Search

This study presents a comprehensive investigation into the mechanical properties of Basic Magnesium Sulfate Cement Concrete (BMSC) in comparison to Ordinary Portland Cement Concrete (OPC) within reinforced concrete components. The main objective is to evaluate BMSC's applicability for practical engineering purposes, with a focus on its with early high strength, improved toughness, and superior crack resistance compared to conventional concrete. Experimental procedures involved fabricating beam specimens using OPC concrete with a C40 strength grade, alongside BMSC beams with varying strength grades (C30, C40, and C50). These specimens underwent bending resistance tests to analyze crack patterns and mechanical characteristics. The findings reveal that BMSC beams demonstrate enhanced bending and tensile properties at equivalent strength grades compared to OPC beams. Particularly, cracking mainly occurred at the mid-span region of BMSC beams, characterized by narrower cracks, indicating superior crack resistance. However, it was noted that the toughness of BMSC beams decreases as the strength grade increases. The maximum mid-span deflection of the BMSC test beam was smaller than that of the OPC test beam, which was 3.8 mm and 2.6 mm, respectively. The maximum crack width of the OPC beam was 4.7 times that of the BMSC beam. To facilitate practical implementation, the study developed calculation models for estimating the crack bending distance and ultimate bending distance in BMSC beams, offering valuable tools for engineering design and optimization. Overall, this research provides significant insights into the mechanical behavior of BMSC, presenting potential advantages for structural engineering applications. [ABSTRACT FROM AUTHOR]

External prestressing is widely employed in structural strengthening engineering due to its numerous advantages. However, external prestressed steel bars are prone to corrosion when exposed to the service environment. This paper is dedicated to examining the use of fiber-reinforced polymer (FRP) bars as external prestressing materials to strengthen one-way concrete slabs. Five one-way concrete slabs were strengthened with externally prestressed FRP bars with different prestress levels and different amounts of FRP bars, while one non-strengthened slab was used for comparison. The effects of strengthening on the flexural behavior, specifically the cracking load, ultimate load, stiffness and failure mode, were analyzed systematically. Moreover, the ductility and cost–benefit optimizing properties of the reinforcing design were discussed. The results show that external prestressed FRP bars significantly improve the cracking load, ultimate load and stiffness of one-way concrete slabs. The absence of a bond between the concrete and FRP bars overcomes the brittleness of the FRP bars, while the strengthened slabs exhibit satisfactory ductility and a higher post-yield stiffness and bearing capacity. Additionally, the cost/benefit ratio is optimized by increasing the prestress level, while a higher number of prestressed FRP bars is beneficial to ductility. Finally, a method for calculating the stress in prestressed FRP bars at ultimate loads was proposed. Irrespective of the prestressing material, this method is applicable to both strengthened beams and one-way slabs. [ABSTRACT FROM AUTHOR]

After a fire exposure, reinforced concrete (RC) structures typically retain their integrity, yet they incur significant damage due to material degradation and thermal expansion. The restoration of fire-damaged RC structures is a complex structural engineering challenge. This study presents a numerical investigation on the post-fire behavior of RC beams, subjected to parametric fire for different durations (15, 30, 60, and 90 minutes) and their retrofit method. Initially, the impact of high-temperature conditions on the residual load-bearing capacity is assessed, with a focus on beam length and support conditions as key geometric variables. Repair methods aimed at improving the post-fire performance of damaged beams are then evaluated. These methods include the use of additional reinforcement and the implementation of steel jacketing, complemented by concrete of varying compressive strengths (25, 30, 35, and 40 MPa). Eurocode models for both concrete and steel materials are used in the simulations using SAFIR software. Results indicate a decrease in load-carrying capacity with prolonged fire exposure, with capacity reductions reaching 85% for beams subjected to 90-minute fire scenarios. Application of steel jacketing markedly enhances both bending and shear resistance of the compromised beams, with the ability to restore the load-bearing capacity of one-hour fire-exposed beams by up to 112%. The correlation between repair effectiveness and the inherent resistance of the RC beams is also elucidated. Furthermore, an analytical expression is proposed for estimating the post-fire load-bearing capacity of reinforced beams, offering a practical and accurate tool for engineering assessments. [ABSTRACT FROM AUTHOR]

When empirical wavelet transform (EWT) is used to identify the modal parameters of civil engineering structures, the frequency band division is generally not accurate due to the noise effect on the Fourier spectrum. This phenomenon will lead to modal mixing and false modes in the analysis results. Therefore, this article establishes a signal frequency band division method by taking advantages of maximal spectrum technique and spectral skewness index. Combining the random decrement technique (RDT) and the least square method of single component modal parameter identification, an adaptive approach based on the improved EWT for operational modal parameter identification of civil engineering structures under stationary environmental excitations is proposed. The traditional frequency band division method based on EWT can not completely and effectively divide the meaningful frequency bands. While the proposed frequency band division technology based on the spectral skewness index can prevent the phenomenon of insufficient division and excessive division and realize adaptive frequency band division. The effectiveness of the proposed approach is validated by a numerical three-story frame and a field three-span concrete box girder bridge under ambient vibrations. The modal identification results from both numerical and experimental validations demonstrate that the proposed approach can effectively and accurately decompose the vibration responses and identify the structural modal parameters under operational conditions. [ABSTRACT FROM AUTHOR]

This paper presents comprehensive empirical equations to predict the shear strength capacity of reinforced concrete deep beams, with a focus on improving the accuracy of existing codes. Analyzing 198 deep beams imported from 15 existing investigations, this study considers various parameters such as concrete compressive strength (f′c), the shear span-to-effective depth ratio (av/d), and reinforcement ratios (ps, pv, and ph). Introducing a novel predictive empirical equation, this study conducts a rigorous evaluation using statistical metrics and a linear regression analysis (MAE, RMSE, and R2). The proposed model demonstrates a significant reduction in the coefficient of variation (CV) to 27.08%, compared to the existing codes' limitations. Comparative analyses highlight the accuracy of the empirical equation, revealing an improved convergence of data points and minimal sensitivity to variations in key parameters. The results proved that the proposed empirical equation enhanced the accuracy to predict the shear strength capacity of the reinforced concrete deep beams in various scenarios, making it a valuable tool for structural engineers. This research contributes to advancing the understanding of shear strength capacity in reinforced concrete deep beams, offering a reliable empirical equation with implications for refining design methodologies and enhancing safety with the efficiency of structural systems. [ABSTRACT FROM AUTHOR]

