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Determining the impact of pulse-type earthquake characteristics on the vulnerability of base-isolated buildings under non-pounding conditions has yielded conflicting results in previous studies. Moreover, this issue has received less attention for pounding conditions, especially floor-to-floor pounding. Therefore, this study aims to investigate the correlation between pulse-type earthquake characteristics and the seismic response of buildings under both pounding and non-pounding conditions. In the first stage, three base-isolated buildings and one fixed-base building are analyzed separately under 40 pulse-type earthquakes using the nonlinear time history method. Three scenarios are then considered to account for pounding with adjacent buildings. In the first pounding scenario, a base-isolated building with an intermediate moment frame (IMF) is placed between two fixed-base buildings. The second scenario involves changing the base-isolated building's superstructure system to a special moment frame (SMF). Finally, the third scenario increases the base isolation period (Tb) of the base-isolated building used in scenario two. The correlation between earthquake characteristics and the seismic response of buildings is assessed by linear regression and the Pearson correlation coefficient. The results demonstrate that peak ground acceleration (PGA) has a strong correlation with the seismic response of buildings under pounding conditions, while peak ground velocity (PGV) shows a stronger correlation under non-pounding conditions. However, predicting building vulnerability with a single pulse-type earthquake characteristic remains unreliable unless a large number of ground motions are considered. Otherwise, it is crucial to consider the correlation of all earthquake characteristics with seismic responses. [ABSTRACT FROM AUTHOR]

In response to the critical need for enhanced resource management in the construction industry, this research develops an innovative, integrated methodology that synergistically combines Agent-Based Modeling (ABM), Building Information Modeling (BIM), and Bluetooth Low Energy (BLE) technologies. Central to our approach is a sophisticated technological framework that incorporates a Client Early Warning System (CEWS) and a Decision Support System (DSS). These systems facilitate real-time monitoring and management of construction resources, ensuring operational efficiency and optimal resource utilization. Our methodology was empirically validated through a comprehensive case study at Helwan University's College of Engineering. The results demonstrated a significant enhancement in operational efficiency, particularly in resource allocation and progress tracking. Key practical outcomes include the development of a CEWS master dashboard that provides in-depth, real-time insights into project metrics. This dashboard was crucial for managing compliance with health protocols during the COVID-19 pandemic, showcasing the framework's adaptability to critical health standards. Further, the integration of indoor tracking technology revolutionized attendance tracking by replacing outdated manual methods with automated processes. This capability not only underscores the practical applicability of our research but also establishes a new benchmark for future technological advancements in construction project management. Our study sets the stage for subsequent innovations, paving the way for a more connected, efficient, and data-driven approach in the construction industry. [ABSTRACT FROM AUTHOR]

Investigating the impact of near-field ground motions on the fragility curves of multi-span simply supported concrete girder bridges is the main goal of this paper. Fragility curves are valuable tools for evaluating seismic risks and vulnerabilities of bridges. Numerous studies have investigated the impact of ground motions on the fragility curves of bridges. Ground motions are commonly categorized into two sets, based on the distance of the recorded station from the seismic source: far-field and near-field. Studies examining the influence of near-field records on bridge fragility curves vary depending on the specific bridge type and type of fragility curve being analyzed. Due to the widespread use of multi-span simply supported concrete girder bridges in the Central and Southeastern United States, this study makes use of this bridge type. This research investigates the component fragility curves for column curvatures, bearing deformations, and abutment displacements by employing 3-D analytical models and conducting nonlinear time history analysis. These curves illustrate the impact of near-field ground motions on different components. The component fragility curves for two sets of records, 91 near-field ground motions and 78 far-field ground motions, were obtained and compared. These findings demonstrate that near-field ground motions have a greater damaging effect on columns and abutments than far-field earthquakes. When it comes to bearing deformations, the far-field earthquake impact is more severe at lower intensities, whereas the impact of the near-field ground motion is stronger at higher intensities. [ABSTRACT FROM AUTHOR]

