This review paper examines the seismic analysis and design of liquid storage tanks. It integrates the recent knowledge of tank behavior under seismic action including fluid-structure interaction, numerical modeling, as well as experimental techniques. Some typical vulnerabilities and failure modes were reviewed concerning the last earthquakes. Additionally, new construction approaches, namely, base isolation and energy dissipation, were suggested. Moreover, the paper analyzes the philosophy of design codes and standards from their inception to modern times outlining the most important performance-based design and understanding of soil-structure interaction and modeling issues associated with sloshing and base uplifting. Furthermore, it investigates new trends including the seismic performance of tanks, informing the community of engineers of the new findings on the tank seismic response. Finally, the paper presents the development of new research directions to increase the seismic resistance of these crucial engineering structures. • Comprehensive review of seismic analysis and design advancements for liquid storage tanks over the last decade. • Critical evaluation of state-of-the-art numerical modeling techniques and their impact on tank design. • Analysis of recent code updates and their implications for the seismic safety of storage tanks. • Synthesis of lessons learned from tank performance in major earthquakes and their influence on design practices.
Advancements in artificial intelligence (AI) predictive models have emerged as valuable tools for predicting survival outcomes in allogeneic haematopoietic stem cell transplantation (allo-HSCT). These models primarily focus on pre-transplant factors, while algorithms incorporating changes in patient's status post-allo-HSCT are lacking. The aim of this study was to develop a predictive soft computing model assessing survival outcomes in allo-HSCT recipients. In this study, we assembled a comprehensive database comprising of 564 consecutive adult patients who underwent allo-HSCT between 2015 and 2024. Our algorithm selectively considers critical parameters from the database, ranking and evaluating them based on their impact on patient outcomes. By utilising the Data Ensemble Refinement Greedy Algorithm, we developed an AI model with 93.26% accuracy in predicting survivorship status in allo-HSCT recipients. Our model used only seven parameters, including age, disease, disease phase, creatinine levels at day 2 post-allo-HSCT, platelet engraftment, acute graft-versus-host disease (GvHD) and chronic GvHD. External validation of our AI model is considered essential. Machine learning algorithms have the potential to improve the prediction of long-term survival outcomes for patients undergoing allo-HSCT.
Tunnel squeezing is characterized as a significant degree of distortion in the surrounding rock mass that is typically larger than the designed deformation. The squeezing potential of rocks around tunnels can result in support failures, floor heave, and even flood disasters. In this study, the squeezing potential of rocks around tunnels were estimated by employing a hybrid intelligent framework to improve the performance of a classification algorithm. A total of 139 adjacent rock-squeezing patterns were acquired from places such as China, Nepal, and India to form the empirical basis for this study. The data consists of five influential variables, i.e., strength factor, tunnel depth, rock mass quality index, tunnel equivalent diameter and support stiffness. The mechanism of prediction consisted of three steps. Firstly, factor analysis was utilized to reduce the number of influential variables. The resulting factors were then categorized using k-means clustering. Finally, a random forest algorithm was developed to predict various levels of surrounding rock squeezing potential of rocks around tunnels. The proposed hybrid intelligent framework achieved a strong predictive capability of 96%, contributing to safer and more sustainable tunneling practices by reducing operational risks and improving overall structural stability.
The oriental river prawn (Macrobrachium nipponense, de Haan 1849) is a freshwater crustacean species with high economic value and strong development potential. The success of artificial reproduction greatly depends on broodstock conditioning and the method of egg incubation. This study aims to determine the appropriate broodstock rearing density and incubation method in river prawn seed production. In this experiment, male and female prawns were reared at densities of 50, 100, and 150 individuals per square meter in tanks, using commercial feed for white-leg shrimp with a protein 40% and lipid 7%. The results showed that at a rearing density of 50 individuals/m², the survival rate (71.0%) and maturation rate (79.1%) were significantly higher ( P <0.05) compared to the densities of 100 and 150 individuals/m². During the broodstock maturation process, female prawns carrying eggs on days 9-10 were selected to test two egg incubation methods: natural incubation (eggs carried by the female) and artificial incubation (eggs removed from the female and incubated separately). After 9 days of observation at temperatures ranging from 24.5 to 26.5°C, the results showed that natural incubation achieved a higher hatching rate (80.4%) compared to artificial incubation (71.8%). However, artificial incubation offers advantages in environmental control and is more suitable for large-scale production.
