluded that increasing fiber volume up to some optimal volume, using expansive agents and shrinkage mitigation, as well as hybridizing different fibers, can reduce markedly the drying shrinkage. Furthermore, the water binder ratio, curing method and presence of silica fume, fly ash, and ground granulated blast furnace slag (GGBFS) influence shrinkage development in SFRC. In the last part, models predicting the autogenous and drying shrinkage of SFRC are discussed and new research is proposed.

Marvin Wyrich, Marvin Muñoz Barón, Justus Bogner

Software engineering (SE) is full of abstract concepts that are crucial for both researchers and practitioners, such as programming experience, team productivity, code comprehension, and system security. Secondary studies aimed at summarizing research on the influences and consequences of such concepts would therefore be of great value. However, the inability to measure abstract concepts directly poses a challenge for secondary studies: primary studies in SE can operationalize such concepts in many ways. Standardized measurement instruments are rarely available, and even if they are, many researchers do not use them or do not even provide a definition for the studied concept. SE researchers conducting secondary studies therefore have to decide a) which primary studies intended to measure the same construct, and b) how to compare and aggregate vastly different measurements for the same construct. In this experience report, we discuss the challenge of study selection in SE secondary research on latent variables. We report on two instances where we found it particularly challenging to decide which primary studies should be included for comparison and synthesis, so as not to end up comparing apples with oranges. Our report aims to spark a conversation about developing strategies to address this issue systematically and pave the way for more efficient and rigorous secondary studies in software engineering.

Nikhil Patnaik, Joseph Hallett, Awais Rashid

Writing secure code is challenging and so it is expected that, following the release of code-generative AI tools, such as ChatGPT and GitHub Copilot, developers will use these tools to perform security tasks and use security APIs. However, is the code generated by ChatGPT secure? How would the everyday software or security engineer be able to tell? As we approach the next decade we expect a greater adoption of code-generative AI tools and to see developers use them to write secure code. In preparation for this, we need to ensure security-by-design. In this paper, we look back in time to Saltzer & Schroeder's security design principles as they will need to evolve and adapt to the challenges that come with a world of AI-generated code.

Oleg Naimark, Vladimir Oborin, Mikhail Bannikov

We propose the interpretation of the Finite Fracture Mechanics based on the criticality of damage-failure transition due to specific metastability of free energy release. Multiscale mechanisms of fatigue damage-failure transitions in metals are studied for Very High Cycle Fatigue and analyzed as duality of inherently linked two types of singularities related to the collective modes of defects and singularity of stress field as the classical framework of fracture mechanics. Development of collective modes of defects (solitary waves of plastic strain localization and blow-up dissipative structures of damage localization) with the nature of intermediate self-similar solutions are considered for the interpretation of the incomplete self-similarity and mechanism of small crack nucleation (�fish-eye� area in VHCF) and growth up to the Paris crack size. Spatial structural scales corresponding to different stages of damage-failure transition were identified due to the analysis of roughness correlation and estimating of the power (the Hurst) exponent and corresponding structural lengths of characteristic fracture surface areas These lengths and power exponents were used in the constitutive laws as the structure sensitive parameters for characteristic damage-failure transition stages (small crack initiation and growth, the Paris crack advance).

Athina Mitropoulou, Dionysios N. Markatos, Andreas Dimopoulos et al.

Abstract Tissue engineering scaffolds as three-dimensional substrates may serve as ideal templates for tissue regeneration by simulating the structure of the extracellular matrix (ECM). Many biodegradable synthetic polymers, either hydrophobic, like Poly-ε-caprolactone (PCL), or hydrophilic, like Poly(Vinyl Alcohol) (PVA), are widely used as candidate bioactive materials for fabricating tissue engineering scaffolds. However, a combination of good cytocompatibility of hydrophilic polymers with good biomechanical performance of hydrophobic polymers could be beneficial for the in vivo performance of the scaffolds. In this study, we aimed to fabricate biodegradable fibrous scaffolds by combining the properties of hydrophobic PCL with those of hydrophilic PVA and evaluate their properties in comparison with pristine PCL scaffolds. Therefore, single-layered PCL scaffolds, sequential tri-layered (PVA/PCL/PVA), and core-shell (PVA as shell and PCL as core) composite scaffolds were developed utilizing the electrospinning technique. The material structural and biomechanical properties of the electrospun scaffolds, before and after their hydrolytic degradation over a seven-month period following storage in phosphate-buffered saline (PBS) at 37 °C, were comprehensively compared. In addition, human embryonic kidney cells (HEK-293) were cultured on the scaffolds to investigate potential cell attachment, infiltration, and proliferation. The results demonstrated the long-term efficacy of core-shell biodegradable fibrous scaffolds in comparison to single-layers PCL and tri-layers PVA/PCL/PVA, not only due to its superior morphological characteristics and mechanical properties, but also due to its ability to promote homogeneous cell distribution and proliferation, without any external chemical or physical stimuli. Graphical Abstract

Xiaofeng Wang, Henry Edison, Dron Khanna et al.

