Field Of Structural Engineering Research Articles

Enhancement of the structural behavior of elevated water tanks using X-plate dampers

This study aims at utilizing of X-plate dampers to effectively address vibrations and minimize the possible structural harm to water tank facilities, and also at utilizing a finite element methodology to simulate and analyze the structural reactions. A non-linear time history analysis is employed to assess the effectiveness of X-plate dampers when subjected to different blast-induced ground motions. The efficiency of dampers was evaluated by conducting a comparative analysis of liquid storage structures with and without dampers using 50 m3 and 1500 m3 tanks in empty and full conditions. X – plate dampers are installed in a variety of bracing schemes, such as diagonal, X, V, Inverted V, and K bracing subjected to underground blast loads to improve the resilience of the selected elevated water tanks. The results showed the X-bracing pattern is the most effective configuration for X-plate dampers in mitigating blast loads in a 50 m3 elevated tank without water, resulting in a reduction of displacement, shear force, and bending moment by 43%, 15%, and 22%, in case of elevated tank with water reduced by 39% 25% and 33% respectively. In 1500 m3 elevated tank without water, the K-bracing is determined to be the most effective type of bracing, reducing the displacement, shear force, and bending moment by 50%, 70%, and 70%, in case of elevated tank with water reduced by 53%, 52%, and 54% respectively. The current study makes a significant contribution to the field of structural engineering by providing useful insights into raised water tank construction.

Predictive modeling of aramid fiber reinforced polymer confinement for improved compressive strength in circular concrete columns

Fiber Reinforced Polymers (FRPs) have gained significant attention in the field of structural engineering due to their high strength-to-weight ratio, corrosion resistance, and ease of application. Among various types of FRPs, Aramid Fiber Reinforced Polymers (AFRP) stand out for their exceptional tensile strength and durability, making them ideal for confining concrete columns to enhance their compressive strength. This study aims to leverage these benefits by predicting the compressive strength of circular concrete columns confined with AFRP using Artificial Neural Networks (ANNs). To develop and validate the ANN model, a comprehensive dataset consisting of 190 samples was employed during the training phase, while an additional set of 33 samples was used for validation. The performance and predictive capabilities of the ANN model were thoroughly assessed through extensive testing and direct comparison with experimental results, which demonstrated the model’s high accuracy and reliability. Moreover, a detailed parametric study was conducted to examine the influence of various input parameters on the compressive strength prediction. The findings from this study offered significant insights into the effects of various parameters on the predicted outcomes, including column diameter, unconfined concrete strength and strain, as well as AFRP confinement parameters such as thickness, tensile strength, elastic modulus, and strain. Notably, the sensitivity analysis underscored the profound impact of the tensile strength of AFRP on the ANN model's predictive accuracy. The research offers a robust tool for engineers, enabling them to estimate the compressive strength of AFRP-confined circular concrete columns accurately. Additionally, it provides crucial insights that are instrumental in optimizing the design and application of AFRP wrapping or tube-encased methods within the realm of structural engineering. Overall, this research marks a significant step forward in the field of structural engineering, providing a valuable predictive tool and offering insights that can lead to the development of more resilient and sustainable infrastructure.

Machine learning and traditional approaches in shear reliability of steel fiber reinforced concrete beams

In the field of structural engineering, the exploration of steel fibre reinforced concrete (SFRC) beams has recently intensified, particularly due to their improved tension and shear performance of structure. This study pioneers a novel reliability analysis of shear capacity predictions for SFRC beams, distinctively classifying the datasets into high-strength (HSFRC) and normal-strength (NSFRC) categories. A comprehensive database of 142 HSFRC and 265 NSFRC beams serves as the foundation for this analysis, which critically examines the standard Gaussian distribution in shear design models and proposes the Lognormal and Weibull distributions as more precise alternatives. Employing advanced First-order (FORM) and Second-order Reliability Methods (SORM), the study covers a broad spectrum of load conditions, including dead, live, snow, wind, and seismic loads, to evaluate various empirical, semi-empirical and machine learning proposed shear capacity prediction formulas. One of the key innovations of this research is the development of differentiated resistance coefficients for various risk levels in the reliability analysis, allowing future structural designers to tailor their designs according to specific risk profiles. This approach significantly enhances the balance between economic efficiency and structural safety based on the evolution of different target reliability indexes. The study reveals that existing design equations for SFRC beams generally lean towards conservatism. This study found that formulas derived from machine learning exhibited superior prediction ability compared to traditional theoretically derived regression formulas. When compared the proposed machine learning formulas, the Tarawneh's formula demonstrated better prediction ability than the Kara's formula when applied to larger datasets. However, high predictive power does not necessarily equate to high reliability. Machine learning formulas prioritise predictive accuracy, often at the expense of insufficient redundancy for ensuring safety. Overall, it introduces Kara's formula for NSFRC beams, which stands out with its superior predictive performance, offering an optimal balance of safety and cost-efficiency. Ashour's formula is also identified as a more effective and safer option for HSFRC beams. Complementing these findings, the extensive sensitivity analysis of the collected data not only confirms the robustness of the conclusions but also prepares for the integration of broader datasets in the future.

