Search Results (1,603)

The aim of this experimental study was to develop and evaluate the effectiveness of a new strengthening system for reinforced concrete slabs employing external jackets consisting of ultra-high-performance fiber-reinforced-concrete (UHPFRC) and mechanical anchor systems. The issue of debonding between old and fresh concrete layers, as well as the efficiency of utilizing CFRP rods, is the primary challenge of applying the UHPFRC jackets with embedded CFRP rods. In this study, we propose a novel retrofitting technique for implementing a mechanical anchor system to improve the binding of fresh UHPFRC jackets with old RC slabs. An experimental test was conducted by subjecting three slabs to cyclic loads by utilizing a dynamic actuator: a reference slab, a retrofitted slab with an external UHPFRC layer, and a retrofitted slab with an external UHPFRC layer incorporating CFRP bars. Furthermore, finite element models (FEMs) were utilized to investigate the responses of the retrofitted slabs and compare the novel method with traditional strengthening techniques, including near-surface-mounted (NSM) CFRP rods, externally bonded CFRP strips, and epoxy-bonded UHPFRC jackets, as well as two models that were the same as the experimental strengthened slab specimens except for the fact that they did not have a mechanical anchor system. Additionally, analytical mechanistic models were employed to determine the flexural moment capacity of the RC slabs. The experimental findings demonstrated that the proposed strengthening strategy considerably prevented premature debonding and enhanced the maximum load of retrofitted RC slabs by over 82%. Also, the FEM and analytical results are significantly consistent with the experimental outcomes. In conclusion, the newly suggested strengthening technique is a reliable system for enhancing the efficacy of slabs, effectively preventing early debonding between existing and new components. Full article

Restoring and strengthening existing bridges is more economically and environmentally feasible, as cement production in new RC bridges significantly contributes to CO₂ emissions. Additionally, the production of composite carbon materials for strengthening RC structures does not require a large amount of energy, unlike the production of steel for reinforcement, which requires a significant amount of electricity and, accordingly, causes a significant amount of CO₂ emissions. This is why this article presents a comprehensive review of the damage, calculations, and strengthening of RC bridge structures. It examines the main types of damage, including mechanical impacts, material fatigue, corrosion processes, seismic actions, and thermal loads. The mechanisms of their formation, correlations with environmental factors, and operational conditions are detailed. Examples of damage from real engineering objects are provided to assess the scale of the problem. Approaches to the calculation of RC bridge structures are analyzed, particularly methods for modeling the stress–strain state, considering crack formation and material degradation. Key studies by Ukrainian and foreign researchers are highlighted, identifying areas for further methodological improvement. Special attention is given to traditional and modern strengthening methods, including the use of steel elements, composites, and carbon strips. A comparative analysis of the effectiveness of different strengthening approaches is conducted. The conclusion emphasizes the need for further development of existing diagnostic, calculation, and strengthening methods. The integration of innovative materials and technologies is particularly relevant for enhancing the durability of bridges under modern operational loads. Full article

Predicting battery states such as the voltage and state of charge (SOC) can help us monitor lithium batteries more efficiently during usage. This study proposed a predictive model for the lithium battery voltage and SOC by combining a second-order RC equivalent circuit model with a multi-head attention Bidirectional Long Short-Term Memory (MHA-BiLSTM) neural network. The equivalent circuit model simulates long-term charge–discharge cycles in Simulink, providing essential data for model training. The BiLSTM model, enhanced by the multi-head attention mechanism, is used for accurate short-term predictions of the battery voltage and SOC. The experimental results demonstrate that the proposed MHA-BiLSTM model outperforms other models in the prediction accuracy, achieving an R² of 0.91, with the lowest RMSE of 0.0567 and MAPE of 0.0095. This hybrid approach effectively captures the dynamic behavior of the battery and reduces predictive errors, making it a promising solution for battery health monitoring and management. Full article

