Structural Engineering Practice Research Articles

Procedimentos para modelagem BIM de lajes nervuradas de concreto armado em edificações

It is impossible not to admit that commercial computational tools for structural analysis and detailing have become indispensable in the various applications of Structural Engineering nowadays. The importance of these tools equipped with graphical interfaces based on the BIM (Building Information Modeling) environment is mainly due to the flexibility and ease in making common corrections and changes during the elaboration stage, as well as the proposition of different structural conceptions, not always innovative, but almost always bold, as in the case of reinforced concrete ribbed slabs. The use of ribbed slabs in reinforced concrete is an old practice in Structural Engineering, which has encouraged the adaptation of commercial computational tools, both in the development of structurally consistent mathematical formulations, as well as in the development of specific databases, equipped with information from mold manufacturers and materials intended for the execution of this type of slabs. It is highly efficient in building structures in which there are numerous problems of interference of structural elements with elements of installations, such as large-diameter pipes for wastewater, sewage, gas, fire, and even air conditioning ducts, exhaust and architectural elements. This efficiency is mainly due to the fact that large beams are not considered in their supports, making the lower face of the slab practically without obstacles to the building pipes, and is also due to the low average thickness of concrete used in the pavement compared to conventional rectangular supported slabs and flat mushroom slabs. However, it is not always possible to obtain an optimal final result, even with all the adaptations previously described, mainly due to executive and computational modeling problems. This work intends to present a possible structural modeling methodology, which provides results within the limits of the feasibility of the slab, both in the construction aspect of the computational structural model, as well as the possibility of using materials from manufacturers and labor, using a commercial computational tool for structural analysis and detailing.

A physics-informed deep learning approach for combined cycle fatigue life prediction

The prediction of the combined high and low cycle fatigue (CCF) life of welded components was valuable for the reliability of practical engineering structures. In this study, a physics-informed Grey-deep convolutional neural networks (DCNN) model was constructed and the model parameters was determined based on the fatigue behavior characteristics of CCF, which was proved perform well in the CCF life prediction of welded components. The predicted results indicated that the proposed model can realize reliable CCF life prediction with good accuracy (MAE = 0.49) and stability (RMSE = 0.44). Further, the proposed model also exhibited good prediction performance under different loading and geometry conditions, indicating the good universality of the proposed model. And the good universality represented that the proposed model had the potential to be applied in more engineering fields.

Predicting ultimate strength and axial strain of circular concrete columns having AFRP wraps and tube-encased confinement

The current study performs an in-depth analysis into the reinforcement of circular concrete columns (CCC) using aramid fiber-reinforced polymers (AFRP), focusing on two distinct techniques: wet-layup wrapping and tube-encasement with manufactured shells. The need for improved forecasting models for AFRP-wrapped concrete structures arises from observed inconsistencies and limited accuracy in existing models, which often fail to fully account for key parameters such as confining stiffness and composite strain. Addressing this gap, our study begins with a comprehensive review of prior experimental research, emphasizing the impact of various parameters on the performance of AFRP-wrapped CCC under monotonic axial compressive loading. To build a robust foundation for this investigation, we compiled an extensive database of experimental outcomes from existing literature, encompassing 180 concrete cylinders wrapped by AFRP. Our analysis meticulously evaluates nine different models designed to predict the ultimate compressive strength and axial strain of circular columns reinforced with AFRP. These models are scrutinized for their accuracy, revealing notable discrepancies, particularly when key parameters are overlooked. In response, we introduce new equations that offer more precise predictions compared to previously proposed models. These novel equations are derived from an extensive statistical analysis, ensuring their reliability and robustness. Prominently, our proposed model for compressive strength demonstrated superior performance with a coefficient of determination (R2 = 0.87), a root means square error (RMSE) of 0.29, and a mean absolute error (MAE) of 22.91. Similarly, the proposed strain model showed a high level of forecasting accuracy, with a coefficient of determination (R2 = 0.82), a root mean square error (RMSE) of 2.79, and a mean absolute error (MAE) of 208.69. To validate our findings, we employed the Taylor diagram and probability density function, which provide a detailed comparison of the forecasting accuracy of our proposed models against existing ones. The outcomes of this study not only enhance the understanding of AFRP-wrapped concrete columns but also offer valuable insights for the development of more effective reinforcement techniques. Our findings are expected to contribute significantly to the advancement of structural engineering practices, particularly in the application of AFRP for strengthening concrete structures.