Investigating the synergistic performance of masonry buildings with steel slabs in structural Design: A case of historical Mahmut Nedim Kurkcuoglu mansion in Turkey

The main aim of this study is to explore the collaborative performance of masonry structures incorporating steel slabs in their structural design. The focal point of the investigation is the historically significant Mahmut Nedim Kurkcuoglu Mansion located in Urfa, Turkey, renowned for its cultural heritage. The research methodology encompasses a comprehensive analysis of the mansion’s structural design, utilizing both numerical modeling and experimental testing. The findings of the study reveal that integrating steel beams into slabs within the structural design of masonry buildings can exert a significant influence on overall structural performance. This integration contributes to the heightened safety and durability of the structure. The results underscore the importance of accounting for the historical and cultural value of heritage buildings when implementing structural modifications. In terms of methodology, the research adds value to the field of structural engineering by offering insights into the incorporation of steel beams in the design of masonry structures. Moreover, it emphasizes the significance of preserving cultural heritage sites through sustainable and innovative engineering solutions. The study not only enriches the understanding of structural engineering but also holds practical implications for professionals such as architects, engineers, and preservationists engaged in the design and maintenance of heritage buildings. In conclusion, this research significantly contributes to the realms of both structural engineering and heritage preservation. Its implications extend to practical applications and can serve as a foundational platform for further exploration and investigation in this interdisciplinary domain.

Machine Learning and Traditional Approaches in Shear Reliability of Steel Fiber Reinforced Concrete Beams

In the field of structural engineering, the exploration of steel fibre reinforced concrete (SFRC) beams has recently intensified, particularly due to their improved tension and shear performance of structure. This study pioneers a novel reliability analysis of shear capacity predictions for SFRC beams, distinctively classifying the datasets into high-strength (HSFRC) and normal-strength (NSFRC) categories. A comprehensive database of 142 HSFRC and 265 NSFRC beams serves as the foundation for this analysis, which critically examines the standard Gaussian distribution in shear design models and proposes the Lognormal and Weibull distributions as more precise alternatives. Employing advanced First-order (FORM) and Second-order Reliability Methods (SORM), the study covers a broad spectrum of load conditions, including dead, live, snow, wind, and seismic loads, to evaluate various empirical, semi-empirical and machine learning proposed shear capacity prediction formulas. One of the key innovations of this research is the development of differentiated resistance coefficients for various risk levels in the reliability analysis, allowing future structural designers to tailor their designs according to specific risk profiles. This approach significantly enhances the balance between economic efficiency and structural safety based on the evolution of different target reliability indexes. The study reveals that existing design equations for SFRC beams generally lean towards conservatism. This study found that formulas derived from machine learning exhibited superior prediction ability compared to traditional theoretically derived regression formulas. When compared the proposed machine learning formulas, the Tarawneh's formula demonstrated better prediction ability than the Kara's formula when applied to larger datasets. However, high predictive power does not necessarily equate to high reliability. Machine learning formulas prioritize predictive accuracy, often at the expense of insufficient redundancy for ensuring safety. Overall, it introduces Kara's formula for NSFRC beams, which stands out with its superior predictive performance, offering an optimal balance of safety and cost-efficiency. Ashour's formula is also identified as a more effective and safer option for HSFRC beams. Complementing these findings, the extensive sensitivity analysis of the collected data not only confirms the robustness of the conclusions but also prepares for the integration of broader datasets in the future.

