Search Results (1,054)

Reinforced concrete (RC) deep beams constructed with low-strength concrete are susceptible to sudden splitting failures in the strut region due to shear–compression stresses. To mitigate this vulnerability, various strengthening techniques, including steel plates, fiber-reinforced polymer sheets, and cementitious composites, have been explored to confine the strut area. This study investigates the structural performance of RC deep beams with low-strength concrete, strengthened externally using an Engineered Cementitious Composite (ECC) layer. To ensure effective confinement and uniform shear distribution, shear reinforcement was provided at equal intervals with configurations of zero, one, and two vertical shear reinforcements. Four-point bending tests revealed that the ECC layer significantly enhanced the shear capacity, increasing load-carrying capacity by 51.6%, 54.7%, and 46.7% for beams with zero, one, and two shear reinforcements, respectively. Failure analysis through non-linear finite element modeling corroborated experimental observations, confirming shear–compression failure characterized by damage in the concrete struts. The strut-and-tie method, modified to incorporate the tensile strength of ECC and shear reinforcement actual stress values taken from the FE analysis, was used to predict the shear capacity. The predicted values were within 10% of the experimental results, underscoring the reliability of the analytical approach. Overall, this study demonstrates the effectiveness of ECC in improving shear performance and mitigating strut failure in RC deep beams made with low-strength concrete. Full article

To reduce the carbon footprint of the concrete industry and promote a circular economy, this study explores the reuse of waste materials such as glass powder (GP) and nitrile rubber (NR) fibres in concrete. However, the inclusion of these waste materials results in lower compressive strength compared to conventional concrete, limiting their application to non-structural elements. To overcome this limitation, this study adopts the concept of confined concrete by developing concrete-filled steel tube (CFST) stub columns. In total, twelve concrete mix variations were developed, with and without steel tube confinement. GP was utilised at replacement levels of 10–30% by weight of cement, while NR fibres were introduced at 0.5% and 1% by volume of concrete. The findings demonstrate that the incorporation of GP and NR fibres leads to a reduction in compressive strength, with a compounded effect observed when both materials are combined. Steel confinement within CFST columns effectively mitigated the strength reductions, restoring up to 17% of the lost capacity and significantly improving ductility and energy absorption capacity. All CFST columns exhibited consistent local outward buckling failure mode, irrespective of the concrete mix variations. A comparison with predictions from existing design codes and empirical models revealed discrepancies, underscoring the need for refined design approaches for CFST columns incorporating sustainable concrete infill. This study contributes valuable insights into the development of eco-friendly, high-performance structural systems, highlighting the potential of CFST technology in facilitating the adoption of waste materials in the construction sector. Full article

Chloride ion penetration is one of the most aggressive threats to reinforced concrete, as it triggers the electrochemical corrosion of steel reinforcement, compromising structural integrity and durability. Chloride ingress occurs through the porous structure of concrete, making permeability control crucial for enhancing structural longevity. Fiber-reinforced concrete (FRC) is widely used to improve durability; however, the effects of different fiber types on chloride resistance remain unclear. This study examines the influence of glass and polypropylene fibers on concrete’s microstructure and chloride penetration resistance. Cylindrical specimens were prepared, including a reference mix without fibers and mixes with 0.25% and 0.50% fiber content by volume. Both fiber types were tested for chloride resistance. The accelerated non-steady-state migration method was employed to determine the resistance coefficients to chloride ion penetration, while X-ray macrofluorescence (MacroXRF) mapped the chlorine infiltration depth in the samples. Compressive strength decreased in all fiber-reinforced samples, with 0.50% glass fiber leading to a 56% reduction in strength. Nevertheless, the XRF results showed that a 0.25% fiber content significantly reduced chloride penetration, with polypropylene fibers outperforming glass fibers. These findings highlight the critical role of fiber type and volume in improving concrete durability, offering insights for designing long-lasting FRC structures in chloride-rich environments. Full article

This article presents a probabilistic methodology for assessing corrosion initiation in reinforced concrete structures exposed to chloride ingress. The approach addresses key limitations of conventional analytical models by accounting for non-diffusive barriers and incorporating a rigorous statistical treatment of censored data to mitigate biases introduced by limited simulation durations. A combination of analytical solutions for diffusion from opposite sides with time-dependent boundary conditions is also proposed and validated. The probabilistic study includes the depassivation assessment of a hollow pier section. The blocking effect caused by rebars is statistically characterised through correction factors derived from finite element simulations. These factors are used to adjust analytical solutions, which are computationally inexpensive. Results show that neglecting the rebar blocking effect can overestimate the mean corrosion initiation time by up to 42%, while the use of censored data reduces bias in lifetime estimates. The observed frequency of censored events reached up to 20% when simulations were truncated at 100 years. The corrected analytical models closely match the finite element results, statistically validating their application. The case study indicates premature corrosion initiation (less than 10 years to achieve target reliability), underscoring the need to better reconcile the desired levels of reliability with realistic input parameters for depassivation. Full article