Fatigue Crack Monitoring Method Based on the Lamb Wave Damage Index.

For practical engineering structures, fatigue is one of the main factors affecting their safety and durability. Under long-term service conditions, the minor damage will be affected by fatigue loading and expand to macroscopic cracks, affecting the structure's service performance. Based on the sensitivity of Lamb waves to minor and initial damage, a damage monitoring method for fatigue crack propagation is proposed. By carrying out fatigue crack propagation tests under constant amplitude loading, the Paris equation of 316L steel and damage signals at different crack growth stages were obtained. Combined with damage monitoring tests and finite element analysis, the relationship between the phase damage index (PDI), amplitude damage index (ADI), signal correlation coefficient, and fatigue crack propagation length was studied. Compared with PDI and ADI, the signal correlation coefficient is more sensitive to crack initiation, which can be selected as the damage monitoring index in the initial stage of crack growth. With the increase of fatigue crack propagation length, the peak time of the direct wave signal gradually moves backward, which shows an obvious phase change. In the whole fatigue crack growth stage, PDI and crack length show a monotonically changing trend. By using the stress intensity factor as the conversion parameter, a prediction model of the fatigue crack propagation rate based on PDI was established. Compared to the fatigue crack propagation rate measured by experiments, the relative error of the predicted results is 10%, which verifies the accuracy of the proposed damage monitoring method.

Numerical simulation of the vortex-induced vibrations of a 4:1 rectangular cylinder subjected to yaw uniform currents

The practical engineering structures are not always perpendicular to the wind but often in skew positions. This yawed configuration may lead to more complex three-dimensional flow characteristics and different vortex-induced vibration (VIV) responses. However, the related study is very inadequate. Therefore, comprehensive numerical investigations of the VIV characteristics of a 4:1 rectangular cylinder have been conducted using the three-dimensional (3D) Large-Eddy Simulation (LES) at five different yaw angles (β = 0°, 5°, 10°, 20°, 30°). An innovative method for creating 3D meshes was introduced, demonstrating a good agreement with the experimental results while significantly reducing the mesh number, and consequently the computation time. The VIV response, pressure distribution, aerodynamic force, flow field, and energy input/output were analyzed for various yaw angles. The results demonstrate that the periodic boundary conditions on the spanwise domain side is more reliable than those of the wall boundary conditions. The velocity component parallel to the cylinder axis can be ignored in the analysis of elastically-mounted rectangular rigid cylinders for β up to 30°. If only considering the perpendicular axis velocity component, the amplitudes and lock-in regions for β = 5°, 10°, 20°, and 30° are highly consistent with those of β = 0°, which have effectively validated the accuracy of Independence Principle in evaluating the VIV of a 4:1 rectangular cylinder.

End-rotation-driven kinetic elastica structures: Concept and morphology design

Euler’s elastica, a family of planar curves describing the equilibrium shapes of a thin elastic beam subjected to end forces and moments, demonstrates both geometric versatility and mechanical functionality, leading to its diverse applications across multiple disciplines. Despite its roots in ancient structural engineering practices, the kinetic potential of Euler’s elastica remains largely unexplored in this domain. To address this gap, this paper proposes the concept of end-rotation-driven kinetic elastica (ERKE) structures. This novel kinetic form consists of a sequence of discrete elastica arches transformed from initially straight members by elastic bending. The kinetic behavior arises from manipulating the end rotations of each elastica arch, prompting a continuous flow of motions resulting from adaptations in the arch shape. This adaptive quality opens up limitless possibilities for creating visually captivating dynamic patterns in the overall structure by coordinating the motions of individual arches. As the inaugural exploration of ERKE structures, this paper centers around their conceptualization, with an emphasis on morphology design, which refers to the creation of desirable dynamic patterns. To this end, the paper provides an exposition of the general design procedure and criteria, along with demonstrative examples. The paper also envisions potential application scenarios and identifies research needs, paving the way for future investigations.