This study proposes that MNCs might withdraw from the CSR concept to gain tangible benefits, like improved corporate financial performance (CFP), and intangible benefits, such as reputational capital (RC). This represents a paradigm shift from the philanthropic end of the spectrum to the strategic win–win side, where all investments are expected to yield a return. Being tacit, quests for reputational returns are discussed in terms of corporate social performance (CSP) with its currency being RC (an intangible asset). However, this requires a deep understanding of the CSP concept and ‘good management’. This study argues that CSR will change trajectory based on three facets. First, we argue for the replacement of CSR by CSP, where ESG becomes ‘business as usual’. Second, regulatory categories (voluntary or legislated) will merge. Third, ethics endorsing ‘good management’ will alter executive mindsets, making CSP deeply embedded in corporate behavior. Organizational behavior towards CSP must, therefore, be sincere yet not embedded overwhelmingly. We extend previous discussions regarding the relationship between CSP and CFP, who present robust evidence that (1) absent CSR embedment has no/neutral CSP and CFP effect; (2) inadequate CSR yields negative CSP and CFP; and (3) productive CSR positively affects CSP and CFP. Consequently, this study argues that (4) strategic CSR (SCSR) maximizes positive CSP and that (5) excessive CSR is detrimental, yielding negative effects on both CSP and CFP. This study, therefore, conjectures the existence of a ‘sweet spot’, where SCSR optimizes CSP and CFP outcomes. The contributions address ESG engagement as a ‘sweet spot’ concept and provide a model enabling SCSR discussion, CSP evaluations, and an implementation framework for its achievement. The framework gives executives a toolbox to influence their stakeholders toward improved CFP. Therefore, our perspective supports CSP embedment, enabling firms to address business growth and sustainability requirements. Full article

This study experimentally examines the flexural performance, crack formation patterns, and failure mechanisms of glass fiber-reinforced polymer (GFRP) bar-reinforced concrete beams with varying concrete compressive strengths (low, moderate, and high), addressing a gap in the current literature. Furthermore, it employs an innovative machine learning approach to enhance analysis. Nine RC beams reinforced with GFRP bars, having concrete compressive strengths of low (CC20), moderate (CC30), and high (CC40), each measuring 150 × 200 × 1100 mm, were fabricated and tested under three-point bending conditions. Through the integration of three-point bending tests and machine learning-based prediction models, this study connects experimental findings with advanced analytical approaches. One of the key innovations in this study is the use of eighteen ML regression models implemented with Python’s PyCaret library, achieving an impressive average prediction accuracy of 91.5% for RC beam deflection values. In particular, the Ada Boost Regressor and Gradient Boosting Regressor models performed exceptionally well on GFRP bar-reinforced concrete beams, providing the highest number of consistent and highly accurate predictions, making them very useful tools for GFRP bar-reinforced beam ultimate load-carrying capacity/deflection predictions. The outcomes identified clear failure mechanisms: RC beams with CC20, CC30, and CC40 concrete compressive strengths typically developed a single, large flexural crack at the midpoint. Although the ultimate load-carrying capacity of GFRP bar RC beams improved with higher concrete compressive strength, CC20 and CC30 beams displayed more ductile failure behavior than CC40 beams. The ultimate load-carrying capacity of CC40 RC beams was determined to be approximately 74% higher than that of CC20 RC beams. Regardless of the concrete compressive strength class, the absence of shear cracks and the prevention of sudden failure under bending in GFRP bar-reinforced concrete beams are considered major advantages of using GFRP bar reinforcement. Full article

The tomato is among the crops with the most extensive cultivated area and greatest consumption in our nation; nonetheless, secondary salinization of facility soil significantly hinders the sustainable growth of facility agriculture. Melatonin (MT), as an innovative plant growth regulator, is essential in stress responses. This research used a hydroponic setup to replicate saline stress conditions. Different endogenous levels of melatonin (MT) were established by foliar spraying of 100 μmol·L⁻¹ MT, the MT synthesis inhibitor p-CPA (100 μmol·L⁻¹), and a combination of p-CPA and MT, to investigate the mechanism by which MT mitigates the effects of salt stress on the photosynthetic efficiency of tomato seedlings. Results indicated that after six days of salt stress, the endogenous MT content in tomato seedlings drastically decreased, with declines in the net photosynthetic rate and photosystem performance indices (PI_total and PI_abs). The OJIP fluorescence curve exhibited distortion, characterized by anomalous K-band and L-band manifestations. Exogenous MT dramatically enhanced the gene (TrpDC, T5H, SNAcT, and AcSNMT) expression of critical enzymes in MT synthesis, therefore boosting the level of endogenous MT. The application of MT enhanced the photosynthetic parameters. MT treatment decreased the fluorescence intensities of the J-phase and I-phase in the OJIP curve under salt stress, attenuated the irregularities in the K-band and L-band performance, and concurrently enhanced quantum yield and energy partitioning ratios. It specifically elevated φP_o, φE_o, and ψ_o, while decreasing φD_o. The therapy enhanced parameters of both the membrane model (ABS/RC, DI_o/RC, ET_o/RC, and TR_o/RC) and leaf model (ABS/CS_m, TR_o/CS_m, ET_o/CS_m, and DI_o/CS_m). Conversely, the injection of exogenous p-CPA exacerbated salt stress-related damage to the photosystem of tomato seedlings and diminished the beneficial effects of MT. The findings suggest that exogenous MT mitigates salt stress-induced photoinhibition by (1) modulating endogenous MT concentrations, (2) augmenting PSII reaction center functionality, (3) safeguarding the oxygen-evolving complex (OEC), (4) reinstating PSI redox potential, (5) facilitating photosynthetic electron transport, and (6) optimizing energy absorption and dissipation. As a result, MT markedly enhanced photochemical performance and facilitated development and salt stress resilience in tomato seedlings. Full article