Material creep, defined as time-dependent strain accumulation under constant loading, can result in severe deformation and eventual component failure, posing a significant engineering challenge. Therefore, the possibility of early prediction of creep behavior is highly desirable. The objective of this study is to propose a robust method for predicting creep failure. To this end, we investigate the creep behavior of paper samples (quasi-brittle fiber composites) used as a model material, subjected to constant uniaxial tensile loads. Local strain fields are obtained through Digital Image Correlation and analyzed using dimensionality reduction techniques, a form of unsupervised machine learning, to identify universal indicators of deformation. This approach enables the detection of the onset of tertiary creep phase (deformation acceleration towards final failure), prediction of failure time, and accurate prediction of the failure location on the material surface just before the tertiary creep phase begins. Among the techniques used—Principal Component Analysis (PCA), Independent Component Analysis (ICA), Factor Analysis (FA), Non-negative Matrix Factorization (NMF), and Dictionary Learning (DL)—PCA and FA perform better in both detecting the onset of tertiary creep and predicting failure locations. The comparative analysis reveals the presence of universal characteristics in the evolution of local strain fields, offering a novel framework for studying material mechanics and providing key insights into failure prediction. In particular, the prediction of failure location as well as the comparison of the efficacy of various dimensionality reduction techniques are clearly novel aspects introduced in this work. • Universal characteristics of local strain field evolution were revealed. • Failure time and location were predicted through dimensionality reduction analysis. • Failure location was predicted well before visible macroscopic localization. • Principal Component Analysis and Factor Analysis outperformed other methods. • The approach employing DIC strain field data is applicable also to other materials.
Biochar, a byproduct from the biofuels industry, may be a potential feed additive in ruminant diets due to possible improvements in microbial fermentation. Therefore, the objective of this study was to determine the nutritive value, in vitro digestibility, volatile fatty acid (VFA) production, and gas production of biochar inclusion to an orchard grass (Dactylis glomerata) basal diet. The study was designed as a 3 × 2 factorial arrangement with 3 different biochar sources and 2 biochar processed sizes as the main effects factors. Experimental treatments were biochar from 3 different tree types: 1) Chestnut Oak (Quercus prinus L.; CO), 2) Yellow Poplar (Liriodendron tulipifera; YP), or 3) White Pine (Pinus strobus L.; WP), and processed at 2 different biochar particle sizes: a) μm (Fine) or b) >178 μm (Coarse). Biochar was added to the basal diet of orchard grass hay (872.35 g/kg of DM, 98.31 g/kg of CP, and 704.02 g/kg of aNDF, DM basis) at a rate of 81 g/kg DM. Biochar residual ash content was greater (P < 0.01) for Fine particle size and greater (P < 0.01) for CO and YP biochar sources. Biochar aNDF content exhibited a type × size interaction (P = 0.01) with lower aNDF content in both WP sizes compared with their respective biochar type and size. Gas production was not influenced (P = 0.23) by biochar tree type; however, gas production was increased (P = 0.05) by Fine particle size compared with Coarse biochar. The in vitro true digestibility (IVTD) of orchard grass hay was increased (P = 0.01) by the inclusion of Fine biochar particle size compared with Coarse particle size. Additionally, in vitro CP true digestibility (DCP) exhibited a type × size interaction (P = 0.01). Crude protein digestibility was lower for Fine particle-sized CO and WP biochar sources compared with Coarse particle-sized CO and WP (P ≤ 0.004). However, DCP was not different between Coarse and Fine particlesized YP biochar (P = 0.70). Volatile fatty acids (acetate, propionate, and butyrate) were not altered by biochar type (P ≥ 0.66) or particle size (P≥ 0.19). These results indicate that both tree type and particle size of biochar may need to be carefully considered before incorporating into a ruminant diet. Furthermore, Fine particle-sized biochar may be the most effective to incorporate as a feed additive in a ruminant diet based on digestibility parameters.