Solutions of crack constraint parameter in 3D models −− II: Through T-stress

To more accurately analyze the crack front fields, especially under the low constraint conditions which are more common in practical engineering structures, two-parameter approaches (e.g. J-A(A2), J-Q, etc.) are proposed. Currently, no theoretical solutions of the constraint parameters (e.g. A(A2), Q, etc.) in two-parameter approaches are available. Meanwhile, numerical methods for constraint parameter determination are not practical because of significant resource and time consumption, especially for 3D solid models or engineering structures. Therefore, theory and formulae for the estimation of constraint parameter A in 3D solid models are developed in the current work based on T-stress, so as to make it possible to apply two-parameter approaches in practical engineering structures under normal working condition. Two actual structures are used to validate the engineering application of the developed estimate method and formulae.

Solutions of crack constraint parameter in 3D models − I: Through curve shape similarity

Two-parameter approaches of elastic plastic fracture mechanics, e.g., J-A(A2), J-Q, can be used to obtain more accurate fracture analysis results, especially under low crack constraint condition which commonly observed in practical engineering structures. While, theoretical solutions of the constraint parameters in the two-parameter approaches are not available, and substantial consumption of resources and time makes it inappropriate to numerically determine constraint parameters (e.g., A(A2), Q), especially in 3D solid models. In this work, based on the curve shape similarity, the estimation theory and formulae for constraint parameter A in 3D solid models are developed through theoretical and numerical analyses to obtain parameter A solutions quickly and efficiently, so as to enable the application of two-parameter approaches in practical engineering structures. The proposed estimation formulae here are used to predict the constraint parameter A in two actual engineering structures.

Unsupervised quantitative structural damage identification method based on BiLSTM networks and probability distribution model

Current unsupervised structural damage identification methods mainly focus on unsupervised damage detection, i.e., detecting whether damage occurs, but rarely involve damage localization and quantification in practical civil engineering structures. Thus, based on bidirectional long short-term memory (BiLSTM) networks and a probability distribution model, i.e., generalized extreme value distribution (GEVD), this paper proposes an unsupervised structural damage detection, localization, and quantification method. The response correlations model among sensors under healthy conditions was built by employing the BiLSTM networks for reconstructing response data. Then, reconstruction error between reconstructed and true data is used as damage-sensitive features and thus is applied to detect and localized damage. In addition, a stable damage quantification result can be obtained based on the probability distribution model of damage-sensitive features. The proposed method is validated by using the data obtained from a numerical steel beam model and a practical long-span cable-stayed bridge. The research results demonstrate that the proposed method can effectively detect, localize, and quantify damage in an unsupervised manner, even with a limited number of sensors and low-order modal information in acceleration data. The proposed method possesses the capability to autonomously furnish alerts regarding structural damage location and severity under Environmental and Operational Conditions.

Reducing embodied carbon with material optimization in structural engineering practice: Perceived barriers and opportunities

Structural engineers play an essential role in reducing embodied carbon emissions in buildings, which contribute significantly to global carbon emissions. In this work, 39 structural engineers primarily from the Northeastern US are surveyed on how embodied carbon is reduced in practice, particularly through computational tools that facilitate material efficiency (e.g. parametric design models and structural optimization tools). Leading barriers to embodied carbon reductions are identified as: (i) lack of awareness of how to quantify embodied carbon or reduce it, (ii) perception of increased costs, and (iii) lack of power in relation to other project stakeholders. The use of computational tools is found to be primarily inhibited by increased design time and costs. These results highlight opportunities to overcome these barriers, such as increasing awareness of strategies to reduce embodied carbon and targeting time and cost (or perceived time and cost) in computational design workflows for structural engineers.