Non-destructive evaluation of reinforced concrete structures is essential for effective maintenance and safety assessments. This study explores the combined use of active microwave thermography and deep learning to detect and localize steel reinforcement within concrete elements. Numerical simulations were developed to model the thermal response of reinforced concrete subjected to microwave excitation, generating synthetic thermal images representing the surface temperature patterns of reinforced concrete, influenced by subsurface steel reinforcement. These images served as training data for a deep neural network designed to identify and localize rebar positions based on thermal patterns. The model was trained exclusively on simulation data and subsequently validated using experimental measurements obtained from large-format concrete slabs incorporating a structured layout of embedded steel reinforcement bars. Surface temperature distributions obtained through infrared imaging were compared with model predictions to evaluate detection accuracy. The results demonstrate that the proposed method can successfully identify the presence and approximate location of internal reinforcement without damaging the concrete surface. This approach introduces a new pathway for contactless, automated inspection using a combination of physical modeling and data-driven analysis. While the current work focuses on rebar detection and localization, the methodology lays the foundation for broader applications in non-destructive testing of concrete infrastructure. Full article

Experimental tests on confined concrete specimens are essential to characterize the mechanisms activated under varying degrees of confinement. Such characterization is critical for understanding how full, partial, and non-uniform wrapping configurations influence strength and ductility enhancements. This study investigates the compressive behavior of concrete cylinders (160 mm × 320 mm) confined using full, partial, and non-uniform carbon fiber-reinforced polymers (CFRP) configurations. In the first phase, all wrapping schemes were applied with equivalent quantities of CFRP, enabling a direct performance comparison under material parity. The results indicate that non-uniform confinement (NUC) achieved approximately 15% higher axial strength than full confinement (FC2) using the same amount of CFRP. In the second phase, the NUC configuration was tested with 25% less CFRP material, yet the reduction in strength was limited to about 3%, demonstrating its superior efficiency. A new predictive model was developed to estimate peak axial stress and strain in CFRP-confined concrete cylinders. Compared to existing models, the proposed model demonstrated greater predictive accuracy (R² = 0.98 for stress and 0.91 for strain) and reduced error metrics (RMSE and scatter index). ANOVA confirmed the statistical significance of the model’s predictions (p < 0.00001 for stress, p = 0.002 for strain). These findings highlight the performance advantages and material efficiency of non-uniform CFRP confinement and support the utility of the proposed model as a practical design tool for developing advanced confinement strategies in structural engineering. Full article

The escalating global demand for fresh water, driven by urbanization and industrial growth, underscores the need for sustainable water management, particularly in the water-intensive construction sector. Although prior studies have primarily concentrated on treated wastewater, the practical viability of utilizing untreated wastewater has not been thoroughly investigated—especially in developing nations where treatment expenses frequently impede actual implementation, even for non-structural uses. While prior research has focused on treated wastewater, the potential of untreated or partially treated wastewater from diverse industrial sources remains underexplored. This study investigates the feasibility of incorporating wastewater from textile, sugar mill, service station, sewage, and fertilizer industries into concrete paver block production. The novelty lies in a dual approach, combining experimental analysis with XGBoost-based machine learning (ML) models to predict the impact of key physicochemical parameters—such as Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and Hardness—on mechanical properties like compressive strength (CS), water absorption (WA), ultrasonic pulse velocity (UPV), and dynamic modulus of elasticity (DME). The ML models showed high predictive accuracy for CS (R² = 0.92) and UPV (R² = 0.97 direct, 0.99 indirect), aligning closely with experimental data. Notably, concrete pavers produced with textile (CP-TXW) and sugar mill wastewater (CP-SUW) attained 28-day compressive strengths of 47.95 MPa and exceeding 48 MPa, respectively, conforming to ASTM C936 standards and demonstrating the potential to substitute fresh water for non-structural applications. These findings demonstrate the viability of using untreated wastewater in concrete production with minimal treatment, offering a cost-effective, sustainable solution that reduces fresh water dependency while supporting environmentally responsible construction practices aligned with SDG 6 (Clean Water and Sanitation) and SDG 12 (Responsible Consumption and Production). Additionally, the model serves as a practical screening tool for identifying and prioritizing viable wastewater sources in concrete production, complementing mandatory laboratory testing in industrial applications. Full article