To continuously produce blue hydrogen from methane efficiently, a dual fluidized bed reactor was designed, and the corresponding kinetic model was built with the commercial Aspen Plus software v2006 and user-defined FORTRAN routine. To prove the reliability and accuracy of the kinetic model in this work, the model predictions were compared against reported experimental data from similar devices. Then, sensitivity analyses were implemented to fully investigate the characteristics of the designed reactor. The effects of reforming temperature (T_REF), calcination temperature (T_CAL), steam to carbon mole ratio (R_S/C), calcium to carbon mole ratio (R_C/C), catalyst to sorbent mass ratio (m_C/S) and the residence time (t_R) on the produced H₂ dry mole fraction (F_H2), CH₄ conversion rate (C_CH4), carbon capture rate (C_CO2), and the reactor efficiency (E_R) were comprehensively analyzed. It was found that, at the optimal operating conditions (T_REF = 650 °C, R_S/C = 5.0, R_C/C = 3.0, t_R = 60 s, and m_C/S = 3.0), C_CH4 can reach 96%, C_CO2 can reach 77.4%, F_H2 can reach 94.3%, and E_R can reach 67% without heat recover. Full article

This study proposes a method for estimating the junction temperature of power semiconductors, particularly IGBTs (Insulated Gate Bipolar Transistors) and diodes. Traditional temperature measurement methods using NTC (Negative Temperature Coefficient) sensors have limitations in reflecting dynamic conditions in real time, as temperature changes take time to reach the sensors. To address this, this study proposes a junction temperature estimation method using RC curve fitting and a thermal impedance model. This model represents the thermal behavior of IGBTs and diodes using a Foster thermal network that considers the resistance and capacitance of the heat transfer path. In particular, transient temperature estimation considering thermal coupling enables the prediction of temperature changes in IGBTs and diodes. To verify the proposed temperature estimation method, experiments were conducted to build the model based on data measured with an infrared thermal camera and NTC sensors. The model’s estimated results were compared with actual values across 25 operating regions, achieving a maximum MAE (Mean Absolute Error) of 2.26 °C. A comparative analysis of first-, second-, third-, and fourth-order Foster networks revealed that, while higher orders improve accuracy, gains beyond the second order are minimal relative to computational demands. This study contributes to enhancing not only the reliability of power semiconductor modules but also minimizing the temperature margin for inverters by estimating the junction temperature with better dynamic performance than that achieved by NTC sensors. Full article

Background: Frailty, sarcopenia, nutritional risk, and cognitive impairment are prevalent geriatric syndromes that adversely affect health outcomes in older adults, underscoring the need for an effective screen tool to enable early detection and timely intervention. Methods: This study employed a cross-sectional validation design and translated, culturally adapted, and validated the Chinese version of the Rapid Geriatric Assessment (C-RGA) among 416 nursing home residents. The C-RGA consists of four subscales: the simple frail questionnaire screening tool (FRAIL), SARC-F for sarcopenia (SARC-F), the Simplified Nutritional Assessment Questionnaire (SNAQ), and the Rapid Cognitive Screen (RCS). Results: The C-RGA demonstrated high content validity (S-CVI/Ave = 0.982) and strong internal consistency (Cronbach’s α = 0.839). Factor analysis confirmed its four-domain structure, accounting for 61.497% of the variance. Model fit indices demonstrated good construct validity (χ²/df = 1.122, RMSEA = 0.024, GFI, AGFI, and CFI > 0.90), supporting the robustness of the assessment tool. Pearson correlation analysis revealed a strong association between FRAIL and SARC-F with SNAQ (r = −0.671, 95% CI: [−0.742, −0.600], p < 0.01) and a moderate correlation with RCS (r = −0.426, 95% CI: [−0.513, −0.339], p < 0.01), underscoring the interplay among nutritional deficits, muscle weakness, and cognitive impairment. Conclusions: The C-RGA demonstrates strong psychometric properties, supporting its potential use as a screening tool for the early detection of frailty, sarcopenia, nutritional risk, and cognitive impairment among nursing home residents, enabling timely and targeted interventions. Future research should further assess its applicability across diverse healthcare settings to enhance its generalizability and clinical utility. Full article