Non-destructive testing (NDT) is a crucial method for detecting damages in concrete structures. Structural damage can lead to functional changes, necessitating a range of damage detection techniques. Non-destructive methods enable the pinpointing of the location of the damage without causing harm to the structure, thus saving both time and money. Damaged structures exhibit alterations in their static and dynamic properties, primarily stemming from a reduction in stiffness. Monitoring these changes allows for the determination of the failure location and severity, facilitating timely repairs and reinforcement before further deterioration occurs. A systematic approach to damage detection and assessment is pivotal for fortifying structures and preventing structural collapse, which can result in both financial and human losses. In this study, we employ image processing to categorize damaged beams based on their crack growth and propagation patterns. We also utilize support vector machine (SVM) and k-nearest neighbor (KNN) methods to detect the type, location, and extent of failures in reinforced concrete beams. To provide context and relevance for the laboratory specimens, we will compare our findings to the results from controlled experiments in a controlled laboratory setting. [ABSTRACT FROM AUTHOR]

This article presents a novel approach to estimate the flexural capacity of reinforced concrete-filled composite plate shear walls using an optimized computational intelligence model. The proposed model was developed and validated based on 47 laboratory data points and the Transit Search (TS) optimization algorithm. Using 80% of the experimental dataset, the optimized model was selected by determining the unknown coefficients of the network-based computational structure. The remaining 20% of the data was used to evaluate the accuracy of the model, and the best-performing structure was selected. Furthermore, the final neural network details were subjected to statistical analysis to extract a user-friendly formula, making it easier to apply in practice. The proposed ANN model and the proposed user-friendly formula were then compared with the AISC 341-16 and experimental results and demonstrated their efficacy in predicting the flexural behavior of composite shear walls. Overall, the proposed approach provides a more reliable and efficient framework for estimating the flexural behavior of composite shear walls. [ABSTRACT FROM AUTHOR]

Glass fiber-reinforced polymer (GFRP) rebars are commonly used as an alternative to conventional steel reinforcement in a variety of structural applications due to their superior low cost, strength-to-weight ratio, and durability. However, their mechanical properties after exposure to elevated temperatures, particularly in fire-prone environments, remain a significant concern. To address this concern, the present study focuses on investigating the residual tensile behavior, specifically the tensile strength and elastic modulus, of GFRP rebars exposed to high temperatures that are realistically encountered during fire incidents. The temperature range considered in this analysis spans from 100 °C to 400 °C, with a heating rate of 20 °C/min. The fire duration of 1 h is used. This comprehensive analysis is essential for enhancing our understanding of the performance and applicability of GFRP rebars in fire-prone environments. Based on their actual application in the construction industry, five specimens of three different rebar sizes (16, 20, and 25 mm) were examined for the effect of rebar size on tensile behavior after fire exposure. In addition, the effects were investigated of air- and water-cooling methods on residual tensile behavior. The nominal tensile strength, elastic modulus, and ultimate strain of GFRP rebars at ambient temperature are 930 MPa, 50.2 GPa and 1.85%, respectively. The test results indicated that as the temperature increased to 400 °C, the ultimate tensile strength of the GFRP bars decreased by up to 55%, while the ultimate strain increased by up to 44%, regardless of the cooling method. In addition, when rebars of sizes 16–25 mm were subjected to a 400 °C fire treatment, the smaller the rebar, the greater the percentage of ultimate tensile and strain reduction. These findings hold great significance for the utilization of GFRP bars within the construction industry. This study offers valuable insights into the design of fire-resilient structures, emphasizing the importance of considering rebar size and cooling methods due to their impact on the post-fire tensile strength and strain of GFRP rebars. [ABSTRACT FROM AUTHOR]

Suspension bridges' in-plane extended configuration makes them vulnerable to wind-induced vibrations. Vortex shedding is a kind of aerodynamic phenomenon causing a bridge to vibrate in vertical and torsional modes. Vortex-induced vibrations disturb the bridge's serviceability limit, which is not favorable, and in the long run, they can cause fatigue damage. In this condition, vibration control strategies seem to be essential. In this paper, the performance of a tuned mass damper (TMD) is investigated under the torsional vortex phenomenon for an ultra-span streamlined twin-box girder suspension bridge. In this regard, the sensitivity of TMD parameters was addressed according to the torsional responses of the suspension bridge, and the reached appropriate ranges are compared with the outputs provided by genetic algorithm. The results indicated that the installation of three TMDs could control all the vulnerable modes and reduce the torsional rotation by up to 34%. [ABSTRACT FROM AUTHOR]

