The present study concerns the corrosion mitigation of electrochemical chloride extraction (ECE) in concrete structure. Concrete specimen was fabricated with 5.0% chloride in cast, while the other specimen was exposed to 4.0M NaCl solution for 1 year to accelerate corrosion of steel. Then, the ECE was applied to the concrete specimen with 1000 mA/m² of the current density for 2, 4 and 8 weeks, respectively. During the ECE, the corrosion current density and corrosion potential were regularly monitored. As a result, the ECE was very effective in mitigating the degree of corrosion on the steel surface. The corrosion current density was significantly reduced from thousands to decades mA/m², while the corrosion potential was mostly shifted to positive direction. Assuming that the corrosion starts at 1.0 mA/m² of the corrosion current density or/and -275 mV vs SCE of the corrosion potential, the ECE could not fully achieve the repassivation of the steel, although its degree was lowered more or less depending on the duration of the treatment and type of chloride contamination. A visual examination confirmed that an increase in the duration of the treatment could lower the rust formation, but never fully removed all rust stains.

To date, shell-solid and fibre element model analysis are the most commonly used methods to investigate the seismic performance of concrete-filled steel tube (CFST) bridge piers. However, most existing research does not consider the loss of bearing capacity caused by the fracture of the outer steel tube. To fill this knowledge gap, a refined finite element (FE) model considering the ductile damage of steel tubes and the behaviour of infilled concrete with cracks is established and verified against experimental results of unidirectional, bidirectional cyclic loading tests and pseudo-dynamic loading tests. In addition, a parametric study is conducted to investigate the seismic performance of CFST bridge piers with different concrete strength, steel strength, axial compression ratio, slenderness ratio and infilled concrete height using the proposed model. The validation shows that the proposed refined FE model can effectively simulate the residual displacement of CFST bridge piers subjected to highintensity earthquakes. The parametric analysis indicates that CFST piers hold sufficient strength reserves and sound deformation capacity and, thus, possess excellent application prospects for bridge construction in high-intensity areas.

The mechanical behaviour of layered concrete samples containing an internal crack was numerically studied by modelling the geo-mechanical specimens in the particle flow code in two dimensions (PFC2D). The numerical modelling software was calibrated with the experimental results of the Brazilian tensile strengths gained from the laboratory disc-type specimens. Then, the samples with the bedding layers and internal notch were numerically simulated with PFC2D under uniaxial compressive loading. In each specimen, the layers' thickness was 10 mm but the layer's inclination angle was changed to 0°, 30°, 60°, 90°, 120° and 150°. Of course, the layers'interfaces are considered to have very low strengths. The internal notch was kept at 3 cm in length however, its inclination angle was changed to 0°, 40°, 60° and 90°. Therefore, a total, of 24 numerical models were made to study the failure mechanism of the layered concrete samples. Considering these results, it has been concluded that the inclination angles of both internal crack and bedding layers affect the failure mechanism and uniaxial compressive strength of the concrete.

The modeling efficiency of concrete meso-models close to real concrete is one of the important issues that limit the accuracy of mechanical simulation. In order to improve the modeling efficiency and the closeness of the numerical aggregate shape to the real aggregate, this paper proposes a method for generating a two-dimensional concrete meso-model based on pixel matrix and skeleton theory. First, initial concrete model (a container for placing aggregate) is generated using pixel matrix. Then, the skeleton curve of the residual space that is the model after excluding the existing aggregate is obtained using a thinning algorithm. Finally, the final model is obtained by placing the aggregate according to the curve branching points. Compared with the traditional Monte Carlo placement method, the proposed method greatly reduces the number of overlaps between aggregates by up to 95%, and the placement efficiency does not significantly decrease with increasing aggregate content. The model developed is close to the actual concrete experiments in terms of aggregate gradation, aspect ratio, asymmetry, concavity and convexity, and old-new mortar ratio, cracking form, and stress-strain curve. In addition, the cracking loss process of concrete under uniaxial compression was explained at the mesoscale.

