Hasil untuk "Structural engineering (General)"

Ayşe Bengü Sünbül Güner, Ercan Özgan

In geotechnical foundation engineering, the bearing capacity and settlement behaviour of clay soils are key parameters governing foundation performance. Insufficient bearing capacity and excessive settlements limit economical foundation design and may lead to increased structural deformations. This study investigates the physical and mechanical properties of low-plasticity (CL) and high-plasticity (CH) clay soils obtained from boreholes drilled in Düzce Province, Türkiye, where bearing capacity, settlement, and relevant soil parameters were determined through field and laboratory testing and subsequently evaluated using statistical analyses. The calculated bearing capacity values ranged from 192 to 556 kPa, while settlement values varied between 0.88 cm and 5.83 cm. The corresponding maximum-to-minimum ratios were approximately 2.89 for bearing capacity and 6.62 for settlement. The effects of unit weight, water content, particle size distribution, groundwater level, internal friction angle, and cohesion on the bearing capacity and settlement behaviour of the examined clay soils were systematically assessed. The results indicate that unit weight is the most influential parameter for increasing bearing capacity and reducing settlement in CL-type soils, whereas the cohesion coefficient is the dominant parameter in CH-type soils. The results indicate that variations in shear strength and moisture-related parameters exert a significant influence on foundation performance. The findings provide quantitative insight into the relative impact of key soil parameters and offer practical implications for the design of building foundations in clayey soils under similar geological and geotechnical conditions. From a practical perspective, the findings support foundation design, especially in earthquake-prone areas, by accounting for soil bearing capacity and ensuring that settlements remain within permissible limits to maintain long-term structural performance.

Toan Pham Bao, An Huynh-Thai, Luan Vuong-Cong

In the maintenance work of cable configurations, which have limitations such as cables in cable-stayed bridges, suspenders in suspension bridges, and hangers in arch bridges, tension on the cables is required. The safety of the cable is confirmed by checking whether the tensile force on the cable is within the allowable value. In the current widespread practice, cable tension is estimated using the vibration method by measuring the natural frequencies of the cable. However, this method is affected by several factors, including flexural rigidity, sag, and damper. The main difference is that natural frequencies (ωn) are the theoretical frequency of vibration without any energy loss, while damped natural frequencies (ωd) are of the actual system where damping (energy loss) is present. The undamped frequency is a system's inherent property based on its mass and stiffness, while the damped frequency is a practical measurement that is always less than the undamped frequency. This paper proposes a novel method for estimating tensile force that considers global damping. To model the cable as a Rayleigh beam, a theoretical equation for a viscoelastic system has been developed to estimate the natural frequency. The solution method calculates the cable tension and the material damping simultaneously from the natural frequencies. Previous studies verified the validity of the method. The maximum error in the tension is in the range of 4.71% in all valid tests. The evidence confirms the effectiveness of the proposed methods in tension estimation. In this research, the influence of damping on the evaluation of tension is investigated through analytical model, in which the natural frequencies, determined by the damping levels and the damped natural frequencies (measured frequencies). Then, the Hierarchical Bayes model was used to find stable estimates while preserving partial pooling, under sparse data, to stabilize estimates and fully quantify uncertainty. The results show that neglecting damping can cause noticeable errors, especially in low-tension or short cables. The study emphasizes the importance of considering damping in vibration-based tensile force assessments to enhance accuracy and reliability. 

Van den Buverie Nele, Van Mol Noah, Vasseur Lander et al.

Many buildings and structures of (reinforced) concrete reach ages of 50-100 years, and over the lifespan, they can change their function. In some cases, structural assessments should be conducted to guarantee the structure’s safety. Therefore, the material parameters of old construction materials need to be considered. For existing structures, it can be challenging to determine the quality of the reinforcement employed. Furthermore, owing to degradation (e.g. corrosion or fire), the section of rebars and their mechanical parameters can be affected. This research explains how to determine the initial reinforcement quality based on historically available data in Belgium. In addition, the effect of corrosion and high temperatures has been investigated by means of an experimental program on cold-formed dented bars and ribbed Tempcore bars. Based on tensile tests on heated and induced corroded rebars, it was found that both strength and ductility were affected. However, different effects were seen for cold-formed bars versus Tempcore bars with a composite cross-section. Finally, recommendations have been formulated for assessing existing reinforced concrete structures exposed to corrosion or fire.