Sustainability in the construction sector has become a fundamental objective for mitigating escalating environmental challenges; given that concrete is the most widely used man-made material, extending its service life is therefore critical. Among durability concerns, magnesium sulfate (MgSO₄) attack is particularly deleterious to concrete structures. Therefore, this study investigates the short- and long-term performance of concrete produced with copper slag (CS)—a massive waste generated by copper mining activities worldwide—employed as a supplementary cementitious material (SCM), together with recycled coarse aggregate (RCA), obtained from concrete construction and demolition waste, when exposed to MgSO₄. CS was used as a 15 vol% cement replacement, while RCA was incorporated at 0%, 20%, 50%, and 100 vol%. Compressive strength, bulk density, water absorption, and porosity were measured after water curing (7–388 days) and following immersion in a 5 wt.% MgSO₄ solution for 180 and 360 days. Microstructural characteristics were assessed using scanning electron microscopy (SEM), X-ray diffraction (XRD), thermogravimetric analysis with its differential thermogravimetric derivative (TG-DTG), and Fourier transform infrared spectroscopy (FTIR) techniques. The results indicated that replacing 15% cement with CS reduced 7-day strength by ≤10%, yet parity with the reference mix was reached at 90 days. Strength losses increased monotonically with RCA content. Under MgSO₄ exposure, all mixtures experienced an initial compressive strength gain during the short-term exposures (28–100 days), attributed to the pore-filling effect of expansive sulfate phases. However, at long-term exposure (180–360 days), a clear strength decline was observed, mainly due to internal cracking, brucite formation, and the transformation of C–S–H into non-cementitious M–S–H gel. Based on these findings, the combined use of CS and RCA at low replacement levels shows potential for producing environmentally friendly concrete with mechanical and durability performance comparable to those of concrete made entirely with virgin materials. Full article

Non-uniform temperature fields are developed during the welding of studs in steel–concrete composite bridges. Due to uneven thermal expansion and reversible solid-state phase transformations between ferrite/martensite and austenite structures within the materials, residual stresses are induced, which ultimately degrades the mechanical performance of the structure. For a better understanding of the influence on steel–concrete composite bridges’ structural behavior by residual stress, accurate simulation of the spatio-temporal temperature distribution during stud welding under practical engineering conditions is critical. This study introduces a precise simulation method for temperature evolution during stud welding, in which the Gaussian heat source model was applied. The simulated results were validated by real welding temperature fields measured by the infrared thermography technique. The maximum error between the measured and simulated peak temperatures was 5%, demonstrating good agreement between the measured and simulated temperature distributions. Sensitivity analyses on input current and plate thickness were conducted. The results showed a positive correlation between peak temperature and input current. With lower input current, flatter temperature gradients were observed in both the transverse and thickness directions of the steel plate. Additionally, plate thickness exhibited minimal influence on radial peak temperature, with a maximum observed difference of 130 °C. However, its effect on peak temperature in the thickness direction was significant, yielding a maximum difference of approximately 1000 °C. The thermal influence of group studs was also investigated in this study. The results demonstrated that welding a new stud adjacent to existing ones introduced only minor disturbances to the established temperature field. The maximum peak temperature difference before and after welding was approximately 100 °C. Full article

This study investigates the influence of varying water-to-cement (W/C) ratios and fine aggregate compositions on the performance of concrete incorporating expanded perlite aggregate (EPA) as a lightweight alternative to natural sand. A total of eighteen concrete mixes were produced, each with different W/C ratios and fine-to-coarse aggregate (FA/CA) ratios, and evaluated for workability, compressive strength, flexural and tensile strength, water absorption, density, and thermal conductivity. Perlite was used to fully replace natural sand in half of the mixes, allowing a direct assessment of its effects across low-, medium-, and high-strength concrete formulations. The results demonstrate that EPA can improve workability and reduce both density and thermal conductivity, with variable impacts on mechanical performance depending on the W/C and FA/CA ratios. Notably, higher cement contents enhanced the internal curing effect of perlite, while lower-strength mixes experienced a reduction in compressive strength when perlite was used. These findings suggest that expanded perlite can be effectively applied in structural and non-structural concrete with optimized mix designs, supporting the development of lightweight, thermally efficient concretes. Mixture W16-100%EPS was considered the ideal mix because its compressive strength at the age of 65 days 44.2 MPa and the reduction in compressive strength compared to the reference mix 14% and the reduction in density 5.4% compared with the reference mix and the reduction in thermal conductivity 14% compared with the reference mix. Full article