Investigating the synergistic performance of masonry buildings with steel slabs in structural Design: A case of historical Mahmut Nedim Kurkcuoglu mansion in Turkey

The main aim of this study is to explore the collaborative performance of masonry structures incorporating steel slabs in their structural design. The focal point of the investigation is the historically significant Mahmut Nedim Kurkcuoglu Mansion located in Urfa, Turkey, renowned for its cultural heritage. The research methodology encompasses a comprehensive analysis of the mansion’s structural design, utilizing both numerical modeling and experimental testing. The findings of the study reveal that integrating steel beams into slabs within the structural design of masonry buildings can exert a significant influence on overall structural performance. This integration contributes to the heightened safety and durability of the structure. The results underscore the importance of accounting for the historical and cultural value of heritage buildings when implementing structural modifications. In terms of methodology, the research adds value to the field of structural engineering by offering insights into the incorporation of steel beams in the design of masonry structures. Moreover, it emphasizes the significance of preserving cultural heritage sites through sustainable and innovative engineering solutions. The study not only enriches the understanding of structural engineering but also holds practical implications for professionals such as architects, engineers, and preservationists engaged in the design and maintenance of heritage buildings. In conclusion, this research significantly contributes to the realms of both structural engineering and heritage preservation. Its implications extend to practical applications and can serve as a foundational platform for further exploration and investigation in this interdisciplinary domain.

The Current State of Gender-based Diversity within the Field of Canadian Professional Structural Timber Engineering

There is currently minimal data that may be used for retention and recruitment strategies for timber professional engineering in Canada. A comprehensive survey performed in 2022 to address this lack of data is discussed. The survey was generated and analyzed by York University (Canada) and distributed through the Canadian Wood Council’s membership directory. This survey explored participants’ motivations for working on timber-based structural projects and the existing state of EDI training within the workspace. Findings from the context of analysis illustrated that retention in the work force appears not to be affected by gender, with participants overwhelmingly indicating their plan is to continue to work in the timber structural engineering sector. Gender specific findings suggest that most men who practice timber structural design have had more recorded work experience, whereas women were more likely to obtain a PhD when pursuing graduate school.

A Conceptual Study of Wind Load With Different Types of Lateral Load Resisting Methods in 15-Storey Structure

Abstract: In order to strengthen rigidity and control the base deflection of a high-rise structure, stabilizers and trusses are typically used. A stabilizing layer on the building's roof efficiently fortifies the structure. Lateral stiffness is increased by each succeeding stabilizer layer, albeit less so than by the preceding stabilizer layer. The essential components in multi-storey buildings that can withstand shear forces are the shear walls. More efficient shear walls are required as building code requirements rise and tall structures and skyscrapers get taller. The ductility, energy dissipation capacity, and deformation capacity of conventional reinforced concrete shear walls are limited. In the field of structural engineering, a damper in a building is a device intended to lessen the impact of dynamic forces like seismic vibrations or oscillations caused by wind. These devices are necessary in tall or flexible constructions where excessive movement may cause structural damage or cause discomfort for the residents. In this research, we prepare four different cases of 15-storey structure. All modeling work performed in ETABS 2016. Material properties use concrete grade M-40 and steel grade Fe375 and Fe250. One case has shear walls on corners, second case have FVD damper on corners and last case have outrigger on corners. For design wind speed data use code IS: 875-(2015).

Enabling electrical response through piezoelectric particle integration in AA2017-T451 aluminium parts using FSP technology