Electromagnetic wave velocity in ground‐penetrating radar (GPR) constant offset data can be estimated via the diffraction hyperbola fitting method. This method is applicable when radargrams contain diffraction events (hyperbolic patterns) caused by scatters in the host smaller or equal to the dominant wavelength. An alternative method for velocity estimation, if no intrusive information is available for a direct correlation, requires the collection of multi‐offset data. The method is quite common for broad geophysical applications; however, it seems not to be fully utilized for engineering applications, such as slabs/walls where thickness estimation and depth of the embedded features are critical requirements for structural assessments. This method would also overcome the limitations in velocity calibration in environments with no hyperbolic signal signatures. The aim of this study is to explore multi‐offset high‐frequency GPR applications, specifically the wide‐angle reflection and refraction method, for structural engineering, to understand whether it is feasible, possible limitations, and advantages. Numerical models reproducing reinforced concrete elements and a cavity wall were analysed to understand the wave behaviour and predict the response prior to testing on real cases. The main purpose is to explore how reinforcing bars can affect the velocity spectra derived from semblance analysis and what the response would be in a case of multi‐layered structure with increasing velocity with depth (cavity wall). The comparison with real cases showed that, despite some intrinsic limitations, high‐frequency multi‐offset approach could be part of standard workflow for all those surveys where no other velocity estimation method is feasible. [ABSTRACT FROM AUTHOR]

Cold-formed steel, prized for its affordability and durability, gains enhanced structural performance through perforated patterns on beams. This study explores the impact of perforation on load-carrying capacity and capability-to-weight ratio in cold-formed steel cantilever beams, specifically focusing on "C" sections. Assessing beams of varying lengths under uniform loading, the investigation, employing ABAQUS software, reveals significant load-carrying capability reductions of 35% to 95% for beams subjected to minor-axis bends, depending on hole quantity and placement. The findings stress the imperative precision needed when incorporating perforated sections into load-bearing structural systems. Flange perforations result in substantial drops, up to 90%, in load-carrying capacity, while web holes in beams bent about the major axis cause only minor reductions. This study informs the establishment of innovative design standards for perforated cold-formed steel beams, enhancing efficiency in construction and minimizing environmental impact. The proposed cold-formed steel cantilever beam exhibits a substantial load-bearing capacity of 185.4 kg/KN, surpassing capacities of simply-supported RC beams (119 kg/KN) and fixed beams (165 kg/KN), suggesting its potential advantages in structural applications. Bridging structural engineering and material science, this research pioneers sustainable and cost-effective construction approaches for the future. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Concerns have been raised regarding the long-term durability of FRP-bar reinforced seawater sea sand concrete (SWSSC) members in coastal engineering structures. In this context, an experimental study was conducted to investigate the long-term flexural behavior of GFRP-bar reinforced SWSSC beams, which were subjected to sustaining loads and exposed to both natural and chamber-accelerated coastal environments. Following the exposure period, twelve beams were tested under four-point bending loads. The results indicate that both the natural outdoor and chamber accelerated environments exhibited higher load-carrying capacity and lower deflection than the natural indoor environment. The load-carrying capacity showed an improvement ranging from 10.3% to 34.8%, while deflection decreased by 6.5% to 19.9% when compared to the indoor environment. However, the presence of seawater and sea sands in SWSSC beams resulted in a reduced load-carrying capacity compared to normal concrete (NC) counterparts, with the reduction reaching up to 15.8%. Furthermore, an increase in the sustaining load led to decreased load-carrying capacity but increased deflection, with values of 26.0% and 11.0%, respectively. To evaluate the appropriateness of current modern design codes, the load-carrying capacity and ultimate deflection of both NC and SWSSC beams were calculated and compared with the test results. The analysis revealed that the American code ACI440.1R-15 and Chinese code GB50608-2020 exhibited a conservative approach in predicting bending moment capacity. Additionally, the American code ACI440.1R-15 and Canadian code CSA S806-12 underestimated the ultimate deflection, whereas the Chinese code GB50608-2020 overestimated it. These findings provide valuable insights for designing coastal structures using GFRP-bar reinforced SWSSC materials. [ABSTRACT FROM AUTHOR]

Concrete, as the most widely used construction material, is associated with a high environmental impact. Within the present study, structural optimization is the method of choice to counter this issue. The entire process, from optimization, to design, experiments and numerical simulation is outlined. Embedded in the framework of a design competition (Concrete Girder Optimization Competition 2021), a bridge between structural engineering and mathematical optimization is demonstrated. Two design concepts for optimized concrete girders, one with internal and one with external reinforcement, yet both based on strut‐and‐tie modeling, were investigated. Within the boundaries of the competition, several conclusions can be drawn: The results indicate the importance of an adequate structural interpretation of topology optimization results to obtain satisfying structural performance. The environmental evaluation outlines that the reinforcement mass has a substantial share in the total Global Warming Potential. A successful numerical re‐simulation of selected girders can serve as a modeling base for other researchers. Compared to a conventionally designed girder an increase in resource efficiency, measured by load‐carrying capacity versus environmental impact, of more than 30% was achieved. [ABSTRACT FROM AUTHOR]

To reduce the impact on global warming, many countries and regions are advocating the use of low‐carbon and green supplementary cementitious materials (SCMs) to improve the cement industry. This study investigates the shear behavior of reinforced concrete (RC) beams made with a novel green concrete, limestone, calcined clay, and cement clinker (LC3). Full‐scale shear tests were conducted on RC and LC3‐RC beams to evaluate their fundamental behavior for structural applications. The shear resistance of LC3 concrete beams was evaluated using Chinese, American, and Eurocode codes, while carbon emission and environmental impact were analyzed by life cycle assessment (LCA). Results showed that LC3‐RC and RC beams exhibited similar shear behavior in terms of crack initiation, propagation, and development. The increase in concrete strength improved the stiffness of LC3‐RC specimens, while RC specimens remained unchanged. The ductility of RC specimens was inversely proportional to concrete strength, while LC3‐RC beams exhibited optimal ductility. The increase in stirrup ratio significantly improved the shear resistance of both LC3‐RC and RC beams. With increased shear‐span ratio, the failure mode of LC3 specimens improved, changing from diagonal‐compression to shear compression or diagonal tension failure. The shear resistance of both LC3‐RC and RC beams was underestimated by GB 50010‐2010, ACI 318‐19, and Eurocode 2. LC3 concrete significantly reduced energy consumption and carbon emissions and achieved favorable environmental benefits in terms of GWP, PMFP, POFP, TAP, and TETP. Increasing usage of limestone in LC3 may further decrease environmental potential without sacrificing mechanical properties. The findings of this study contribute to the development of sustainable and environmentally friendly structures in engineering. [ABSTRACT FROM AUTHOR]