Nonstructural components (NSCs) are the systems that are attached to the floors of a building structure. NSCs have become critical in sustaining post-earthquake functionality while constructing seismic-resilient structures. The seismic behavior of the NSCs primarily depends upon the behavior of the structure to which the NSC is attached. Building structures are subjected to earthquake loads and behave differently when the supporting soil type varies. In light of this, this study investigates the seismic demands on NSC attached to the floors of an elastic-reinforced concrete building frame supported by different soil types. The present study considered a regular building frame and a building frame with mass irregularity on the lower story. A total of 3 sets of 11 horizontal spectral-matched ground motions consistent with each soil type are considered. Floor response spectra (FRS) can be used to measure the seismic load on non-structural components. Primarily, it was found that the ordinates of FRS depend on the floor height, the vibration periods of the building, and the soil type. The presence of mass irregularity at the lower story amplified the floor response at all floor levels. Additionally, the values of floor spectral acceleration increase as soil flexibility increases. The amplification factors are critical for generating the floor response spectra, and their variation along the building height is discussed. The floor acceleration was found to vary non-linearly with the height of the building. Finally, artificial neural networks (ANNs) are employed to develop the prediction models for dynamic amplification factors. The results calculated by the dynamic time history analyses are utilized to validate the proposed prediction models. [ABSTRACT FROM AUTHOR]

In this study, the employment of the gene expression programming (GEP) technique in forecasting models on sustainable construction materials including mineral admixtures and civil engineering quantities (e.g., compressive strength), was investigated. Compared to the artificial neural networks (ANN) based formulations, which are often too complicated to be used, GEP-based derived models provide estimation equations that are reasonably simple and may be used for practical design purposes and even for hand calculations. Many popular models, such as best-fitted curves based on regression analyses, multi-linear regression (MLR), multinomial logistic regression (MNLR), and multinomial variate regression (MNVR), can also be used for construction materials properties modeling. However, due to the nonlinearity and complexity of the target properties, the models established using linear regression analyses may not reveal the precise behavior. Additionally, regression models lack generality, and this comes from the fact that some functions are defined for regression in classical regression techniques; while in the GEP approach, there is no predefined function to be considered, and it reproduces or omits various combinations of parameters to provide the formulation that fits the experimental outcomes. If the input parameters can be evaluated through simple laboratory or rapid measurements, and also a comprehensive experimental database is made available, the models can be constructed with optimal flexibility. Flexibility in choosing the complexity and fitness functions, such as RMSE, MAE, and MSE, might lead to better performance of the approach and well-capturing the governing pattern behind the material's characteristics. There may be minor inaccuracies with this technique; however, the explicit mathematical expressions, which can be easily implemented in the design and analysis process, may cover the minor inaccuracies compared to ANN, support vector machine (SVM), and other intelligent approaches. Based on the presented study, sometimes it would be better to provide more than one GEP model and consider different combinations of input contributing variables to afford the possible initial feed for a more settled and comprehensive model. Mostly, GEP's strengths as a superior machine learning technique in modeling the behavior of construction materials including mineral admixtures, leading to innovative solutions in civil engineering, have been presented. [ABSTRACT FROM AUTHOR]

Due to the corrosion problem in reinforced concrete structures, the use of fiber-reinforced polymer (FRP) bars may be preferred in place of traditional reinforcing steel. FRP bars are used in concrete constructions to boost the strength of structural elements and retain their longevity. In this study, the axial load carrying capacity (ALCC) of the FRP-reinforced concrete columns has been evaluated using analytical, as well as machine learning, models. A total of fourteen popular analytical models and one proposed machine learning-based model were used to estimate the ALCC of the concrete columns. The proposed machine learning model is based on an artificial neural network (ANN) method. The performance of the ANN, as well as the analytical models, are assessed using six different performance indices. The R-value of the developed ANN model is 0.9758, followed by an NS value of 0.9513. It has been found that the mean absolute percentage error of the best-fitted analytical model is 328.71% higher than the ANN model, and the root-mean-square error value of the best-fitted analytical model is 211.97% higher than the ANN model. The evaluated data demonstrate that the proposed ANN model performs better than the other analytical models. The developed model is quick and easy-to-use to estimate the axial capacity of the FRP-reinforced concrete columns. [ABSTRACT FROM AUTHOR]