[Context] Systematic Literature Review (SLR) has been a major type of study published in Software Engineering (SE) venues for about two decades. However, there is a lack of understanding of whether an SLR is really needed in comparison to a more conventional literature review. Very often, SE researchers embark on an SLR with such doubts. We aspire to provide more understanding of when an SLR in SE should be conducted. [Objective] The first step of our investigation was focused on the dataset, i.e., the reviewed papers, in an SLR, which indicates the development of a research topic or area. The objective of this step is to provide a better understanding of the characteristics of the datasets of SLRs in SE. [Method] A research synthesis was conducted on a sample of 170 SLRs published in top-tier SE journals. We extracted and analysed the quantitative attributes of the datasets of these SLRs. [Results] The findings show that the median size of the datasets in our sample is 57 reviewed papers, and the median review period covered is 14 years. The number of reviewed papers and review period have a very weak and non-significant positive correlation. [Conclusions] The results of our study can be used by SE researchers as an indicator or benchmark to understand whether an SLR is conducted at a good time.

Wouter Groeneveld, Laurens Luyten, Joost Vennekens et al.

Creativity is a critical skill that professional software engineers leverage to tackle difficult problems. In higher education, multiple efforts have been made to spark creative skills of engineering students. However, creativity is a vague concept that is open to interpretation. Furthermore, studies have shown that there is a gap in perception and implementation of creativity between industry and academia. To better understand the role of creativity in software engineering (SE), we interviewed 33 professionals via four focus groups and 10 SE students. Our results reveal 45 underlying topics related to creativity. When comparing the perception of students versus professionals, we discovered fundamental differences, grouped into five themes: the creative environment, application of techniques, creative collaboration, nature vs nurture, and the perceived value of creativity. As our aim is to use these findings to install and further encourage creative problem solving in higher education, we have included a list of implications for educational practice.

Lihua Hu, Liyuan Yu, Minghe Ju et al.

The effect of intermediate stress (in situ tunnel axial) on a strainburst is studied with a three-dimensional (3D) bonded block distinct element method (DEM). A series of simulations of strainbursts under true triaxial in situ stress conditions (i.e. high tangential stress, moderate intermediate stress and low radial stress) of near-boundary rock masses are performed. Compared with the experimental results, the DEM model is able to capture the stress-strain response, failure pattern and energy balance of strainbursts. The fracturing processes of strainbursts are also numerically reproduced. Numerical results show that, as the intermediate stress increases: (1) The peak strain of strainbursts increases, the yield stress increases, the rock strength increases linearly, and the ratio of yield stress to rock strength decreases, indicating that the precursory information on strainbursts is enhanced; (2) Tensile and shear cracks increase significantly, and slabbing and bending of rock plates are more pronounced; and (3) The stored elastic strain energy and dissipated energy increase linearly, whereas the kinetic energy of the ejected rock fragments increases approximately exponentially, implying an increase in strainburst intensity. By comparing the experimental and numerical results, the effect of intermediate stress on the rock strength of strainbursts is discussed in order to address three key issues. Then, the Mogi criterion is applied to construct new strength criteria for strainbursts by converting the one-face free true triaxial stress state of a strainburst to its equivalent true triaxial stress state. In summary, the effect of intermediate stress on strainbursts is a double-edged sword that can enhance the rock strength and the precursory information of a strainburst, but also increase its intensity.

Melissa Nel, Melvin Simuyaba, Justina Muchelenje et al.

IntroductionBroad Brush Surveys (BBS) are a rapid, qualitative assessment approach using four meta-indicators -physical features, social organization, social networks and community narratives - to gauge how local context interfaces with service/intervention options, implementation and uptake.MethodsIn 2021, responding to rapid urbanization and the accompanying need for water and sanitation services, BBS was innovatively applied by social scientists and engineers to assess water and sanitation infrastructure, both formal and informal, in two African cities - Lusaka and Cape Town. In four urban communities, identified with local stakeholders, BBS data collection included: four mapping group discussions with local stakeholders (participants = 24); eight transect walks/drives; 60 structured observations of water and sanitation options, transport depots, health facilities, weekends, nights, rainy days; seven mixed gender focus group discussions (FGDs) with older and young residents (participants = 86); 21 key-informant interviews (KII, participants = 21).ResultsFindings were rapidly summarized into community profiles, including narrative reports, maps and posters, and first discussed with community stakeholders, then at national/provincial levels. The meta-indicator framework and set sequence of qualitative activities allowed the detail on water and sanitation to gradually emerge. For example, the mapping discussion identified water sources considered a risk for waterborne infections, further observed in the transect walks and then structured observations, which compared their relative condition and social interactions and what local residents narrated about them. FGDs and KIIs elaborated on the control of these sources, with nuanced detail, including hidden sources and the use of different water sources for different activities also emerging.DiscussionWe demonstrated that despite some limitations, BBS provided useful insight to systems and social processes surrounding formal and informal water and sanitation infrastructure in and across designated urban areas. Furthermore, BBS had the potential to galvanize local action to improve infrastructure, and illuminated the value of informal options in service delivery.