In the field of structural engineering, the integration of smart materials and structural health monitoring (SHM) has given rise to self-sensing materials (SSM), leading to a paradigm shift in SHM. This paper focuses on the interplay between self-sensing capabilities and the piezoelectric properties of lead zirconate titanate (PZT) and barium titanate (BT) in aluminium components. Leveraging Friction Stir Processing (FSP), the study explores the synthesis and performance of SSMs with embedded piezoelectric particles, potentially transforming structural engineering. The paper highlights FSP as a key methodology for incorporating piezoelectric particles into structural materials, showcasing its potential in developing SSMs with enhanced functionalities. A specific focus is placed on integrating PZT and BT particles into AA2017-T451 aluminium parts using FSP, with metallographic assessments and mechanical property evaluations conducted to analyse particle distribution and concentration. This study shows how BT and PZT particles are incorporated into AA2017-T451 aluminium to create a SSM that responds to external stimuli. Under cyclic loading, the SSMs exhibit a linear load-electrical response correlation, with sensibility increasing at lower frequencies. Metallographic analysis shows homogeneous particle distribution, while PZT induces increased brittleness and brittle fractures. Yield strength remains relatively stable, but ultimate strength decreases post-FSP. Hardness variations indicate weaker bonding with PZT particles. Eddy’scurrent testing aligns with hardness profiles, and sensorial characterization reveals a non-linear frequency-sensibility relationship, showcasing the SSMs’ suitability for low-frequency applications, particularly with PZT embedment.

Flexural wave propagation characteristics of advanced nanocomposites reinforced sandwich structure via 3D-flexibility-deep neural networks approaches

The propagation of flexural waves in advanced nanocomposites reinforced sandwich structures is a topic of paramount importance in the field of structural engineering and materials science. This study investigates the dynamic behavior of such structures, focusing on the wave propagation characteristics along with longitude and lateral directions and their influence on structural performance. Advanced nanocomposites, with their unique material properties and enhanced mechanical properties, offer promising opportunities for the development of lightweight and high-strength sandwich structures. For this important aim, first, a mathematical modeling using three-dimensional flexibility theory and an analytical solution procedure, the results are collected. After that using the datasets of the mathematical modeling section, the hybrid deep neural networks are trained, tested, and validated. Through comprehensive analytical simulations and machine network validations, this research elucidates the complex interplay between material composition, structural geometry, and wave propagation phenomena. Key parameters, such as wave velocity, dispersion, and geometry conditions are analyzed to gain insights into the dynamic response of the sandwich structure. The findings contribute to a deeper understanding of the underlying physics governing flexural wave propagation in nanocomposite-reinforced sandwich structures, thereby facilitating the design and optimization of lightweight and resilient structural systems for diverse engineering applications.

Efficiency enhancement techniques in finite element analysis: navigating complexity for agile design exploration

PurposeThis paper aims to comprehensively explore techniques for reducing solution time in finite element analysis (FEA), addressing the critical need for expediting computations to facilitate agile design exploration within project timelines.Design/methodology/approachDrawing from a wide array of literature sources, this paper synthesizes and analyzes various methodologies used to enhance the efficiency of FEA. Techniques are scrutinized in terms of their applicability, effectiveness and potential limitations.FindingsThe review signifies application of linear assumptions across multiple facets of analysis and delves into matrix order reduction strategies, geometry simplification, symmetry exploitation, submodeling and mesh attribute control. It reveals how these techniques can effectively reduce computational burdens while maintaining acceptable levels of accuracy.Research limitations/implicationsWhile this review provides a comprehensive overview of existing efficiency enhancement techniques in FEA, it acknowledges inherent limitations of any synthesis-based study. Future research should focus on refining these methodologies.Practical implicationsThe insights provided in this paper offer practical guidance for structural engineers and researchers seeking to optimize FEA workflows. By implementing these techniques, practitioners can expedite solution times and enhance their ability to explore design alternatives efficiently ultimately leading to cost savings and more robust structures.Originality/valueThis review contributes to the existing literature by offering a comprehensive synthesis of efficiency enhancement techniques in FEA. By highlighting the originality and value of each discussed methodology, this paper provides a roadmap for future research and practical implementation in the field of structural engineering.