Crack patterns provide critical information about the structural integrity and safety of concrete structures. However, until now, there has been a lack of sufficient studies on using the Finite Element (FE) method to investigate the characteristics of the crack patterns of reinforced concrete (RC) beams. Therefore, this study aims to develop an FE model to analyze the load–displacement and crack characteristics of a beam under a four-point bending test using the concrete damaged plasticity (CDP) model that accounts for the influence of mesh size. The simulation results were validated against experimental results, including mesh convergence analysis, energy balance, load characteristics, and crack patterns. A parametric study was then conducted using this model to investigate the influence of the rebar's diameter, number, and spacing on the RC beam's load–displacement characteristics and crack behavior. The findings demonstrate that the FE model accurately simulates the working behavior of the RC beam, with a maximum deviation at a cracking load of 8.7% and crack patterns with a maximum deviation in the mean crack height of 12.1%. In addition, the results of the parametric study suggest that the rebar configuration significantly affects the RC beam's loading carrying capacity. This study provides deeper insights into the use of FE modeling for analyzing the behavior of RC beams, which can be useful for designing and optimizing structures in civil engineering. [ABSTRACT FROM AUTHOR]

This research describes an attempt to conduct analytical investigations on the deflection behavior of Carbon Fiber Reinforced Polymer (CFRP)-reinforced concrete beams. The primary objective of this study is to undertake a comprehensive review of formulation studies grounded in deflection equations. To accomplish this objective, a total of eleven test data points from three groups of researchers were acquired. Then, these data are compared against the deflection predictions from four deflection equations, namely, ACI 440.1R-06, ACI 440.1R-15, Bischoff and Gross, and ISIS Canada. An empirically derived model was proposed to predict the effective moment of inertia for reinforced concrete (RC) beams reinforced with CFRP, based on Branson's equation. Furthermore, to enhance the prediction of the moment-deflection relationship up to the ultimate strength, a nonlinear parameter (k) has been introduced in previous research for FRPs. These parameters were added to the formulation and aimed to mitigate the impact of the cracked moment of inertia on the reinforced concrete member. The accuracy of the novel formulation for analyzing deflections in CFRPreinforced concrete beams was statistically evaluated. In a comparative study employing various design codes, the proposed model exhibited greater agreement with experimental test results. Ultimately, the proposed model demonstrated enhanced accuracy and emerged as a familiar approach for structural engineers to forecast and evaluate the deflection behavior of RC beams reinforced with CFRP. [ABSTRACT FROM AUTHOR]

Early detection of damage is necessary for the safe and reliable use of civil engineering structures made of concrete. Recently, the identification of micro-cracks in concrete has become an area of growing interest, especially when it comes to using wave-based techniques. In this paper, a non-destructive testing approach for the characterization of the fracture process was presented. Experimental tests were performed on concrete beams subjected to mechanical degradation in a 3-point bending test. Ultrasonic waves were registered on a specimen surface by piezoelectric transducers located at several points. Then, the signals were processed taking advantage of wave scattering due to micro-crack disturbances. For early-stage damage detection, coda wave interferometry was used. The novelty of the work concerns the application of the complex decorrelation matrix and the moving reference trace approach for better distinguishment of sensors located in different parts of a crack zone. To enhance coda wave-based damage identification results, optical imaging of crack development was performed by means of digital image correlation measurement. The results obtained showed that the coda wave interferometry technique can be successfully used as a quantitative measure of changes in the structure of concrete. The results also indicated that the course of decorrelation coefficient curves enabled the identification of three stages during degradation, and it depended on the location of acquisition points versus the crack zone. [ABSTRACT FROM AUTHOR]

In recent years, substantial efforts have been made to incorporate the nonlinear behavior of joints in frame analysis. In this regard, a well-established component-based model (CBM) is provided in EN 1993-1-8 to capture the nonlinear moment–rotation curve of steel beam-column joints. In this paper, to extend the design approach in EN 1993-1-8 to steel beam-to-reinforced concrete column (RCS) joints, a simple mechanical model is first developed to determine the moment resistance of RCS joints. The proposed model can predict two different failure modes of RCS joints and yields better accuracy and consistency compared to the current design method for such joints. After determining the joint moment resistance, an appropriate CBM is developed to simulate the nonlinear moment–rotation response of RCS joints. The proposed CBM can capture the moment–rotation behavior of RCS joints at elastic, transitional, and ultimate states. The initial rotational stiffness of RCS joints can be easily calculated based on the proposed CBM. This is particularly important in the frame analysis as the beam-column joint can be classified as a rigid, semirigid, or pinned joint based on the initial rotational stiffness. The derivations are discussed in detail, followed by verifications of the proposed CBM. Compared to the experimental results, the proposed CBM shows reasonable accuracy in predicting the moment–rotation behavior of RCS joints. Most importantly, the numerical procedure of the proposed CBM can be implemented by a spreadsheet method, which provides a simple and robust means for structural engineers to obtain the mechanical properties of RCS joints. [ABSTRACT FROM AUTHOR]