This study is aimed at identifying structural element stiffness influence on vertical earthquake response of mid-rise R/C frame buildings. To this aim, a mid-rise RC building structure is designed as per the new Turkish Seismic Code for Buildings-2018, and 3D FE model of the building is established. Based on the established FE model, a total number of six buildings are considered depending on certain percentage increase in beam, slab, and column. The time-history response analyses (THA) are performed separately for only horizontal (H) and horizontal +vertical (H+V) earthquake motions to make a comparison between the load cases. The analysis results are presented comparatively in terms of the monitoring parameters of the base overturning moment (M_o), the top-story lateral displacement (d_L) and the top-story vertical displacement (d_V). The obtained results reveal that the base overturning moment and the top-story vertical displacement are affected by vertical earthquake motion regardless of the increase in the dimension of beam, slab, and column. However, vertical earthquake motion is not effective on the top-story lateral displacement due to no change between H and H+V load. The dimensional increase in either slab or beam leads to a considerable increase in the base overturning moment and the top-story vertical displacement while causing decrease in the top-story lateral displacement. In addition, the dimensional increase in column has a positive effect on the decrease in the monitoring parameters of the base overturning moment (M_o), the top-story lateral displacement (d_L) and the top-story vertical displacement (d_V).

This paper proposes a study of cement replacement with rice husk ash (RHA) and eggshell ash (ESA) for enhanced mechanical properties of geopolymer (GP) concrete with and without admixture. The main objective is to investigate the mechanical properties of GP with various replacement levels of Pozzolana Portland cement by RHA and ESA. The GP resistance to durability is examined and impact of ash materials on concrete's durability performance is determined. The environmental benefits of using agricultural waste materials in GP manufacturing minimize cement usage and CO2 emissions. The goal is to assess value of RHA-ESA of building material, paving stones for structures to lessen environmental impact. The novelty lies in use of ESA and RHA as partial replacements for cement and investigation of admixtures to enhance concrete properties, and reduce environmental impact. The research contributes by introducing a novel approach to reducing cement consumption by using ESA and RHA to address environmental concerns. It also explores the potential benefits of admixtures improving concrete performance and reducing environmental pollution. A study is carried with and without impacts of admixture to find compressive strength of GP cubes. The cement has been replaced by RHA and ESA in the range of (2.5%+7.5%, 5%+5%, 7.5%+2.5) by weight of cement for M20 mix. The compressive strength (CS) and split tensile strength (STS) at 7days, 14 days and 28 days is obtained as 21 N/mm2 at 7.5%RHA+2.5%ESA and 2.3 at 7.5%RHA+2.5%ESA, 24 N/mm2 at 7.5%RHA+2.5%ESA and 2.3 at 7.5%RHA+2.5%ESA, 28 N/mm2 at 7.5%RHA+2.5%ESA and 2.8 at 7.5%ESA respectively with normal curing condition.

This study presents a macro-modeling procedure for nonlinear finite element analysis of reinforced and prestressed concrete panels under blast loading. The analysis procedure treats cracked concrete as an orthotropic material based on a smeared rotating crack model within the context of total-load secant stiffness-based formulation. A direct time integration method compatible with the analysis formulation is adapted to solve the dynamic equation of motion. Considerations are made to account for strain rate effects. The analysis procedure is verified by modeling 14 blast tests from various sources reported in the literature including a blast simulation contest. The analysis results are compared against those obtained from experiments, simplified single-degree-of-freedom (SDOF) methods, and sophisticated hydrocodes. It is demonstrated that the smeared crack macro-modeling approach is a viable alternative analysis procedure that gives more information about the structural behavior than SDOF methods, but does not require detailed micro-modeling and extensive material characterization typically needed with hydrocodes.

This study presents an innovative AI-driven approach to assess the ultimate axial load in Double-Skinned Profiled Steel sheet Composite Walls (DPSCWs). Utilizing a dataset of 80 entries, seven input parameters were employed, and various AI techniques, including Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Decision Tree with AdaBoost Regression, Random Forest Regression, Gradient Boost Regression Tree, Elastic Net Regression, Ridge Regression, and LASSO Regression, were evaluated. Decision Tree Regression and Random Forest Regression emerged as the most accurate models. The top three performing models were integrated into a hybrid approach, excelling in accurately estimating DPSCWs' ultimate axial load. This adaptable hybrid model outperforms traditional methods, reducing errors in complex scenarios. The validated Artificial Neural Network (ANN) model showcases less than 1% error, enhancing reliability. Correlation analysis highlights robust predictions, emphasizing the importance of steel sheet thickness. The study contributes insights for predicting DPSCW strength in civil engineering, suggesting optimization and database expansion. The research advances precise load capacity estimation, empowering engineers to enhance construction safety and explore further machine learning applications in structural engineering.