Systematic Mapping of Circular Economy in Structural Engineering

Facing increasing sustainability demands, the construction industry is at a turning point where the implementation of circular economy (CE) strategies plays an essential role in driving the necessary transformation aimed at reducing the environmental impact. To facilitate this shift, structural engineering must effectively integrate circular principles into building design. With the exponential growth of research articles within this field, it is crucial to map the evolution of the research area. The objective of this study is to detail the trends with, challenges to, and research contributions, integration, and material applications of CE principles within structural engineering. Consequently, a systematic mapping of the CE within the field of structural engineering has been conducted in this study. Initially, the mapping process began with the identification of relevant keywords, followed by searches across four databases. Each resulting article was carefully screened against content criteria, culminating in 91 publications that were thoroughly evaluated. The publications were then categorized and analyzed based on attributes such as research type, circular design, materials, and applications. The results are presented through informative figures and tables. The analysis of the research indicates a predominant focus on technical solutions for structural systems, with demountable connections designed to facilitate the future reuse of materials representing more than half of the literature reviewed. A significant portion of the literature also addresses designing from reclaimed elements; these articles reflect a transformation in engineering approaches, incorporating computational design and innovative methodologies. The focus on steel as a structural material is prominent in the reviewed literature. However, there is an increasing focus on timber, which signals a definitive shift toward sustainable structural systems. Recurring challenges identified in the literature regarding the transition to a circular economy (CE) in the construction industry include the need for industry-wide adoption, precise standardization, the integration of digital tools, and the overcoming of related obstacles in policy and market acceptances. Furthermore, the literature demonstrates a significant research gap: the absence of a comprehensive digital framework enabling an effective digital circular structural design workflow.

Bamboo structures: Innovative methods and applications for structural health monitoring and dissemination

Bamboo is gaining more and more attention in the field of building materials thanks to its unique characteristics, such as resistance, lightness, sustainability, and flexibility. However, the widespread adoption of bamboo as a building material presents some challenges, especially in relation to the durability of the material. In fact, being a natural material, it requires protection from atmospheric agents, such as rain and sun, as well as from attacks by insects and animals. Ensuring the longevity of bamboo involves implementing tailored treatments, precise precautions, and regular structural inspections to detect any potential degradation over time. In this context, the article focused on the monitoring of a structure made of bamboo and on the testing of an innovative system to identify the structural critical issues present, in the context of a FISR project which envisaged a cultural exchange between China and Italy. Starting from the survey of the structure carried out with a drone, a 3D model was then built using a commercial software. For the monitoring phase of the structure, an innovative system was used that exploits the YOLO v5s6 algorithm to identify structural critical issues, supported by a Virtual/Augmented/Mixed reality app developed by the authors. This app is useful not only during the monitoring phase, but also in the subsequent phase of disseminating information relating to the potential of the use of bamboo in the field of structural engineering. The tested and proposed methodologies have proven to be particularly useful and high-performance, especially regarding the monitoring of bamboo structures, in relation to the resolution of problems linked to their possible fragility and limitations in certain application areas. The contribution provided in this paper by Geomatics methodologies is evident to better highlight the potential of this eco-friendly material for its desirable ever-increasing use in the construction sector by becoming aware of its potential.

Fully automated operational modal identification based on scale-space peak picking algorithm and power spectral density estimation

Modal analysis is a fundamental and essential research direction in the field of structural engineering. The ultimate goal is to determine the modal parameters of the structures. However, the existing modal analysis algorithms often require a large number of parameter adjustments and manual intervention during operation, which cannot be fully automated. In order to realize the automatic identification of modal parameters, the automatic operational modal identification method (AOMI) is proposed based on the interpolated power spectral density estimation (IPSE). To achieve more precise spectrum analysis in the low-frequency band, IPSE is employed to perform Fourier transform on the original frequency domain segment with optimized frequency resolution. This enhances the sharpness of the obtained spectrum in the low-frequency range, making peak frequencies more discernible. Subsequently, the scale-space peak picking algorithm is used to automatically obtain the peak of the power spectral density (PSD), thus enabling the automatic identification of the natural frequency. Finally, the frequency domain decomposition method (FDD) is used to identify modal parameters based on the natural frequencies. The effectiveness of AOMI is verified through the modal identification of the old steel truss bridge and the three layer framework. Under the environmental excitation, the frequencies identified by the IPSE method is close to that of FDD, Bayesian fast fourier transform (FFT) and covariance driven stochastic subspace identification (SSI-COV). Furthermore, the PSD obtained through IPSE has sharper peak than that of FDD and the Welch’s method. The damping ratio identification accuracy and modal assurance criterion (MAC) are satisfactory in AOMI, which can improve the automatic performance.