Houssam KHELALFA, Mustapha TEKKOUK, Hiba BEDJAOUI et al.

This study provides a comprehensive evaluation of the Tabellout RCC dam in Jijel Province, Algeria, a unique structure classified as a combined gravity-arch dam. It addresses critical geotechnical, hydraulic and structural stability aspects, presenting significant contributions to the field of dam engineering by advancing the understanding of the interaction between RCC layers and adjacent slopes under operational conditions. This research employs real-time, full-scale numerical modelling using "Plaxis 2D," with a particular focus on arch behavior. The study bridges a gap by integrating hydrostatic and hydrodynamic pressures measured during the initial filling phase into the model, offering insights into the dam’s behavior under both static and dynamic conditions. Monitoring of RCC layers across three elevations prior to operation revealed minimal displacements, with a maximum recorded displacement of 1 mm in the critical interaction zone between the RCC and the left bank slope. Post-operation analysis demonstrated uniform deformation across elevations, with a negligible 1 mm variance, confirming the homogeneity of RCC stiffness—an essential factor for structural stability. The safety factor (FoS) analysis confirmed stability under static conditions, but highlighted vulnerabilities under seismic conditions, underscoring the need for enhanced resilience measures. This work extends findings from previous studies, particularly by validating the importance of a seismic belt at the foundation, through detailed numerical analysis and comparison with real-time monitoring data from PDL pendulums.

Xian Li, Jianzhuang Xiao, Liangyu Zhu et al.

Abstract Carbon emissions from engineering construction play a critical role in achieving urban carbon peak and neutrality goals. This study evaluates the carbon emission reduction benefits of the renovation project of Yihe Bridge on Beijing Road using a life cycle assessment (LCA) approach. The carbon emissions resulting from the renovation were compared with those of an alternative demolition and reconstruction plan. The calculation boundary for carbon emissions during the bridge construction period was defined based on the renovation project’s specifics, dividing the process into three stages: material production, material transportation, and mechanical construction. By integrating factor decomposition theory with the carbon emission factor method, a carbon emission mode was developed, allowing a comprehensive quantitative analysis for the construction period. Results indicate that total carbon emissions were 84 560.40 t, with material production contributing 94.73%, transportation 1.47%, and mechanical construction 3.80%. The carbon emission intensity of the newly expanded bridge section was 2.11 t/m2. Compared to the demolition and reconstruction, the renovation plan reduced carbon emissions by 53 643.44 t, achieving a 38.81% reduction.

Vicky Choi

We analyze the behavior of stoquastic transverse-field quantum annealing (TFQA) on a structured class of Maximum Independent Set (MIS) instances, using the same decomposition framework developed in our companion work on the DIC-DAC-DOA algorithm (Beyond Stoquasticity). For these instances, we provide a structural explanation for the anti-crossing arising from the competition between the energies associated with a set of degenerate local minima (LM) and the global minimum (GM), and analytically derive the associated exponentially small gap. Our analysis proceeds in two steps. First, we reduce the dynamics to an effective two-block Hamiltonian $H_{core}$, constructed from the bare (decoupled) subsystems associated with the LM and GM. This reduction is justified analytically using the structural decomposition. Second, we reformulate the eigenvalue problem as a generalized eigenvalue problem in a non-orthogonal basis constructed from the bare eigenstates of the subsystems. This transformation enables a clean perturbative treatment of the anti-crossing structure, independent of the transverse field, unlike standard perturbation theory approach, which requires treating the transverse field as a small parameter. This paper serves as a supplementary companion to our main work on the DIC-DAC-DOA algorithm, where we demonstrate how appropriately designed non-stoquastic drivers can bypass this tunneling-induced bottleneck.

Xiangliang Tian, Linchuan Xiang, Shigen Fu et al.

Ceiling beams at the top of tunnels are more common in actual projects. Under the influence of thermal buoyancy, the ceiling structure significantly affects the diffusion characteristics of fire smoke within the tunnel. This study investigated the influence of ceiling structural characteristics (beam height (hB) and beam spacing (dB)) on tunnel longitudinal ventilation through numerical simulation. The results show that the spacing between tunnel ceiling beams has negligible impact on the critical velocity (V), and the determination of the critical velocity is primarily correlated with the height of the ceiling beams. Moreover, it established a dimensionless critical velocity (Vc) model for the tunnels with multiple beams in the ceiling, and this model is suitable for predicting the critical longitudinal velocity of tunnels with ceiling beams whose dimensionless beam height is less than 0.25. When the dimensionless beam height exceeds 0.25, the predictive values of this model are excessively high. This study broadens the application scope of fire smoke control models, which can offer technical support for the design of smoke prevention and exhaust systems in tunnels with similar structures.