Post-tensioned (PT) reinforced concrete bridges are particularly vulnerable structures, as the deterioration of internal tendons is often difficult to detect using conventional inspection methods or visual assessments. This paper introduces a practical framework for ranking non-destructive testing (NDT) techniques employed to assess PT systems. The ranking is based on four performance categories: measurement accuracy, ease of use, cost, and impact of disruption to bridge operations on traffic. For each NDT technique, a score is assigned for each evaluation category, and the final ranking is determined using the weighted sum model (WSM). This approach enables the final assessment to reflect the priorities of different decision-making contexts defined by the end-user such as accuracy-oriented, cost-oriented, and impact-oriented scenarios. The proposed method is then applied to an existing bridge in order to practically demonstrate its effectiveness and the flexibility of the proposed criteria. Full article

This paper presents a formulation for the dynamic analysis of dam–reservoir–foundation systems, employing a coupled finite element model that integrates displacements and reservoir pressures. An innovative coupled approach, without separating the solid and fluid equations, is proposed to directly solve the single non-symmetrical governing equation for the whole system with non-proportional damping. For the modal analysis, a state–space method is adopted to solve the coupled eigenproblem, and complex eigenvalues and eigenvectors are computed, corresponding to non-stationary vibration modes. For the seismic analysis, a time-stepping method is applied to the coupled dynamic equation, and the stress–transfer method is introduced to simulate the nonlinear behavior, innovatively combining a constitutive joint model and a concrete damage model with softening and two independent scalar damage variables (tension and compression). This formulation is implemented in the computer program DamDySSA5.0, developed by the authors. To validate the formulation, this paper provides the experimental and numerical results in the case of the Cahora Bassa dam, instrumented in 2010 with a continuous vibration monitoring system designed by the authors. The good comparison achieved between the monitoring data and the dam–reservoir–foundation model shows that the formulation is suitable for simulating the modal response (natural frequencies and mode shapes) for different reservoir water levels and the seismic response under low-intensity earthquakes, using accelerograms measured at the dam base as input. Additionally, the dam’s nonlinear seismic response is simulated under an artificial accelerogram of increasing intensity, showing the structural effects due to vertical joint movements (release of arch tensions near the crest) and the concrete damage evolution. Full article

As semiconductor manufacturing advances into the angstrom-scale era characterized by three-dimensional integration, conventional metrology technologies face fundamental limitations regarding accuracy, speed, and non-destructiveness. Although optical spectroscopy has emerged as a prominent research focus, its application in complex manufacturing scenarios continues to confront significant technical barriers. This review establishes three concrete objectives: To categorize AI–optical spectroscopy integration paradigms spanning forward surrogate modeling, inverse prediction, physics-informed neural networks (PINNs), and multi-level architectures; to benchmark their efficacy against critical industrial metrology challenges including tool-to-tool (T2T) matching and high-aspect-ratio (HAR) structure characterization; and to identify unresolved bottlenecks for guiding next-generation intelligent semiconductor metrology. By categorically elaborating on the innovative applications of AI algorithms—such as forward surrogate models, inverse modeling techniques, physics-informed neural networks (PINNs), and multi-level network architectures—in optical spectroscopy, this work methodically assesses the implementation efficacy and limitations of each technical pathway. Through actual application case studies involving J-profiler software 5.0 and associated algorithms, this review validates the significant efficacy of AI technologies in addressing critical industrial challenges, including tool-to-tool (T2T) matching. The research demonstrates that the fusion of AI and optical spectroscopy delivers technological breakthroughs for semiconductor metrology; however, persistent challenges remain concerning data veracity, insufficient datasets, and cross-scale compatibility. Future research should prioritize enhancing model generalization capability, optimizing data acquisition and utilization strategies, and balancing algorithm real-time performance with accuracy, thereby catalyzing the transformation of semiconductor manufacturing towards an intelligence-driven advanced metrology paradigm. Full article

With the advancement of modern engineering structures, traditional cement concrete is increasingly unable to meet the mechanical performance requirements under complex conditions. To overcome the performance limitations of materials, modified concrete has become a focal point of research. By incorporating modifying materials such as fibers, polymers, or mineral admixtures, the properties of concrete can be significantly enhanced. Among these, rubberized concrete has attracted considerable attention due to its unique performance advantages. This study conducted fracture tests on rubberized concrete using non-standard concrete three-point bending beam specimens of varying dimensions to evaluate its fracture performance. Employing conventional concrete fracture theoretical models, the fracture toughness parameters of rubberized concrete were calculated, and a comparative analysis was performed regarding the applicability of various theoretical calculation formulas to rubberized concrete. The results indicated that the fracture performance of rubberized concrete varied significantly with changes in specimen size. The initial toughness exhibited a consistent size-dependent variation across different theoretical models. The fracture toughness corresponding to crack height ratios between 0.05 and 0.25 showed contradictory trends; however, for crack height ratios between 0.3 and 0.5, the fracture toughness became consistent. This study integrated boundary effect theory and employed Guinea’s theory to propose an optimization coefficient γ for the double-K fracture toughness formula, yielding favorable optimization results. Full article