COMPARATIVE STUDY OF STEEL CONNECTION USING US AND IS CODE ON TEKLA STRUCTURE

This paper presents a comprehensive comparative study of steel connections implemented in Tekla Structures software, employing both the US and IS (Indian Standards) codes. The investigation focuses on analyzing the differences in design criteria, detailing requirements, and fabrication practices between the two codes. Specifically, the study explores variations in bolt sizing, beam and column dimensions, as well as detailing specifications for bolted connections. Through detailed examination and comparison, significant disparities are identified, highlighting the importance of adhering to specific standards to ensure structural integrity and compatibility in construction projects. Additionally, the study addresses the implications of these differences on material estimation, cost evaluation, and overall project outcomes. By providing insights into the utilization of US and IS codes within Tekla Structures, this research contributes to enhancing the understanding and implementation of international standards in structural engineering practices. Keyword- Tekla Structure, Steel Structure, Us and Is Codes

Efficient Sizing and Layout Optimization of Truss Benchmark Structures Using ISRES Algorithm

This paper presents a comprehensive investigation into the application of the Improved Stochastic Ranking Evolution Strategy (ISRES) algorithm for the sizing and layout optimization of truss benchmark structures. Truss structures play a crucial role in engineering and architecture, and optimizing their designs can lead to more efficient and cost-effective solutions. The ISRES algorithm, known for its effectiveness in multi-objective optimization, is adapted for the single-objective optimization of truss designs with multiple design constraints. This study encompasses a wide range of truss benchmark structures, including 10-bar, 15-bar, 18-bar, 25-bar, and 72-bar configurations, each subjected to distinct loading conditions and stress constraints. The objective is to minimize the truss weight while ensuring stress and displacement limits are met. Through extensive experimentation, the ISRES algorithm demonstrates its ability to efficiently explore the solution space and converge to optimal solutions for each truss benchmark structure. The algorithm effectively handles the complexity of the problems, which involve numerous design variables, stress constraints, and nodal displacement limits. A comparative analysis is conducted to assess the performance of the ISRES algorithm against other state-of-the-art optimization methods reported in the literature. The comparison evaluates the quality of the solutions and the computational efficiency of each method. Furthermore, the optimized truss designs are subjected to finite element analysis to validate their structural integrity and stability. The verification process confirms that the designs adhere to the imposed constraints, ensuring the safety and reliability of the final truss configurations. The results of this study demonstrate the efficacy of the ISRES algorithm in providing practical and reliable solutions for the sizing and layout optimization of truss benchmark structures. The algorithm’s competitive performance and robustness make it a valuable tool for structural engineers and designers, offering a versatile and powerful approach for complex engineering optimization tasks. Overall, the findings contribute to the advancement of optimization techniques in structural engineering, promoting the development of more efficient and cost-effective truss designs for a wide range of engineering and architectural applications. The study’s insights empower practitioners to make informed decisions in selecting appropriate optimization strategies for complex truss-design scenarios, fostering advancements in structural engineering and sustainable design practices.

Damage Identification of Full-Scale Steel Truss Structure Based on Model Condensation and Mean-Value Normalization Regularization Techniques

Structural health monitoring and damage identification aim to detect the internal damage and evaluate the health conditions of the practical engineering structure, which has been the most popular research field for several decades. The sensitivity-based method incorporated with the regularization techniques is the classical and useful approach, and it can obtain accurate damage detection results. However, with the development of civil engineering structures, this classical method faces two problems: one is it is only applied to simple structures rather than full-scale structures, and second is the iterative calculation efficiency is lower. Therefore, aiming at these drawbacks, the two improvement strategies have been introduced to the original method for its enhancement in the application potential and computational efficiency. The proposed method has been verified based on two examples, i.e., a numerical steel truss with 144 elements and a full-scale experimental steel truss with 160 elements. The results prove that the proposed method has better efficiency and good application potential in the practical full-scale engineering structure.