Makoto Ohsaki, Kentaro Hayakawa, Jingyao Zhang

We propose a two-level structural optimization method for obtaining an approximate optimal shape of piecewise developable surface without specifying internal boundaries between surface patches. The condition for developability of a polyhedral surface onto a plane is formulated using the area of discrete Gauss map formed by unit normal vectors at the faces adjacent to each vertex. The objective function of the lower-level optimization problem is the sum of square errors for developability at all interior vertices. The contribution of large error to the objective function is underestimated by filtering with hyperbolic tangent function so that the internal boundary between the surface patches can naturally emerge as a result of optimization. Vertices are located non-periodically to generate the internal boundaries in various unspecified directions. Simulated annealing is used for the upper-level optimization problem for maximizing stiffness evaluated by the compliance under the specified vertical loads. The design variables are the heights of the specified points. It is shown in the numerical examples that the compliance values of the surfaces with a square and a rectangular plan are successfully reduced by the proposed method while keeping the developability of each surface patch. Thus, a new class of structural shape optimization problem of shell surfaces is proposed by limiting the feasible surface to piecewise developable surfaces which have desirable geometrical characteristics in view of fabrication and construction.

Jun Yang, Kentaro Yaji, Shintaro Yamasaki

Topology optimization is a structural design methodology widely utilized to address engineering challenges. However, sensitivity-based topology optimization methods struggle to solve optimization problems characterized by strong non-linearity. Leveraging the sensitivity-free nature and high capacity of deep generative models, data-driven topology design (DDTD) methodology is considered an effective solution to this problem. Despite this, the training effectiveness of deep generative models diminishes when input size exceeds a threshold while maintaining high degrees of freedom is crucial for accurately characterizing complex structures. To resolve the conflict between the both, we propose DDTD based on principal component analysis (PCA). Its core idea is to replace the direct training of deep generative models with material distributions by using a principal component score matrix obtained from PCA computation and to obtain the generated material distributions with new features through the restoration process. We apply the proposed PCA-based DDTD to the problem of minimizing the maximum stress in 3D structural mechanics and demonstrate it can effectively address the current challenges faced by DDTD that fail to handle 3D structural design problems. Various experiments are conducted to demonstrate the effectiveness and practicability of the proposed PCA-based DDTD.

Thomas Rochefort-Beaudoin, Aurelian Vadean, Sofiane Achiche et al.

This paper introduces YOLOv8-TO, a novel approach for reverse engineering of topology-optimized structures into interpretable geometric parameters using the YOLOv8 instance segmentation model. Density-based topology optimization methods require post-processing to convert the optimal density distribution into a parametric representation for design exploration and integration with CAD tools. Traditional methods such as skeletonization struggle with complex geometries and require manual intervention. YOLOv8-TO addresses these challenges by training a custom YOLOv8 model to automatically detect and reconstruct structural components from binary density distributions. The model is trained on a diverse dataset of both optimized and random structures generated using the Moving Morphable Components method. A custom reconstruction loss function based on the dice coefficient of the predicted geometry is used to train the new regression head of the model via self-supervised learning. The method is evaluated on test sets generated from different topology optimization methods, including out-of-distribution samples, and compared against a skeletonization approach. Results show that YOLOv8-TO significantly outperforms skeletonization in reconstructing visually and structurally similar designs. The method showcases an average improvement of 13.84% in the Dice coefficient, with peak enhancements reaching 20.78%. The method demonstrates good generalization to complex geometries and fast inference times, making it suitable for integration into design workflows using regular workstations. Limitations include the sensitivity to non-max suppression thresholds. YOLOv8-TO represents a significant advancement in topology optimization post-processing, enabling efficient and accurate reverse engineering of optimized structures for design exploration and manufacturing.