In recent years, the combined use of posttensioning methods with steel decking in the concrete slabs has provided significant benefits in both economic and structural terms. In construction practice, this prestressed composite floor has extensive use in two‐way spanning slab systems. However, this application still has inherent limitations due to its novelty and lack of research in this specific field. Hence, this study aims to gain the first insight into the flexural behavior of a two‐way posttensioned composite (PTC) slab system based on a numerical approach. Scientific software is utilized for the derived model validation with a focus on models' geometric and material nonlinearity. Available modeling techniques used to perform the posttensioning process and interaction of composite slabs are verified with existing experiments. A comparative study was conducted between PTC slabs and various conventional slab systems on two different support conditions. The findings in this study consider the structural effects of posttensioning and profiled steel sheeting on the flexural behavior of PTC slabs. In addition, numerical results from this research might help structural engineers to modify the design input parameters and make judgments on the bending stiffness of this two‐way slab structure. Further studies are required since the shear behavior, flexural, and punching shear conditions in the column regions are not addressed in this article. [ABSTRACT FROM AUTHOR]

Digital twins map physical objects, processes, and further entities from the real (physical) world into digital space. Going one step further, hybrid digital twins combine physics‐based modeling with data‐based techniques to form a simulation tool with predictive power. In the light of an increasing digitalization of our built world, such digital twins have great potential to contribute to the protection of critical technical infrastructures. In case of bridges, digital twins can have a key role in structural health monitoring. This contribution outlines a path to approach these goals and provides a proof of concept of a hybrid digital twin for steel‐reinforced concrete beams as a representative component in civil engineering structures. Four model components are combined to form the hybrid digital twin, namely, a physics‐based full‐order model, a fast‐to‐evaluate reduced‐order model, a purely data‐driven model, and a baseline model. Applied to the concrete beam, the full‐order model is based on a novel finite element formulation allowing for efficient modeling of slender structures embedded into solid bodies. We use this method to capture the interaction between reinforcement components and concrete matrix of the beam. As reduced‐order model, a physics‐informed neural network is trained with parts of the available measurement data and with the governing equations of a simplified physical model. The data‐driven model localizes cracks in the concrete in a statistical outlier analysis of fiber‐optical strain measurement data. In completion, the baseline model estimates the system behavior based on a closed‐from expression from civil engineering literature. The proposed hierarchy of four models with decreasing model complexity allows to select an appropriate model according to an application specific trade‐off between model accuracy and cost. We demonstrate the combination of physics‐based modeling with data‐driven techniques based on sensor data measured in a physical four‐point bending test of the reinforced concrete beam. [ABSTRACT FROM AUTHOR]

This study aimed to determine the area of reinforcing steel in rectangular reinforced concrete beams, a critical concern given that a significant proportion of residential structures in Peru do not conform to technical design regulations. Data were collected through a structured form encompassing various design variables, yielding a comprehensive data matrix. The methodology adopted involved Knowledge Discovery in Databases (KDD), executed in several stages: (1) selection, where the InfoGainAttributeEval algorithm was utilized to identify variables influencing the reinforcing steel area; (2) preprocessing, during which anomalous and duplicate data were purged using the Python libraries, Pandas and Numpy; (3) reduction, and (4) data mining. For the latter, Decision Stump, Hoeffding Tree, J48, Logistic Model Trees (LMT), and Random Tree classification algorithms were employed, facilitated by Weka 3.9.4. Accuracy rates of these algorithms were found to be 25, 51.70, 77.97, 78.39, and 88.98% respectively. The Random Tree algorithm, in conjunction with the GP_04 model, provided estimations of the steel area in the beams with a reliability exceeding 88%. The application of this model could enable the optimization of beam design, facilitate cost savings on materials, and enhance structural safety. This research thus presents a significant contribution to the field of structural engineering, particularly in regions where compliance with technical design standards is suboptimal. [ABSTRACT FROM AUTHOR]

The aim of this study is to find a simple, fast, and economical method for designing RC cantilever beams instead of the traditional methods. Generally, the concrete beams have been designed as T-sections so that their flange has failed within the compression zone, which has embedded with reinforced concrete slab. The structural engineer can select the appropriate section for each span length, but the trial and error method may be used with more effort and time. The economic selection has been become more difficult for cantilever beams especially with a large span due to the significant deflections they have been exposed to. In order to solve this problem, a current theoretical study has been conducted on a number of locally common dropped and inverted T-sections for cantilever RC beams that reinforced with a different reinforcement ratios and their sustained and immediate deflections have been checked according to the ACI -19 provisions. By creating an algorithm and using the Python 3.4 software, the allowable sections have been identified for each required span length. Three curves have been drawn to choose the optimum dimensions for both dropped and inverted T-sections easily. In addition, by comparison, it has been found out that the selection of inverted T-sections has more economy to provide longer spans than dropped sections. [ABSTRACT FROM AUTHOR]

This correction notice addresses errors in the article "Collapse Resistance of Reinforced Concrete Frame Beams under Sustained Loads" by Ghazaleh M. Laleh, Ying Tian, and Sarah L. Orton. The errors occurred when converting imperial units to metric units in the "Bending Moment" section of the paper. The correction involves dividing the numbers on the horizontal axes of Figures 16 and 17, as well as the vertical axis of Figure 18, by 12. Additionally, two values in the last paragraph of this section need to be changed. The corrected figures are provided in the erratum. The purpose of this correction is to ensure the accuracy of the published version. [Extracted from the article]

This editorial highlights six papers published in the Proceedings of the Institution of Civil Engineers: Structures & Buildings. The topics covered include the bond properties of steel rebars in cracked concrete, flexural strengthening of reinforced concrete beams, repairing fire-damaged circular RC columns, seismic performance of a connection between concrete-filled steel columns and RC frames, optimal sensor placement for detecting rail damage, and the mechanical behavior of fasteners in laminated bamboo lumber. The papers present experimental and theoretical research, providing valuable insights into various aspects of civil engineering. [Extracted from the article]