Ahmed A. Elansary, Mohamed I. Metwally, Adel G. El-Attar

Abstract Structural engineers should appropriately design concrete structures to resist lateral loads. Determining the adequate system for resisting the expected lateral loads is important to control the building drift. Choosing the appropriate system is usually conducted assuming the predicted forces are applied to completed concrete buildings at one step which is commonly known as ordinary analysis (OA). Nevertheless, these structures are constructed sequentially which requires using staged analysis (SA) instead of OA. In this paper, a comprehensive numerical model for SA of concrete buildings, which accounts for time dependent effects, is utilized using a well-validated commercial software. Six reinforced concrete buildings with 10 and 20 storeys are analyzed using the developed model. Three various structural systems are considered (Rigid Frame (RF), Shear Wall (SW), and Wall Frame (WF). A comparison is conducted between the displacements and internal forces in beams and slabs obtained from the SA and OA. For a 10-storeys RF building, maximum bending moment from SA is 29.9% higher than that from OA. The same conclusion was observed for the maximum shearing force with a percentage of 19.6%. Moreover, maximum bending moments and shearing forces from SA for the 20-storeys RF building are, respectively, 35.0% and 23.5% larger than those from OA. The RF and WF systems provided the minimum difference in differential displacement between the OA and SA analyses. The RF system produced the least differences in internal forces from OA and SA for all studied buildings.

Alovya Ahmed Chowdhury, Georges Kesserwani, Charles Rougé et al.

Wavelet-based grid resolution adaptation driven by the ‘multiresolution analysis’ (MRA) of the Haar wavelet (HW) allows to devise an adaptive first-order finite volume (FV1) model (HWFV1) that can readily preserve the modelling fidelity of its reference uniform-grid FV1 counterpart. However, the MRA entails an enormous computational effort as it involves ‘encoding’ (coarsening), ‘decoding’ (refining), analysing and traversing modelled data across a deep hierarchy of nested, uniform grids. GPU-parallelisation of the MRA is needed to handle its computational effort, but its algorithmic structure (1) hinders coalesced memory access on the GPU and (2) involves an inherently sequential tree traversal problem. This work redesigns the algorithmic structure of the MRA in order to parallelise it on the GPU, addressing (1) by applying Z-order space-filling curves and (2) by adopting a parallel tree traversal algorithm. This results in a GPU-parallelised HWFV1 model (GPU-HWFV1). GPU-HWFV1 is verified against its CPU predecessor (CPU-HWFV1) and its GPU-parallelised reference uniform-grid counterpart (GPU-FV1) over five shallow water flow test cases. GPU-HWFV1 preserves the modelling fidelity of GPU-FV1 while being up to 30 times faster. Compared to CPU-HWFV1, it is up to 200 times faster, suggesting that the GPU-parallelised MRA could be used to speed up other FV1 models. HIGHLIGHTS Wavelet-based grid adaptation is parallelised on the GPU via a Z-order space-filling curve and a parallel tree traversal algorithm.; An adaptive Haar wavelet first-order finite volume shallow water model running on the GPU is developed (GPU-HWFV1).; GPU-HWFV1 is 20–300 times faster than its single-core serial CPU version 4.; GPU-HWFV1 is 1.3–30 times faster than its GPU-parallelised reference uniform-grid counterpart.;

Yimin Qiu, Chenxi Zhai, Ling Chen et al.

The dearth of knowledge on the diverse structures and functions in bacterial collagen-like proteins is in stark contrast to the deep grasp of structures and functions in mammalian collagen, the ubiquitous triple-helical scleroprotein that plays a central role in tissue architecture, extracellular matrix organization, and signal transduction. To fill and highlight existing gaps due to the general paucity of data on bacterial CLPs, we comprehensively reviewed the latest insight into their functional and structural diversity from multiple perspectives of biology, computational simulations, and materials engineering. The origins and discovery of bacterial CLPs were explored. Their genetic distribution and molecular architecture were analyzed, and their structural and functional diversity in various bacterial genera was examined. The principal roles of computational techniques in understanding bacterial CLPs' structural stability, mechanical properties, and biological functions were also considered. This review serves to drive further interest and development of bacterial CLPs, not only for addressing fundamental biological problems in collagen but also for engineering novel biomaterials. Hence, both biology and materials communities will greatly benefit from intensified research into the diverse structures and functions in bacterial collagen-like proteins.