Sai Kiran Burla, Prasad S.R. Pinnelli

Most chemical engineering firms value carbon dioxide as a valuable commodity and the main greenhouse gas that is alarming climate change. The importance of limiting CO2 emissions while also providing sectors with beneficial CO2 is paramount. The current work defines the viability of CO2 capture via gas hydrates, followed by storage and transportation. 0.5 wt% of l-methionine amino acid powder is used as an additive. The experiments were performed in non-stir conditions, and the CO2 hydrate nucleation was observed at 268.3 ± 2.2 K and 2370.7 ± 56.2 kPa. l-methionine accelerated the gas uptake kinetics, and 90% of the hydrate conversion was within 28.7 ± 4.6 minutes, which is 6.25 times faster than the bulk system (without additive). It acted as a catalyst and did not alter the hydrate's structural characteristics. The stochastic nature of hydrate nucleation is abated, and water reuse yielded a similar conversion. The maximum achieved CO2 hydrate yield is 100.3 ± 1.5 v/v, accounting for ∼67% of the maximum feasible value (149.3 v/v). For viable transportation of the captured CO2 in hydrate form, the self-preservation phenomenon is experimentally evaluated at ∼268 K. The study established the prolonged stability of CO2 hydrates in the self-preservation zone for 50 hours. The hydrate preservation is interrelated to the ice capping theory, which describes how a thin layer of liquid water forms on the surface of the hydrate and eventually freezes to form the ice cap. The boil-off observed due to the hydrate melting, and self-annealing was 25% of the total gas. Regasification is easy and accomplished by raising the temperature over the ice melting point. The findings highlight the significance of more extended hydrate stability in the self-preservation window, which could be a valuable tool for CO2 storage and transportation under milder pressure-temperature conditions.

Bradley P. Allen

We describe a method, based on Jennifer Nado's proposal for classification procedures as targets of conceptual engineering, that implements such procedures by prompting a large language model. We apply this method, using data from the Wikidata knowledge graph, to evaluate stipulative definitions related to two paradigmatic conceptual engineering projects: the International Astronomical Union's redefinition of PLANET and Haslanger's ameliorative analysis of WOMAN. Our results show that classification procedures built using our approach can exhibit good classification performance and, through the generation of rationales for their classifications, can contribute to the identification of issues in either the definitions or the data against which they are being evaluated. We consider objections to this method, and discuss implications of this work for three aspects of theory and practice of conceptual engineering: the definition of its targets, empirical methods for their investigation, and their practical roles. The data and code used for our experiments, together with the experimental results, are available in a Github repository.

Oluwatosin Ogundare, Gustavo Quiros Araya, Yassine Qamsane

Industrial process engineering and PLC program development have traditionally favored Function Block Diagram (FBD) programming over classical imperative style programming like the object oriented and functional programming paradigms. The increasing momentum in the adoption and trial of ideas now classified as 'No Code' or 'Low Code' alongside the mainstream success of statistical learning theory or the so-called machine learning is redefining the way in which we structure programs for the digital machine to execute. A principal focus of 'No Code' is deriving executable programs directly from a set of requirement documents or any other documentation that defines consumer or customer expectation. We present a method for generating Function Block Diagram (FBD) programs as either the intermediate or final artifact that can be executed by a target system from a set of requirement documents using a constrained selection algorithm that draws from the top line of an associated recommender system. The results presented demonstrate that this type of No-code generative model is a viable option for industrial process design.

Tao Chen, Miqing Li

Configurable software systems can be tuned for better performance. Leveraging on some Pareto optimizers, recent work has shifted from tuning for a single, time-related performance objective to two intrinsically different objectives that assess distinct performance aspects of the system, each with varying aspirations. Before we design better optimizers, a crucial engineering decision to make therein is how to handle the performance requirements with clear aspirations in the tuning process. For this, the community takes two alternative optimization models: either quantifying and incorporating the aspirations into the search objectives that guide the tuning, or not considering the aspirations during the search but purely using them in the later decision-making process only. However, despite being a crucial decision that determines how an optimizer can be designed and tailored, there is a rather limited understanding of which optimization model should be chosen under what particular circumstance, and why. In this paper, we seek to close this gap. Firstly, we do that through a review of over 426 papers in the literature and 14 real-world requirements datasets. Drawing on these, we then conduct a comprehensive empirical study that covers 15 combinations of the state-of-the-art performance requirement patterns, four types of aspiration space, three Pareto optimizers, and eight real-world systems/environments, leading to 1,296 cases of investigation. We found that (1) the realism of aspirations is the key factor that determines whether they should be used to guide the tuning; (2) the given patterns and the position of the realistic aspirations in the objective landscape are less important for the choice, but they do matter to the extents of improvement; (3) the available tuning budget can also influence the choice for unrealistic aspirations but it is insignificant under realistic ones.