Estimating the shear strength of a fiber-reinforced polymer (FRP)–reinforced-concrete (RC) beam is a complex task that depends on multiple design variables. The use of FRP bars has emerged as a promising alternative to diminish the corrosion problems that are associated with steel reinforcement in adverse environments; however, an accurate and reliable method of shear strength prediction is needed to ensure the economical use of materials and robust designs. Several optimized design equations are available in the literature; however, when utilizing these equations a substantial difference is observed between the predicted outcome (Vpred) and the experimental shear strength (Vexp) result. Therefore, this paper presented a novel approach toward implementing machine learning (ML) algorithms to accurately estimate the shear strength of FRP–RC beams. A large database that consisted of 302 shear test results on FRP-reinforced slender concrete beams without stirrup was collected from the literature to formulate the most efficient prediction model. The performance of each ML algorithm model was compared with the existing design provisions and models. The model interpretation was performed through feature importance analysis to explain the model output compared with a black box. The proposed data-driven ML models demonstrated a high level of accuracy and excellent performance and were superior to the existing shear strength models. In addition, a simple graphical user interface (GUI) was developed to aid practicing engineers when estimating shear strength without the need for complicated design procedures. The shear strength of FRP–RC beams is calculated using various design codes and guidelines that are heuristically developed based on previous test results. In general, the developed equations are either mechanics-based or empirical. However, this paper demonstrated that data-driven ML algorithms could generate a more reliable and appropriate prediction of the shear strength of FRP–RC beams. Furthermore, as the database increases, it could be automatically updated, which would result in more accurate and reliable results. Designers and practitioners could conveniently use the developed algorithms for the reliable and quick prediction of the shear strength of FRP–RC beams. In addition, the developed GUI is innovative and user-friendly. It allows users to determine the design shear strength without referring to an existing code by employing ML in conjunction with a large, reliable, and authenticated database to ensure accuracy. This could be important for the structural engineering community when assessing the shear capacity of existing FRP–RC beams. [ABSTRACT FROM AUTHOR]

Cracks will inevitably occur in concrete structural during the normal use phase due to load action, fatigue effect, and environmental corrosion. Concrete resistance can reflect some internal concrete damage. In this work, three-point bending tests were performed on concrete beams. We simultaneously measured the concrete beam resistance before and after cracking to investigate the feasibility of resistance measurement for achieving self-monitoring of concrete cracking and crack development. This work provides a methodology for the monitoring of engineering structures. It establishes a quantitative reference for the relationship between the number of steel fibers used and their conductive and mechanically sensitive properties. [ABSTRACT FROM AUTHOR]

The data‐driven approach based on plenty of high‐fidelity data such as experimental data becomes prevalent in the prediction of structural behavior. However, sometimes the high‐fidelity data are hard to obtain and are only in small amount. Meanwhile, the low‐fidelity data like simulation result are in large amount but their accuracy is relatively poor and are not suitable for establishing models. Thus, based on machine learning (ML) algorithms a multifidelity approach is present, which can enhance the prediction models performance under multifidelity data. First the basic theory and application procedure of this approach are introduced. Then a case study for predicting the shear capacity of reinforced concrete deep beams was carried out to validate this method's feasibility. The influence of different ML algorithms, low‐fidelity data resources, and high‐fidelity data ratios were thoroughly investigated. The results showed that this approach would effectively promote a models accuracy under multifidelity data and has the potential to be an alternative to facilitate solving some prediction issues in structural engineering. [ABSTRACT FROM AUTHOR]

Through the combination of two approaches to evaluating structure change, a structural model and an unstructured model, a constructed model has been proposed in this article that evaluates structural change through the expansion of a linear model following the Hooke's Law principle. The study has relied on the pure compression model of a structure's concrete beam with elastic modulus (E) and has added the coefficient of viscosity resistance (C) to suggest a new evaluation method. By defining the aggregation of values of both coefficients C and E through the experimental model, the input parameters are the amplitude values of the vibration spectra and the values of frequencies based on machine learning, through which Z EC values are generated. The Z EC values determine a regression plane accumulated from the aggregation of values for both C and E. The article has introduced the Z EC concept as a useful parameter for the assessment of the quality of concrete structures by the nonlinear model with the appearance of the coefficient C. The results show that the Z EC values have expressed the distribution validity according to the structure's differing degrees of change. Depending on the texture type and the structure status, these Z EC values will form different shapes. By implementing the actual surveys from many bridges with two types of beam structures, prestressed concrete and conjugated concrete, the Z EC values show the same development trend. On the contrary, in the case of a change in mechanical structure, the Z EC values tend to increase. This evidence proves, in regard to the process of structural change, that the larger the changes in the structure, the more pronounced the distribution of Z EC values, and the wider the distribution range. This shows that the ratio of the damping coefficient C to the elastic modulus E will become increasingly unstable as the structure becomes weaker and weaker. In the future, the results from this study can be applied in the assessment of many types of actual structures. [ABSTRACT FROM AUTHOR]

The durability of reinforced concrete (RC) beams strengthened with carbon fiber-reinforced polymer (CFRP) is a worldwide concern in structural engineering. As an important part of the strengthened beam, the performance of the CFRP–concrete interface under hygrothermal environments is a delicate problem. In this paper, the fatigue behavior of CFRP-strengthened RC beams is analyzed by a theoretical model. In the model, CFRP–concrete interface degradation under hygrothermal environments is involved. Since interface debonding and rebar fracture induced by intermediate cracking are two typical failure modes, the damage models of rebar and the CFRP–concrete interface are established. Based on the theoretical model, the failure mode of CFRP-strengthened RC beams can be predicted, and fatigue life can be determined. The results showed that IC debonding is more likely to occur under hygrothermal environments. The accurate prediction of failure modes is essential for fatigue life prediction. [ABSTRACT FROM AUTHOR]

Evaluating the performance of postfire RC beams is a crucially important step to provide information for structural engineers on the next step of retrofitting design. Appropriate evaluation methods should be placed in the context of very limited information obtained from a real fire event. In this direction, this paper presents an experimental and theoretical investigation on the flexural performance of postfire RC beams. Experiments were performed on 15 RC beams, which were classified into 5 groups exposed to 0, 30, 45, 60, and 75 min of fire. These beams were then loaded to failure and different mechanical properties were analyzed, followed by theoretical analyses. The results showed that postfire specimens experienced flexural-shear failure, which was different from flexural failure of control specimens. Compared with control specimens, the yield stiffness and strength of postfire specimens were reduced by 47.5% and 13.1%, respectively, while yield deflection was increased by 42.6%−91.6%. The ductility of postfire specimens was decreased by up to 61.1%, resulting in moderately ductile behavior. Significant changes in yield stiffness, yield deflection, and ductility confirmed these high-priority parameters in evaluating postfire RC beams. The proposed simple method, which combined the temperature-distribution model and the reduced 500°C isotherm method, provided conservative bending moment capacities compared with the test results. The simplicity and reasonable safety of the model can be useful for structural engineers. [ABSTRACT FROM AUTHOR]