This study proposes FastCrackNet, a computationally efficient crack-detection approach. Instead of a computationally costly convolutional neural network (CNN), this technique uses an effective, fully connected network, which is coupled with a 2D-wavelet image transform for analyzing and a locality sensitive discriminant analysis (LSDA) for reducing the number of features. The algorithm described here is used to detect tiny concrete cracks in two noisy adverse conditions and image shadows. By combining wavelet-based feature extraction, feature reduction, and a rapid classifier based on deep learning, this technique surpasses other image classifiers in terms of speed, performance, and resilience. In order to evaluate the accuracy and speed of FastCrackNet, two prominent pre-trained CNN architectures, namely GoogleNet and Xception, are employed. Findings reveal that FastCrackNet has better speed and accuracy than the other models. This study establishes performance and computational thresholds for classifying photos in difficult conditions. In terms of classification efficiency, FastCrackNet outperformed GoogleNet and the Xception model by more than 60 and 80 times, respectively. Furthermore, FastCrackNet's dependability was proved by its robustness and stability in the presence of uncertainties produced by network characteristics and input images, such as input image size, batch size, and input image dimensions. [ABSTRACT FROM AUTHOR]

The compressive capacity of the column is one of the key parameters in the design. The importance of such structural members and their performance under load conditions are very effective in the overall behavior of the structure, and its failure can lead to the collapse of the entire structure. Therefore, determining the capacity of columns is considered an important issue in structural problems. Thus, this article presents an applicable computational framework to predict the compression capacity of stirrups-confined concrete. A machine learning model based on neuro-fuzzy systems was considered to formulate the proposed model. For this purpose, some experimental datasets were gathered from the literature to tune the unknown parameters of the model and evaluate its accuracy. The target, the ratio of the ultimate axial capacity to bearing area, was predicted with consideration of the column properties, including the compressive strength of concrete, stirrups section area, dimension of the stirrups, and the column section. The results showed that the proposed framework could be used as an applicable technique to determine the compressive capacity of the stirrups-confined concrete columns. [ABSTRACT FROM AUTHOR]

Infrastructure design, construction and development experts are making frantic efforts to overcome the overbearing effects of greenhouse gas emissions resulting from the continued dependence on the utilization of conventional cement as a construction material on our planet. The amount of CO2 emitted during cement production, transportation to construction sites, and handling during construction activities to produce concrete is alarming. The present research work is focused on proposing intelligent models for fly ash (FA)-based concrete comprising cement, fine and coarse aggregates (FAg and CAg), FA, and water as mix constituents based on environmental impact (P) considerations in an attempt to foster healthier and greener concrete production and aid the environment. FA as a construction material is discharged as a waste material from power plants in large amounts across the world. Its utilization as a supplementary cement ensures a sustainable waste management mechanism and is beneficial for the environment too; hence, this research work is a multi-objective exercise. Intelligent models are proposed for multiple concrete mixes utilizing FA as a replacement for cement to predict 28-day concrete compressive strength and life cycle assessment (LCA) for cement with FA. The data collected show that the concrete mixes with a higher amount of FA had a lesser impact on the environment, while the environmental impact was higher for those mixes with a higher amount of cement. The models which utilized the learning abilities of ANN (-BP, -GRG, and -GA), GP and EPR showed great speed and robustness with R2 performance indices (SSE) of 0.986 (5.1), 0.983 (5.8), 0.974 (7.0), 0.78 (19.1), and 0.957 (10.1) for Fc, respectively, and 0.994 (2.2), 0.999 (0.8), 0.999 (1.0), 0.999 (0.8), and 1.00 (0.4) for P, respectively. Overall, this shows that ANN-BP outclassed the rest in performance in predicting Fc, while EPR outclassed the others in predicting P. Relative importance analyses conducted on the constituent materials showed that FA had relatively good importance in the concrete mixes. However, closed-form model equations are proposed to optimize the amount of FA and cement that will provide the needed strength levels without jeopardizing the health of the environment. [ABSTRACT FROM AUTHOR]

Much research has been conducted and published on the examination of the behavior of reinforced steel and concrete structures with a FRP system. Nevertheless, the performance of FRP differs from that of FRCM, particularly at high temperature and ultimate strength. The present study provides a review of previous research on structural elements (viz. beams, columns, arches, slabs, and walls) retrofitted with FRCM systems, taking account of various parameters, such as layers, composite types, configurations, and anchors for controlling or delaying failure modes (FMs). Additionally, this paper discussed the details of different FMs observed during experimental tests, such as crushed concrete or bricks, fiber debonding from substrate materials, slippage, fiber rupture, and telescopic failure for strengthened specimens. Moreover, this paper investigated where and how fractures may develop in structural elements retrofitted with the FRCM system under various retrofit scenarios. To this end, in addition to the review of the relevant literature, a large dataset has been compiled from different (RC) structural elements and masonry members. Next, a relationship is developed between failure modes (FMS) and influential parameters, i.e., the number of layers and the type of composite, based on this dataset. This can be used as a benchmark example in future studies, as there is no such basis available in the literature, to the best of the authors' knowledge. [ABSTRACT FROM AUTHOR]