Tomoaki Kisaku, Yuki Yoshida, Kaho Muto et al.

Abstract Air voids remaining on the concrete surface are detrimental to the durability. Surface voids also affect the esthetics of concrete structures. In this study, the method of reducing the surface voids caused by entrapped air of fresh concrete was verified using 122 cases of vibration box tests with different materials, mix proportions, vibration frequencies, and amplitudes. The surface void area ratio (SVAR) decreased when the acceleration of the vibration box was increased regardless of the type of concrete. The relationship between SVAR and acceleration can be expressed through a power function. However, a segregation was observed in medium‐fluidity concrete when the acceleration exceeded 4.0 Gal (hereinafter referred to as G). In addition, the SVAR correlated with the mortar rheological constant was measured using a steel ball pull‐up test. An equation using mortar rheological constants, which can be estimated using a concrete slump, was proposed to predict the SVAR‐acceleration relationship.

Gopi Krishnan Rajbahadur, Shaowei Wang, Yasutaka Kamei et al.

Researchers usually discretize a continuous dependent variable into two target classes by introducing an artificial discretization threshold (e.g., median). However, such discretization may introduce noise (i.e., discretization noise) due to ambiguous class loyalty of data points that are close to the artificial threshold. Previous studies do not provide a clear directive on the impact of discretization noise on the classifiers and how to handle such noise. In this paper, we propose a framework to help researchers and practitioners systematically estimate the impact of discretization noise on classifiers in terms of its impact on various performance measures and the interpretation of classifiers. Through a case study of 7 software engineering datasets, we find that: 1) discretization noise affects the different performance measures of a classifier differently for different datasets; 2) Though the interpretation of the classifiers are impacted by the discretization noise on the whole, the top 3 most important features are not affected by the discretization noise. Therefore, we suggest that practitioners and researchers use our framework to understand the impact of discretization noise on the performance of their built classifiers and estimate the exact amount of discretization noise to be discarded from the dataset to avoid the negative impact of such noise.

Xinyi Lei, Zhicheng Chen, Hui Li et al.

Diagnosing the changes of structural behaviors using monitoring data is an important objective of structural health monitoring (SHM). The changes in structural behaviors are usually manifested as the feature changes in monitored structural responses; thus, developing effective methods for automatically detecting such changes is of considerable significance. Existing methods for change detection in SHM are mainly used for scalar or vector data, thus incapable of detecting the changes of the features represented by complex data, e.g., the probability density functions (PDFs). Detecting the abrupt changes occurred in the distributions (represented by PDFs) associated with the damage-sensitive features extracted from SHM data are usually of crucial interest for structural condition assessment; however, the SHM community still lacks effective diagnostic tools for detecting such changes. In this study, a change-point detection method is developed in the functional data-analytic framework for PDF-valued sequence, and it is leveraged to diagnose the distributional information break encountered in structural condition assessment. A major challenge in PDF-valued data modeling or analysis is that the PDFs are special functional data subjecting to nonlinear constraints. To tackle this issue, the PDFs are embedded into the Bayes space, and the associated change-point model is constructed by using the linear structure of the Bayes space; then, a hypothesis testing procedure is presented for distributional change-point detection based on the isomorphic mapping between the Bayes space and a functional linear space. Comprehensive simulation studies are conducted to validate the effectiveness of the proposed method as well as demonstrate its superiority over the competing method. Finally, an application to real SHM data illustrates its practical utility in structural condition assessment.

Pawel Gniewek, Bradley Worley, Kate Stafford et al.

In drug discovery, structure-based virtual high-throughput screening (vHTS) campaigns aim to identify bioactive ligands or "hits" for therapeutic protein targets from docked poses at specific binding sites. However, while generally successful at this task, many deep learning methods are known to be insensitive to protein-ligand interactions, decreasing the reliability of hit detection and hindering discovery at novel binding sites. Here, we overcome this limitation by introducing a class of models with two key features: 1) we condition bioactivity on pose quality score, and 2) we present poor poses of true binders to the model as negative examples. The conditioning forces the model to learn details of physical interactions. We evaluate these models on a new benchmark designed to detect pose-sensitivity.

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