This paper proposes a direct analytical model to evaluate compressive arch action (CAA) of reinforced concrete (RC) beam-column sub-assemblages under middle column removal scenario (MCRS), incorporating bending curvature of the double-span beam, and different horizontal and rotational restraint conditions at the beam ends. Based on the proposed approach, the structural engineer can easily obtain the CAA peak capacity and the corresponding CAA displacement by hand calculations. Unlike the previous models in which the accuracy highly relies on pre-assumed CAA displacement, this prediction approach directly derives the CAA displacement and CAA capacity through force equilibrium, geometric relationship, compatibility and boundary conditions, incorporating various horizontal restraint conditions, span-to-depth ratio and section profile of the bridging beam. Comparison with existing test results in available literature indicates that this model not only gives good predictions of peak capacity, displacement and maximum axial beam force in CAA, but also incorporates the effect of horizontal restraint stiffness and working stress of compression reinforcement. Besides, a further simplified explicit model is proposed to enhance usability in practical design, and sensitive studies are conducted to study how the simplification affects the prediction accuracy of vertical CAA capacity and displacement. Thereafter, the proposed model is extended to substructures with orthogonal beams in X- and Y- directions to predict CAA capacity under different column removal scenarios such as interior, penultimate, edge and corner column removal cases. • An easy-to-use analytical model to predict compressive arch action of RC beams under middle column removal. • Bending curvature, horizontal and rotational restraint conditions of beam are considered. • The model does not rely on pre-assumed CAA displacement. • Effect of horizontal restraint condition on CAA displacement and capacity can be well considered. • The model is extended to two-way framed substructures under different column removal scenarios. [ABSTRACT FROM AUTHOR]

The implementation of structural health monitoring (SHM) systems in existing civil engineering structures could contribute to a safer and more resilient infrastructure as well as important savings. Due to their light weight, small size, and high resistance to the environment, distributed optical fibre sensors (DOFS) stand out as a very promising technology for damage detection and quantification in reinforced concrete (RC) structures. In this paper, the performance of DOFS featuring an external polymeric cladding with rough surface, to accurately assess deflection and crack width of a stainless-steel RC beam subjected to four-point bending is investigated. Several sensor positions, both embedded in the concrete and attached to the surface, are investigated in a multi-layer configuration. The study revealed that embedded sensors yield very accurate results regardless of the sensor position and the load level, that is, service or ultimate loads, being the sensor glued in a premade groove on the steel bar the most reliable solution for high-load levels. Conversely, externally deployed sensors for the assessment of existing structures, described attenuated values for the measured deflections, and, to some extent also the crack width, due to a loss of bond between the sensor and the surrounding concrete, already for service loads. Corrective methods to further use the obtained data are presented, yet the clad DOFS attached to the concrete surface described a significant drop of performance with increasing load levels, showing an important loss of data at 80% of the ultimate load. [ABSTRACT FROM AUTHOR]

Despite ongoing research efforts aimed at understanding the structural response of ultrahigh-performance concrete (UHPC) beams, there are very limited provisions for structural design of UHPC beams, specifically under flexure. To address the current limitations in design provisions for UHPC beams, which notably hinder the applicability of UHPC in concrete structures, it is essential to develop simplified design methodologies that can optimize the superior range of properties offered by this new class of material. In this study, a machine learning (ML)–based approach for predicting the flexural capacity of UHPC beams was developed. ML algorithms such as support vector machine regression (SVMR) and multigene genetic programming (MGGP) were applied to analyze data and observations collected from large set of tests on UHPC beams with different geometries, fiber properties, and material characteristics. SVMR was applied to predict the flexural capacity of UHPC beams, whereas MGGP was employed to derive a simplified expression, through data-driven analysis of actual test data, for predicting the flexural capacity of the beams. A parametric study was also performed to validate the proposed approach with varying input parameters. The results indicated that the proposed data-driven approach using ML was effective in predicting the flexural capacity of UHPC beams with different material and configuration characteristics, paving the way for the development of guidelines for the design of UHPC beams. The results of this comprehensive analysis showcase the merit of employing ML in structural engineering applications and thereby promoting the applicability of UHPC in concrete structures. [ABSTRACT FROM AUTHOR]

Realization of the new AirportAcademy on the LabCampus at Munich airport As a general contractor, Ed. Züblin AG is planning and executing for the LabCampus GmbH the new training center AirportAcademy, which is part of the LabCampus innovation hub on the Munich Airport site. Especially the external two‐story concrete truss poses a significant challenge in terms of structural engineering. Inspired by the distinctive shape of Munich Airport's runway & taxiway, the structure is designed without expansion joints. In combination with the laterally connected steel‐composite beams and the rising concrete façade pillars, the concrete truss functions as part of the superstructure by supporting the floors above. Regarding the fulfilling of demands for an even and seamless appearance of the see‐through external supporting structure, the combination of a unique structural design and the presence of highly stressed nodes poses a significant challenge to the formwork and exemplifies the complexity in concrete construction. [ABSTRACT FROM AUTHOR]

Ultra-high-performance concrete (UHPC) has been gradually used in structural engineering due to its excellent mechanical performance; however, predicting the shear strength of UHPC beams is still a challenge, especially for beams with small shear span-depth ratios. To address this issue, this paper is devoted to developing a rational model to predict the shear capacity of UHPC beams with stirrups. The shear-resisting mechanism of UHPC beams has been revealed and the contributions of compression zone and dowel action on shear strength have been presented. Combined with the Modified Compression Field Theory (MCFT), a simple method for shear capacity assessment has been presented. Twelve UHPC beams were designed and tested to validate the proposed model. The proposed method has been verified and can be used to predict the shear capacity and average diagonal crack width of UHPC beams with satisfactory accuracy, while the current design codes, including SETRA-AFGC and SIA, give conservative shear strength for UHPC beams when the shear to span is less than 2.5. Moreover, the contributions of compression zone and dowel action are found to be 32 and 17% for UHPC beams with small shear span-depth ratios. [ABSTRACT FROM AUTHOR]