As a result of the 2017 Pohang earthquake, numerous piloti-type structures incurred damage, and the cause was attributed to the wide spacing of transverse reinforcement. Improper spacing of transverse reinforcement can lead to brittle failure of columns, potentially causing the collapse of buildings. This study aimed to analyze the failure mode of columns where load and displacement are concentrated due to vertical irregularity, and to quantify the spacing of shear reinforcement according to the degree of vertical irregularity to prevent shear failure of the column. First, a vertically irregular frame with vertical irregularity and an RC moment frame with the same upper and lower structural systems was modeled, and the failure mode of the column was analyzed. In this paper, the failure modes were classified into shear failure, flexure–shear failure, and flexural failure based on the shear capacity ratio. The analysis results showed that in the case of vertical irregularity, the shear demand of the column was evaluated as high due to the high flexural stiffness of the horizontal members, and the failure mode of the column was classified as shear failure. The impact of the spacing of shear reinforcement on the shear strength of the structure was also examined. Next, an analysis was performed according to the degree of vertical irregularity by adjusting the thickness of the first-floor shear wall, and as a result, the proportion of the entire columns classified as shear failure increased as the vertical irregularity increased. It was confirmed that the minimum spacing of shear reinforcement of 150 mm specified in Korean standards becomes inadequate when the degree of vertical irregularity exceeds 2.6. At a vertical irregularity of 8.3, the spacing required to prevent shear failure decreased to 136 mm, which is 9.33% less than the minimum specified by the Korean standards. This indicates that even if the code’s minimum spacing is adhered to, shear failure can still occur in columns. In order to prevent shear failure of the column, the spacing of the shear reinforcement should be designed smaller, because the shear force increases as the vertical irregularity increases. For piloti-type structures with high horizontal irregularity, there is a need to design shear reinforcement narrower than the minimum standard to prevent shear failure of the column. Full article

Reinforced concrete (RC) tanks are essential for storing liquids and bulk materials across various industries. However, simplified analytical methods fall short in providing an accurate analysis, while traditional methods, such as finite element modeling, can be computationally intensive and time-consuming, especially when dealing with nonlinear material properties and complex geometries, like conical and cylindrical shapes. This highlights the need for a more efficient and simplified analysis approach. Accordingly, the present paper introduces a machine learning (ML) framework as an effective predictive tool for RC conical and cylindrical tanks under hydrostatic pressure. Data from 320 RC conical and cylindrical water tanks, previously analyzed using finite element modeling, were used to train and test various ML models, considering geometrical and material nonlinearities. Four machine learning models—decision trees, random forests, gradient boosting, and extreme gradient boosting—were utilized to predict critical internal forces, including the maximum ring tension force, maximum meridional moment, and maximum meridional axial force. The accuracy of each model was evaluated using different statistical measures. To improve model interpretability and identify key predictors, feature importance techniques were employed to rank the significance of each input variable to the predictions. Furthermore, Accumulated Local Effects (ALE) plots were utilized to visualize the relationships between model inputs and outputs, providing a clearer understanding of the inner workings of the ML models. The combined use of feature importance and ALE plots enhances model transparency by illustrating how specific features contribute to the predictions, thereby supporting the informed application of ML in the structural design and analysis of RC tanks. Ultimately, the framework presented in this study aims to promote the practical application of machine learning in structural engineering, contributing to simpler, more efficient, and accurate analysis and design processes for RC water tanks. Full article