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.

Development of methods of structural reliability

The growth of structural reliability theory and applications, along with a recognition of its role in guiding the structural engineering profession in addressing some of the most important issues in design of the built environment, represents one of the key engineering achievements during the past five decades. Structural reliability provides a unifying framework for managing uncertainties affecting performance of structures and a quantitative link between the practice of structural engineering and its social consequences. Such links perhaps are most obvious in probability-based codified design and performance evaluation but there are numerous other applications, which are summarized in this special issue. As the field has matured, researchers in reliability have worked with structural engineers to elevate both the practice of structural engineering and the quality of research to levels that otherwise would not have been possible. The Joint Committee on Structural Safety has played a central role in this historic development and it will inspire future opportunities for the reliability community to build upon past successes to improve structural engineering and construction practices. This paper surveys the key theoretical developments and milestones that enable these opportunities.

Experimental and numerical investigation of through-diaphragm in H-shaped steel beam to CFST column connections

This research proposes a cruciform through-diaphragm (CTD) for an H-shaped steel beam to concrete-filled steel tubular (CFST) column and investigates its seismic performance experimentally and numerically. The proposed connection consists of through-plates passing through the aligned slots in the panel zone and end plates directly welded to an H-shaped steel beam. This connection eliminates welding inside the steel tube for installation of the diaphragm, while providing a reliable load path from the steel beam to the CFST column. The experimental program examined the connection hysteretic behaviors, including the moment-rotation response, ductility, initial stiffness, and energy dissipation capacity. The proposed connection shows stable hysteric behavior and good energy dissipation up to a story drift up to 4 % and satisfies the AISC seismic provisions criteria for special moment connection. A finite element (FE) model was established and verified against the experimental results. The effects of concrete infill, steel tube column thickness, axial load ratio, and through-plate thickness on the hysteretic behavior of the proposed connection were investigated through parametric analysis of 24 FE models. This study provides the information on the optimized design parameters that ensures the stable seismic performance of the proposed connection that could be used in structural engineering practice.

Wavelet denoising based on comprehensive index optimization and improved L2 regularization for load identification

Load identification, as an inverse problem, is still one of the challenges in the field of vibration engineering. In this paper, a comprehensive load identification method combining entropy weight Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) noise reduction evaluation algorithm and improved L2 regularization method is proposed to improve the load identification accuracy from the following two perspectives. Firstly, the entropy weight TOPSIS method is used to evaluate the wavelet denoising scheme comprehensively, and the optimal denoising scheme is found to reduce the noise interference in the process of inverse problem solving. Secondly, we introduce a new regularization filter function, and search for the regularization parameters by Generalized Cross-Validation (GCV) criterion and one-dimensional optimization algorithm to improve the ill-posedness of the inverse problem. Finally, the simulation and experimental verification of single-source load identification are carried out with the scaled crankcase model, while the accuracy is compared with the Tikhonov regularization method. The results show that the integrated algorithm proposed in this paper can improve the accuracy of load identification while overcoming the inadequacy of inverse problem. Unlike previous studies, the comprehensive evaluation method of denoising effect introduced in this paper provides a way to judge the denoising effect in practical engineering, and the experimental model is closer to practical engineering structures, which shows potential engineering application value.