Various cases of two adjacent multi-story buildings with different numbers of floors and equal or unequal foundation levels under earthquake loading and considering soil-structure interaction (SSI) are investigated. A two-dimensional model for each case of the two adjacent unequal buildings without separation distance is used and a special arrangement of contact elements in the contact zone is employed to fulfil all possible deformation contact modes which take place under seismic loading. The soil is modelled by two-dimensional 4-node elements which are in contact with the foundations of the two adjacent buildings. This paper studies the earthquake-induced double pounding that takes place between the two adjacent unequal height buildings in some upper points at superstructure in the contact zone and also at foundation level, considering soil-structure interaction (SSI). The double pounding and the soil-structure interaction (SSI) effects should be taken into consideration in the seismic analysis of adjacent buildings especially those with different heights and different foundation levels. [ABSTRACT FROM AUTHOR]

An existing riverine platform in Egypt, together with its pile group foundation, is analyzed under environmental loads using 3D FEM structural analysis software incorporating soil-pile interaction. The interaction between the transfer plate and the piles supporting the platform is investigated. Two connection conditions were studied assuming fixed or hinged connection between the piles and the reinforced concrete platform for the purpose of comparison of the structural behavior. The analysis showed that the fixed or hinged connection condition between the piles and the platform altered the values and distribution of displacements, normal force, bending moments, and shear forces along the length of each pile. The distribution of piles in the pile group affects the stress distribution on both the soil and platform. The piles were found to suffer from displacement failure rather than force failure. Moreover, the resulting bending stresses on the reinforced concrete plate in the case of a fixed connection between the piles and the platform were almost doubled and much higher than the allowable reinforced concrete stress and even exceeded the ultimate design strength and thus the environmental loads acting on a pile-supported riverine offshore platform may cause collapse if they are not properly considered in the structural analysis and design. [ABSTRACT FROM AUTHOR]

Soil-structure interaction (SSI) could affect the seismic response of structures. Since liquid storage tanks are vital structures and must continue their operation under severe earthquakes, their seismic behavior should be studied. Accordingly, the seismic response of two types of steel liquid storage tanks (namely, broad and slender, with aspect ratios of height to radius equal to 0.6 and 1.85) founded on half-space soil is scrutinized under different earthquake ground motions. For a better comparison, the six considered ground motions are classified, based on their pulse-like characteristics, into two groups, named far and near fault ground motions. To model the liquid storage tanks, the simplified mass-spring model is used and the liquid is modeled as two lumped masses known as sloshing and impulsive, and the interaction of fluid and structure is considered using two coupled springs and dashpots. The SSI effect, also, is considered using a coupled spring and dashpot. Additionally, four types of soils are used to consider a wide variety of soil properties. To this end, after deriving the equations of motion, MATLAB programming is employed to obtain the time history responses. Results show that although the SSI effect leads to a decrease in the impulsive displacement, overturning moment, and normalized base shear, the sloshing (or convective) displacement is not affected by such effects due to its long period. [ABSTRACT FROM AUTHOR]

Steel plate shear walls (SPSWs) have advantages such as high elastic stiffness, stable hysteresis behavior, high energy absorption capacity, and decent ductility. However, one of the main drawbacks of SPSWs is their buckling under lateral loading. To address this issue, a simple and practical solution in the form of using a trapezoidal plate moment connection (PMC) and a narrow gap between the infill plate and columns is presented. The PMC will act as an energy absorber, similar to a yielding steel plate, and keep the other structural members in an elastic state. Extensive three-dimensional finite element (FE) models of the SPSW system were investigated under monotonic and cyclic loading. The results revealed that by separating the infill plate from the vertical boundary elements and using two vertical edge stiffeners at both edges of the wall, the same lateral bearing capacity of the conventional system can be achieved. In addition, by increasing the thickness of the PMC from 6.5 to 26 mm, the load-bearing capacity, energy dissipation, and elastic stiffness increased approximately 2, 2.5, and 3.2 times, respectively. It was also found that the flexural capacity ratio of the connection to the beam had little effect on the overall force–displacement behavior. However, it can affect the system failure mechanism. Finally, the tension field inclination angle for such SPSWs was proposed in the range of 30 to 35°. [ABSTRACT FROM AUTHOR]