This study conducted a 1:3 scale model test to investigate the improvement mechanism of damaged steel–concrete transition segments strengthened by UHPC. Meanwhile, a void region was introduced at the bottom of the transition segment to simulate the grouting defect in practical engineering. Then, static and fatigue tests on these transition segments were carried out on different parameters, including non-strengthening, UHPC strengthening and UHPC strengthening combined with void repair. Digital image correlation (DIC) was employed to characterize the global strain field of the transition segment. The experimental results show that UHPC strengthening reduced the relative displacement by 0.06 mm (46.2%), while UHPC strengthening combined with void repair achieved a reduction of 0.13 mm (96%). The average strain at critical points of the transition segment decreased by 76.2% after UHPC strengthening, while a greater reduction of 86.5% was achieved when UHPC strengthening was combined with void repair. In addition, crack propagation was effectively inhibited following UHPC strengthening. The refined finite element analysis results indicated that the predicted damage state at 1.0 P was in good agreement with the experimental observations, and under the 1.3 P overload condition, the difference between calculated and measured loads at the same displacement level was only 2.5%, and most of the stresses remained below the tensile and compressive strengths of UHPC. Finally, the proposed predictive method for the circumferential tensile stress of the transition segment exhibited a prediction error of 5%, indicating satisfactory accuracy.
Abstract The field of reinforced concrete (RC) strengthening continues to evolve as the construction industry seeks cost‐effective and sustainable alternatives to structural replacement. This study, therefore, aimed to comprehensively investigate the pioneering application of steel‐reinforced grout (SRG) for strengthening large‐scale, 3.5‐meter‐long continuous RC beams. The main purpose was to explore the complex interplay among SRG density, the number of SRG layers, and the steel reinforcement ratio, and to assess their collective impact on the structural performance of the strengthened beams. Extensive experimental testing was carried out on 10 large‐scale RC continuous beams, including two pristine beams serving as references. The experimental findings demonstrated significant enhancements in the load‐carrying capacity of the strengthened beams, achieving increases ranging from 24% to 104% compared to the corresponding reference beams. Although a reduction in ductility was observed, this emphasized the need to optimize the balance between strength and deformation in the strengthened members. The use of low‐density SRG proved remarkably effective, providing superior bonding and higher efficiency, while high‐density SRG exhibited slightly lower performance due to reduced matrix penetration. Additionally, the number of SRG layers was found to play a crucial role in boosting the load capacity, with more pronounced effects in beams with lower steel reinforcement ratios. To complement the experimental investigation, two theoretical models based on the SRG effective strain were developed and validated against the experimental results. The close agreement between the models and the experimental data underscores their potential as practical tools for the design and optimization of SRG‐strengthened beams, thereby contributing critical insights for engineering applications.
Abstract Reinforcement corrosion is a major cause that leads to the deterioration of reinforced concrete (RC) structures. As a result, quickly assessing its impact on structural components has become a priority in recent research. This study examines the use of machine learning (ML) models, an advanced approach in structural engineering, to predict the performance of corroded RC beams, specifically their load‐carrying capacity and bending moment. A substantial database of 804 beam samples is utilized, consisting of 649 corroded beams and 155 uncorroded beams, to capture the complex relationships between input features and structural responses. Nine ML models are trained and compared to determine their effectiveness for this task. Additionally, advanced optimization techniques are employed to improve the predictive accuracy and feature selection of the best‐performing models. Finally, the optimized models are integrated into graphical user interfaces, offering a practical tool to support future research and facilitate predictions regarding the performance of corroded RC beams.
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