Prediction of Torque Capacity in Circular Concrete-Filled Double-Skin Tubular Members under Pure Torsion via Machine Learning and Shapley Additive Explanations Interpretation

The prediction of torque capacity in circular Concrete-Filled Double-Skin Tubular (CFDST) members under pure torsion is considered vital for structural design and analysis. In this study, torque capacity is predicted using machine learning (ML) algorithms, such as Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Random Forest (RF), and Decision Tree (DT), which are employed. The interpretation of the results is conducted using Shapley Additive Explanations (SHAPs). The performance of these ML models is evaluated against two traditional analytical formulas that have been proposed and are available in the literature. Through comprehensive analysis, it is shown that superior predictive capabilities are possessed by the CatBoost and XGBoost models, characterized by high R2 values and minimal mean errors. Additionally, insights into the influence of input features are provided by SHAP interpretation, with an emphasis on key parameters such as concrete compressive strength and steel tube dimensions. The gap between empirical models and ML techniques is bridged by this study, offering engineers a more accurate and efficient tool for CFDST structural design. Significant implications for optimizing CFDST column designs and advancing structural engineering practices are presented by these findings. Directions for future research include the further refinement of ML models and the integration of probabilistic analyses for enhanced structural resilience. Overall, the transformative potential of ML and SHAP interpretation in advancing the field of structural engineering is showcased by this study.

Static Load Analysis of Inverted Umbrella Structure for Rain Water Harvesting

The study presents a comprehensive structural integrity assessment of inverted umbrella rainwater harvesting systems through an in-depth analysis conducted using the STAAD-PRO software. The inverted umbrella design, increasingly employed for rainwater harvesting in various settings, necessitates a rigorous examination of its structural performance to ensure reliability and safety. The study employs STAAD-PRO environment to evaluate the load-bearing capacities, stress distributions, and deformations exhibited by the inverted umbrella structures. This study contributes to the broader field of structural engineering by establishing a methodology for utilizing STAAD-PRO in the evaluation of unconventional structures such as inverted umbrella rainwater harvesting systems. Different kinds of live and dead loads acting on the structure was estimated and an emphasis is placed on understanding the dynamic interactions between the system components and the forces imposed by environmental factors, including live and dead loads and seismic activities. Wind load. The results of the STAAD-PRO analysis offer valuable insights into the structural behavior of the inverted umbrella rainwater harvesting systems under diverse loading conditions. By assessing critical points such as nodes, connections, and supporting elements, the study aims to identify potential vulnerabilities and proposes design modifications to enhance overall structural robustness. Total dead load acting on the structure was found as 2.88kN. The maximum and minimum force experienced by the structure in X, Y and Z direction was found as (2.33kN and 1.37N), (65.12N and 1.37N) and (261.15N and 6.02N) respectively. The structure was also found stable against bending, shear and tensile strength with utility ratio of less than 1. Ultimately, this investigation strives to foster the adoption of reliable and resilient inverted umbrella rainwater harvesting systems, advancing the integration of innovative engineering solutions into sustainable water resource practices.

Application of Artificial Neural Network for Prediction of Story Drift in High-Rise Buildings

The difficulties arising from growing population expansion and extensive urbanization are driving up the demand for high-rise structures. Comprehending the symmetry and composition of structural components correspondingly is becoming more and more important in light of current developments in computer technology, novel materials, and unique structures. The structural responses of multistory buildings predicted by Artificial Neural Networks (ANN) briefly outlined in a systematic manner. This review enhances the application of ANN for the prediction of story drift in multistory buildings with greater level accuracy. A multistory structure might fall because of the intense movement of the ground, resulting in fatalities and economic loss. As a result, it is imperative that the multistory structure be appropriately constructed to minimize the earthquake hazards. Based on a literature analysis, it can be concluded that artificial neural networks are widely used in simulating and predicting story drift in high-rise building systems. The objective of the current investigation is to present a comprehensive overview of the many approaches and uses of artificial neural network modeling research in the domain of structural engineering. Accordingly, the articles were categorized in this way based on author, year, algorithm used, software application and description of findings and conclusions. Here, the most influential parameter (input vector) is also rigorously reviewed. This information may be taken into account when creating a neural network model that operates more effectively. The review's conclusions might prove beneficial for structural and/or civil engineering problems. The approaches described here assist the structural practitioner in understanding the limitations and advantages of ANN in comparison to other traditional mathematical modelling. This review contributes a body of knowledge for the ANN modelling approach to forecast the story drift evaluation within the field of structural engineering.