The Practice of Forensic Structural Engineering in IABSE's Member Countries

The IABSE Task Group 5.1 on Forensic Structural Engineering aims to examine and to mitigate structural failures by sharing knowledge of technical, human and organizational causes of failure, in addition to methods and techniques in forensic investigation processes. Forensic engineering expertise has yet to be recognized worldwide as a specific domain of civil engineering practice, and this paper amends previously published technical reports in 2012, 2014 and 2015 by former IABSE Working Group 8 following a new survey carried out in 2020–2022. This new survey has been developed with the lessons learnt from the previous survey in 2013–2014, including the validation of the results obtained by eliminating potential subjectivity in the former assessment. This survey also includes further understanding of the institutional and national regulatory framework. The survey was completed by 22 IABSE member countries whose responses were provided by national experts on forensic structural engineering. This paper will allow the reader to familiarize him/herself with the state of the art in forensic structural engineering practice across 22 countries, allowing a national-level diagnosis. The survey will also help to identify the main challenges within each country related to forensic engineering. Furthermore, recommended steps, as suggested by the national experts, for the awareness and/or development of good industry practice within forensic structural engineering are highlighted in this paper. Online supplemental data for this article can be accessed at https://doi.org/10.1080/10168664.2024.2307411.

Data-driven estimates of the strength and failure modes of CFRP-steel bonded joints by implementing the CTGAN method

The bond strength between the CFRP and steel usually dominates the final strengthened effectiveness. However, the CFRP-steel bond strength is affected by various geometric and material properties and exhibits different failure modes, making accurate predictions challenging. This study utilises data-driven machine learning (ML) methods to predict the strength and failure modes of CFRP-steel joints. An experimental dataset consisting of 178 single-lap shear test results was first built, after which the Conditional Tabular Generative Adversarial Networks (CTGAN) method was applied to augment the limited available data. Four broadly used ML algorithms: Support Vector Machines (SVM), K-Nearest Neighbours (KNN), Decision Trees (DT) and Artificial Neural Networks (ANN) were applied. The ANN regression model achieved the best performance in predicting joint strength (Rtest2=0.95), while the SVM classification model achieved the best performance in predicting failure modes (accuracy ≥ 92.3 %). The SHapley Additive exPlanations analysis further revealed that the Young's modulus of the adhesive was most significant to the joint strength, while the tensile strength of the adhesive was most significant to the failure modes. The ultimately constructed ML models and the corresponding analyses presented can benefit practical structural engineering applications and provide insights into the optimal CFRP-steel joint design.

Continuous adsorption of Metanil Yellow and Remazol Black B dyes onto fixed-bed of 3D graphene oxide/chitosan biopolymer

Treatment of dye polluted water using sustainable and efficient approaches is essential to mitigate the detrimental effects of dyes. The present work reports on the adsorption effectiveness of a graphene-based adsorbent configured into a practical engineering structure, namely three-dimensional graphene oxide integrated with chitosan biopolymer (3D-GCB). This unique graphene configuration enabled its application in continuous fixed-bed adsorption of Metanil Yellow (MY) and Remazol Black B (RBB) dyes from polluted water for the first time. The breakthrough attributes of the fixed-bed of 3D-GCB were established in terms of bed depth (3 – 5 cm), influent concentration (100 – 200 mg/L for MY and 100 – 300 mg/L for RBB) and feed flowrate (2 – 4 mL/min). The greatest bed adsorption capacities attained were 157.06 and 247.96 mg/g for MY and RBB, respectively. The column parameters for obtaining these results were 5 cm bed depth, 2 mL/min feed flowrate, and 150 and 200 mg/L influent concentrations respectively for MY and RBB. The fixed-bed performance of the 3D-GCB was well correlated to the Thomas and Yoon-Nelson models, and the corresponding kinetic properties were determined. Furthermore, the higher affinity of the 3D-GCB for RBB as compared to MY was elucidated based on the ionic strengths of the dye molecule. The utilisation of sustainable and renewable raw materials, such as graphite and chitosan, have resulted in a 3D-GCB production cost of approximately RM 10.72 per gram. This work revealed the suitability of 3D-GCB as the state-of-the-art graphene composite for continuous adsorption of anionic dyes, specifically MY and RBB.