Now a days all the civil engineering structures are designed earthquake resistant to withstand the high intensity earthquakes that are occurring more frequently. But there is a need to strengthen the already existing structures to withstand for earthquakes. Usage of CFRP sheets is a technique to strengthen a structural member of a structure to withstand for natural occurring calamities. This study presents the experimental and numerical investigation on the effect of CFRP sheets with varying angles of straps on the strength of the beam. The objective of this work is to explore the behaviour of RC beam strengthened with CFRP sheet and straps. Numerical analysis was carried out using software to represent the ultimate load vs deflection and ultimate load carrying capacity of the beam. Comparison of experimental with numerical results exhibited, that beam strengthened with angled straps showed more effective in enhancing the deflections, failure modes and ultimate strength of the RC beam. The strength of CFRP straps with different orientations were analyzed and compared. [ABSTRACT FROM AUTHOR]

In this study the effectiveness of using Basalt Fiber Reinforced Polymer (BFRP) for strengthening and repairing concrete beams after pre-damage was investigated. Four-point bending tests were conducted on concrete beams, and their strain nephograms were analyzed using 3D-DIC (Digital Image Correlation) to assess the BFRP reinforcement and repair effects on concrete. A damage indicator model based on stress-displacement curves was proposed to quantify concrete damage and evaluate the BFRP strengthening effect. Additionally, the YOLO-v5 object detection algorithm was employed to detect surface damage on concrete specimens, and its results were cross-verified with the damage indicator. The study reveals that BFRP reinforcement primarily affects the concrete beams beyond the elastic limit, enhancing their load-bearing capacity by 37.91% and improving ductility. The proposed damage indicator proves successful in quantifying damage in BFRP-reinforced concrete beams, distinguishing the transition points between elastic, cracking, and failure stages. The YOLO-v5 algorithm accurately detects damage in strain nephograms, providing sensitive and timely identification of concrete damage. Results proves that the offset ratio between the damage identification results of YOLO-v5 and the experimental stress-displacement curve data is between − 3.03% and 8.66%. The proposed damage indicator and YOLO-v5 detection offer valuable tools for evaluating damage and monitoring the health of concrete structures in engineering applications. • Evaluation of BFRP repair of concrete beams using 3D-DIC and YOLO-v5 algorithms. • Proposing an effective stress-displacement based concrete damage quantification model. • Damage detection of concrete with YOLO-v5 with good recognition results. • Demonstrate that both YOLO-v5 and damage quantification modeling have good results. [ABSTRACT FROM AUTHOR]

• Bond behavior between SFCB and ECC is comprehensively investigated by spliced beam tests. • A bond-slip constitutive model of SFCB in ECC is devised to estimate the bond-slip behavior. • The bond stress distribution of splicing SFCB in the splice region of ECC beams is discussed through FE analysis. The collaborative utilization of steel-FRP composite bar (SFCB) and engineered cementitious composites (ECC) represents a favorable choice for engineering structures exposed to aggressive environments, offering benefits involving both mechanical behavior and durability. This paper primarily studies the bond performance of SFCB embedded in ECC through spliced beam tests coupled with finite element analysis. The findings demonstrate that with an appropriate cover thickness or transverse confinement, spliced ECC beams exhibit failure modes characterized by splitting and pull-out. Under such conditions, the bond-slip response demonstrates limited sensitivity to changes in cover thickness and transverse confinement. Conversely, increasing the cover thickness or introducing transverse confinement in spliced concrete beams, which exhibit splitting failure, significantly enhances the bond strength. Furthermore, in contrast to spliced concrete beams, spliced ECC beams are observed to demonstrate a bond toughness that is 3.85–12.10 times greater. The discrepancy in bond toughness between ECC and concrete diminishes as the cover thickness and transverse confinement increase. Notably, the uniform distribution of bond stress in the splice region of ECC beams remains unaffected by variations in splice length. To incorporate the impact of bond stress distribution when calculating the splice length of SFCB in ECC beams, a coefficient of 0.85 is introduced. [ABSTRACT FROM AUTHOR]

Fiber-reinforced polymer (FRP) is a revolutionary breakthrough in the history of structural engineering innovation due to its unique characteristics to strengthen and repair the deficient reinforced concrete structures. This paper aimed at evaluating the flexural characteristics of jute fiber reinforced polymer (JFRP) bonded reinforced concrete beams. The influence of the test variables comprised of strengthening scheme and corrosion rate for reinforced concrete (RC) beams. The experimental study comprised of casting six RC beams and testing them in flexure loading. To determine the flexural response of RC beams, three beams were fabricated with JFRP laminate having a level of corrosion of 0%, 7.5%, and 15%, whereas three beams were designated as control beams having same corrosion levels with no JFRP. Test results indicated that all JFRP strengthened beams exhibited increased ultimate load, yield load, first cracking load, and lower ductility index compared to control beams. The results also revealed that JFRP strengthening technique improved the flexural strength of the corroded beams efficiently, albeit the ultimate load of the beams diminished with higher corrosion level. Analytical calculations were carried out for quantifying the flexural characteristics and mass loss of beams which provided a good agreement with the test results. [ABSTRACT FROM AUTHOR]

Structural analysis is used to assess the behavior of engineering structures under the application of various loads. Commonly used structural analysis methods include analytical, experimental and numerical methods. The finite element method has become very popular among engineers and researchers as it is considered to be one of the best methods for solving complex engineering problems efficiently. There are various finite element software packages such as ATENA, ABAQUS, Hypermesh, Nastran and ANSYS to solve the engineering problems. Hence, in the present study ANSYS (Analysis System), finite element software is used for solving the reinforced concrete beams. The results thus obtained are verified with the experiment carried out. The experimental work highlights the study on flexural behavior of conventional and geopolymer reinforced beams with different grades of concrete namely M-30, M-40 and M-50. The test specimens was 100 x 200 x 2000 mm reinforced beams cured at room temperature. The alkaline solution used for present study was the combination of sodium silicate and sodium hydroxide solution with the ratio 2.50. The molarity used for the preparation of geoploymer solution was 12. The beams considered for the study were designed as balanced reinforced sections with percentage of reinforcement varying in the range of 1.45, 2.10 and 2.40. It was observed that the experimental values of reinforced concrete beams were in par with the results obtained with ANSYS modeling. [ABSTRACT FROM AUTHOR]