Resistance Factor Spectra for the Ultimate Limit State of the National Building Code of Canada

Over the years, structural engineering codes and specifications in Canada and elsewhere have moved from an allowable stress design (ASD) approach to a load and resistance factor design (LRFD) philosophy. LRFD methodology takes better account of the inherent variability in both loading and resistance by providing different factors of safety for loads of distinct natures with regard to their probability of overload, frequency of occurrences and changes in point of application. The method also results in safer structures because it considers the behavior at collapse. While resistance factors for traditional construction materials based on LRFD in the National Building Code (NBC) of Canada are available, they cannot be used for non-conventional ones. This is because the resistance of such materials due to various load effects has unique bias factors (λR) and coefficients of variation (VR), which greatly impact their reliability index (β). In this study, relationships between the resistance factor ϕ and critical load effects from the NBC load combinations at ultimate limit states are developed for a wide range of resistance bias factors and coefficients of variation. The relationships are presented in the form of charts that are useful for researchers and code-writing professionals who have expertise in the various fields of structural engineering but lack proper background in reliability theory. The developed spectra showed that for the same ϕ, β increases with an increase in the live-to-dead load (L/D) ratio until it reaches 1; thereafter, the shape of the relationship will depend on the statistics of the resistance as well as on the magnitude of ϕ. For a small ϕ and VR, β will keep increasing with an increase in the L/D ratio from 1 until 3, albeit at a lesser rate. For L/D > 3, the relationship between the critical β and applied load is just about constant. This finding is also true for load combinations involving snow and wind. Application of the method is illustrated by a practical example involving the shear strength of a corrugated web steel beam.

An Analytical Solution for the Bending of Anisotropic Rectangular Thin Plates with Elastic Rotation Supports

The mechanical analysis of thin-plate structures is a major challenge in the field of structural engineering, especially when they have nonclassical boundary conditions, such as those encountered in cement concrete road slabs connected by transfer bars. Conventional analytical solutions are usually limited to classical boundary conditions—clamped support, simple support, and free edges—and cannot adequately describe many engineering scenarios. In this study, an analytical solution to the bending problem of an anisotropic thin plate subjected to a pair of edges with free opposing elastic rotational constraints is found using a two-dimensional augmented Fourier series solution method. In the derivation process, the thin-plate problem can be transformed into a problem of solving a system of linear algebraic equations by applying Stoke’s transform method, which greatly reduces the mathematical difficulty of solving the problem. Complex boundary conditions can be optimally handled without the need for large computational resources. The paper addresses the exact analytical solutions for bending problems with multiple combinations of boundary conditions, such as contralateral free–contralateral simple support (SFSF), contralateral free–contralateral solid support–simple support (CFSF), and contralateral free–contralateral clamped support (CFCF). These solutions are realized by employing the Stoke transformation and adjusting the spring parameters in the analyzed solutions. The results of this method are also compared with the finite element method and analytical solutions from the literature, and good agreement is obtained, demonstrating the effectiveness of the method. The significance of the study findings lies in the simplification of complex nonclassical boundary condition problems using a simple and reliable analytical method applicable to a wide range of engineering thin-plate structures.

Lateral torsional capacity of steel beams in different loading conditions by neural network

Lateral torsional buckling (LTB) is a common mode of failure in steel structures due to instability. However, current standard recommendations have limitations in accurately determining the ultimate capacity of members subjected to LTB. To address this issue, an in-depth parametric study using finite-element analysis (FEA) was conducted to investigate the effects of major parameters, including various types of loading, on the strength of steel I-beams. Additionally, the artificial neural network (ANN) technique was used to find a reliable procedure for assessing the LTB strength of steel I-beams using a generated database. To demonstrate the efficacy of the developed formulation, it was compared against predictions using existing equations. The presented formula demonstrated strong accuracy, making it an effective tool for engineers designing I-beams to resist LTB. This research makes significant contributions to the structural engineering field and has important implications for the creation and evaluation of steel structures.