In this research, the seismic performance optimization of a Y-eccentrically braced composite frame (Y-EBCF) was conducted. An efficient fiber beam–spring element model, which considers the spatial composite effect of the RC slab, was proposed and used to simulate the research objects. A genetic algorithm (GA) was developed for the Y-EBCF, and the chromosome coding method, fitness function, termination condition, and the selection, crossover, and mutation operators were specified. This algorithm was then applied to the optimization problems of arrangement strategies of the eccentric braces and mechanical parameters of shear links for a typical 10-storey composite frame building under different acceleration excitations. The results indicated that, compared to the traditional enumeration algorithm, the proposed GA could find the optimal solution rapidly for the seismic performance optimization problems of the Y-EBCF. The computation cost of the GA for the optimization problems involving the arrangement strategies of the eccentric braces and mechanical parameters of shear links was only 1.5% and 2.6% of those of the enumeration algorithm, respectively. A subsequent parametric analysis revealed that the calculation cost of the GA could be further reduced by adjusting the values of population size, selection, and mutation ratio. Full article

Reinforced concrete (RC) structures in coastal atmospheres commonly suffer the penetration of chloride ions, which can lead to the corrosion of reinforcements and, thus, a reduction in their structural performance under earthquakes. In recent years, time-dependent seismic fragility analysis has been widely used as an effective tool to represent the deterioration in the seismic performance of aging RC structures. However, few studies have considered the influences of varying chloride ion exposure environments due to the different distances of structures from a coastline. In light of this, this study performs a time-dependent seismic fragility analysis for aging RC frames, considering varying distances of the buildings from the coastline. To conduct this, a time-dependent reinforcement corrosion rate model that can consider the effect of the distance of a building from the coastline is established by combining a concrete surface chloride ion concentration model, an initial corrosion time model, and an electrochemical corrosion rate model. By integrating material deterioration models for reinforcements and concrete, the seismic fragility relationships for structures with different degrees of corrosion damage can be developed. A corrosion deterioration factor is then proposed to quantify the relationship between the seismic fragility function parameters and the corrosion rate. Subsequently, time-dependent fragility functions considering the effect of the distance from the coastline can be established. A nine-story RC frame designed according to the existing Chinese codes is used for illustration. The time-dependent seismic fragility relationship of the structure is developed considering different distances of buildings from the coastline. The results show that the effect of the distance of a building from the coastline varies under different categories of environment. The seismic fragility results for a structure under a III-a environment are more significantly influenced by the structural distance from the coastline compared to those for a structure under a II-a environment. Full article

To analyze the impact of capacity uncertainty on the seismic collapse fragility of reinforced concrete (RC) frame structures, a fragility analysis framework based on seismic reliability methods is proposed. First, incremental dynamic analysis (IDA) curves are plotted by IDA under a group of natural seismic waves. Subsequently, collapse points are identified based on recommendations from relevant standards, yielding the probability distribution of the maximum inter-story drift ratios (MIDRs) at collapse points. Then, the distribution of the MIDRs under various intensity measures (IMs) of artificial seismic waves is calculated by using the fractional exponential moments-based maximum entropy method (FEM-MEM). Next, the structural failure probability is determined based on the combined performance index (CPI), and a seismic collapse fragility curve is plotted using the four-parameter shifted generalized lognormal distribution (SGLD) model. The results indicate that the collapse probability is lower considering the capacity uncertainty. Compared to deterministic MIDR limits of 1/25 and 1/50, the median values of the structure’s collapse resistance increased by 13.2% and 87.3%, respectively. Additionally, the failure probability obtained by considering the capacity uncertainty is lower than the results based on deterministic limits alone. These findings highlight the importance of considering capacity uncertainty in seismic risk assessments of RC frame structures. Full article

This study aimed to identify the critical ground motion parameters that lead to structural damage and assess their impact on the nonlinear responses of buildings. The analyses are carried out using a calibrated numerical model that was acquired within the ICONS experimental framework that represents reinforced concrete (RC) structures constructed before seismic design regulations were enforced. For the analysis, 30 seismic records were chosen based on magnitude (M), epicentral distance (R), and peak ground acceleration (PGA) for two high seismic activity areas that were observed. Eleven parameters are categorized, traditional metrics, energy-based, spectrum-based, duration-based, and fundamental metrics, and examined based on their main attributes. The results showed a strong relationship between certain seismic properties and the maximum interstory drifts of building as a damage prediction parameter. Peak ground velocity (PGV), specific energy density (SED), and Housner Intensity (HI) were found to be the most important variables in assessing the correlation with possible structural damage. Therefore, the assessment of structural damage based on nonlinear dynamic analysis should primarily incorporate PGV with the possible addition of energy- and spectrum-based metrics as the most reliable ground motion parameters for the selection of earthquake records for time history analysis. Full article