In recent years, the π-shape structure, that is, two pile shafts supporting a reinforced concrete beam and its superstructure, is increasingly used for bridge foundations. Considering the construction quality and the requirement of settlement tolerance, the pile lengths of the π-shape structure are sometimes different when passive or active. However, the effect of the unequal pile lengths on the dynamic characteristics of the π-shape structure has not been investigated. Therefore, this paper analyzes the dynamic response of the π-shape structure with unequal pile lengths. A beam–unequal length piles–soil coupled vibrating system is established in the frequency domain considering both pile–beam–pile propagation and pile–soil–pile interaction. In order to account for the pile–soil–pile interaction, a fictitious soil pile model is employed to compensate for the length difference between the piles. The developed model and the analytical results are verified through comparison with the results of rigorous finite-element analysis and a model test. The verified model and its corresponding solutions are then utilized to conduct a comprehensive parametric study. Some conclusions derived from this study can extend the application of the π-shape structure in engineering practice. [ABSTRACT FROM AUTHOR]

This paper investigates the use of distributed optical fiber sensors (DOFS) based on Optical Frequency Domain Reflectometry of Rayleigh backscattering for Structural Health Monitoring purposes in civil engineering structures. More specifically, the results of a series of laboratory experiments aimed at assessing the suitability and accuracy of DOFS for crack monitoring in reinforced concrete members subjected to external loading are reported. The experiments consisted on three-point bending tests of concrete beams, where a polyamide-coated optical fiber sensor was bonded directly onto the surface of an unaltered reinforcement bar and protected by a layer of silicone. The strain measurements obtained by the DOFS system exhibited an accuracy equivalent to that provided by traditional electrical foil gauges. Moreover, the analysis of the high spatial resolution strain profiles provided by the DOFS enabled the effective detection of crack formation. Furthermore, the comparison of the reinforcement strain profiles with measurements from a digital image correlation system revealed that determining the location of cracks and tracking the evolution of the crack width over time were both feasible, with most errors being below ±3 cm and ±20 µm, for the crack location and crack width, respectively. [ABSTRACT FROM AUTHOR]

Unsupervised learning methods are effective and suitable tools for damage detection. The main reason for the popularity of these methods in structural health monitoring originates from the fact that the process of learning can be implemented by information of the only normal condition called training data. In contrast, supervised learning methods require information of both normal and current conditions for the process of interest. Because civil engineering structures are expensive and complex, it is not reasonable and economical to impose intentional damage on providing training data. Hence, it is not simple to directly exploit supervised learning techniques in structural health monitoring. To deal with this limitation, this article proposes a novel two-level strategy including three algorithms for using the concepts of both unsupervised learning and supervised learning. The major contribution of this strategy is to consider supervised learning as a validation tool for damage detection. First, the results of damage detection are obtained from two unsupervised learning methods developed by Mahalanobis squared distance and a deep autoencoder neural network in the first two algorithms of the proposed strategy. The main objective is to separate accurate and confusing results of damage detection based on Type I and Type II errors. Second, the confusing results are fed into the third algorithm to train a classifier and compute their classification margins for making the final decision and validating damage detection. The effectiveness and applicability of the proposed strategy are assessed by a numerical concrete beam and an experimental laboratory frame. Results show that this strategy with the aid of the Naïve Bayes classifier enables the unsupervised learning methods to make accurate decisions. [ABSTRACT FROM AUTHOR]

The vibration signal analysis-based structural health monitoring (SHM) system has been widely used for bridge condition identification. With the development of artificial intelligence (AI), machine learning (ML) has made numerous breakthroughs in the detection of civil engineering structures. However, conventional data-driven ML methods heavily rely on prior knowledge for their performance. This paper proposes a deep learning (DL) model 1DCNN-BiLSTM for detecting small local structural changes of reinforced concrete (RC) beams, which applies the structure of the Inception module in GoogLeNet to one-dimensional convolution neural networks (1DCNNs) for feature extraction at different scales, and combines the advantages of bidirectional long short-term memory (BiLSTM) modules for processing long time-series data. Firstly, a three-dimensional numerical model of the RC beam was established using finite element software, and the accuracy of the numerical model was verified by comparing it with the test results. Based on the numerical model, tests on RC beams impacted by falling hammers were carried out, and the acceleration signals of the beam in different states were collected as a dataset. The proposed DL model can automatically extract the spatial and temporal domain features in the signals, accurately identify the location of small bridge local variations, the accuracy in the test set is 98.8% compared to 92.6% for a traditional ML model, and has better noise immunity performance and robustness to the missing data than the traditional ML model. Finally, the internal inference process of the model was explored and visualized, illustrating that the model has adaptive learning capabilities. [ABSTRACT FROM AUTHOR]

Crack inspection is an essential means to guarantee the healthy service state of infrastructure. However, conventional methods suffer from bottlenecks such as wide blind inspection areas and low efficiency, which make it difficult to reveal the real-time development of cracks. This paper proposes an online crack analysis framework based on deep learning, aiming to effectively broaden the inspectable area and provide real-time feedback on crack development information. The proposed framework includes: (1) Lightweight attention-based crack segmentation U-shape network (CrackSeU). CrackSeU, in comparison to conventional segmentation networks, demonstrates improved fusion between multi-level and multi-scale features. According to the experimental results, CrackSeU achieves enhanced crack segmentation with reduced computation and parameters compared to several advanced network models. (2) Simple and efficient quantitative characterization algorithm. In light of the crack segmentation results, the algorithm can further quantify the actual size of cracks in the physical world, which is convenient for engineers to grasp the structural security status. (3) An online crack monitoring system incorporating the above algorithms. It enables visualization and quantitative monitoring of crack development and establishes a solid foundation for structural safety evaluation. The engineering feasibility and broad application prospect of the proposed framework are further verified by an indoor full-scale concrete beam loading test. • A lightweight attention-based crack segmentation U-shape network is proposed. • A simple and efficient quantitative characterization algorithm is designed. • An online crack intelligent monitoring system is developed. • CrackSeU achieves better performance than some influential segmentation models. [ABSTRACT FROM AUTHOR]