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.

Accurate and efficient rock mass quality classification is a prerequisite for assessing slope stability, designing support schemes, and ensuring mining safety in open-pit mines. However, traditional empirical classification methods rely heavily on expert judgment and often struggle to capture the complex, nonlinear relationships among factors influencing slope stability. Existing intelligent classification models also suffer from limitations, including sensitivity to incomplete data, insufficient feature interaction learning, and unstable performance on small-scale datasets. To address these issues, this study develops a deep forest (DeepForest) model optimized by three metaheuristic algorithms—brown bear optimizer (BBO), tuna swarm optimizer (TSO), and sparrow search algorithm (SSA)—to intelligently classify slope rock mass quality. A rock mass quality dataset containing 204 groups of slope and non-slope cases was established to train and evaluate the classification performance of the DeepForest models. Six influencing factors were set as input parameters: uniaxial compressive strength (UCS) of rock, rock quality designation (RQD), spacing of discontinuities (Sd), rock mass integrity coefficient (Kv), groundwater conditions (W), and site type (St). Multivariate imputation by chained equations (MICE), isolation forest (IsoForest), and synthetic minority over-sampling technique (SMOTE) were used to handle missing values, outliers, and imbalance in the dataset, respectively. The performance of the proposed models was evaluated using five metrics: accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The experimental results indicate that the BBO-DeepForest model performed best on the independent test set, with accuracy, precision, recall, F1-score, and average AUC values of 0.878, 0.682, 0.678, 0.678, and 0.961, respectively. A comparison with seven well-known imputation algorithms revealed the superiority of the selected imputation algorithm in recovering incomplete rock mass quality datasets. Model interpretation results showed that RQD and UCS are critical feature parameters for classifying slope rock mass quality. At last, the proposed BBO-DeepForest model was employed to verify the rock mass quality of three slopes at the Luming molybdenum mine, resulting in classifications consistent with on-site observations. It demonstrates that combining DeepForest with metaheuristic optimization algorithms is a feasible and accurate approach for intelligently classifying the rock mass quality of slopes.

In this study, a novel digital twin (DT) modeling approach is developed to enhance the reliability assessment of civil structures subjected to multi-source uncertainties. Referring to the proposed procedure, a modular Bayesian inference (MBI) with the Transitional Markov Chain Monte Carlo (TMCMC) sampling algorithm is first used to calibrate the DT model of structures based on measured structural responses. Distinguished from the classic Bayesian inference approach, the uncertainty caused by modeling errors is added to the extended likelihood function by using a bias function. Then, the DT model calibration of structures can be achieved by three modules. Based on the calibrated DT model of structures, a finite number of discrete structural response samples can be generated by performing nonlinear dynamic analysis. Then, the generalized extreme value distribution (GEVD) is used to fit the probability density function (PDF) of these discrete response samples based on the maximum likelihood estimation (MLE) algorithm. Subsequently, the earthquake-induced failure probability of structures can be assessed by directly integrating the fitted GEVD and predefined structural failure thresholds. Numerical simulations on a three-story steel frame structure subjected to seismic excitations are developed to validate the feasibility of the proposed approach. The shake table tests on a scaled reinforced concrete (RC) column structure are further conducted to verify the effectiveness of the proposed approach. Both numerical and experimental results demonstrate that the proposed approach is reliable and highly effective for structural reliability assessment.

Distance-decay models are fundamental to accessibility modeling; yet their alignment with actual travel behavior remains insufficiently examined in empirical terms. To help address this gap, we propose a dual-component Tanner–Gaussian decay model that seeks to mitigate two key limitations of traditional accessibility frameworks by simultaneously describing the phenomena of full decay and local peaks. The model is calibrated using survey data from an urban park in Hangzhou, China, and subsequently assessed on two additional datasets from Wuhan and Shanghai. Results indicate that: (1) traditional Gaussian functions may overestimate short-distance and underestimate long-distance accessibility, while Tanner functions tend to capture long-tail decay more effectively and appear more suitable for long-distance accessibility estimation; (2) the proposed dual-component model performs favorably in large samples, though its stability appears sensitive to sample size. By comparing the application of multiple accessibility models across three datasets, we highlight how their suitability varies depending on the specific context. This comparison may offer a reference that could assist designers in identifying underserved areas and supporting more equitable access to urban green spaces in the context of rapid urbanization.

This study investigates chloride ingress and the attainment of steel depassivation conditions in fully saturated concrete exposed to seawater under long-term temperature scenarios. A coupled reactive-transport model is implemented in Toughreact by integrating multi-ion diffusion, thermodynamic aqueous speciation/mineral equilibria, kinetic dissolution-precipitation of major hydrates, chloride binding, and porosity feedback on the effective diffusion coefficient D e . The model is benchmarked against published long-term submerged chloride profiles to ensure realistic coupled transport-reaction behavior. Results show that non-Fickian near-surface features (chloride “drop” and subsurface peaks) can persist even under permanently submerged conditions, driven by a thin altered surface layer with reduced transport porosity/D e and diminished binding capacity (reduced bound chloride). Parametric simulations over 5-40°C (up to 50 years) demonstrate that higher temperature accelerates mineral alteration, alkalinity loss, and the advance of depassivation indicators. A consistent comparison of three criteria (free chloride, total chloride, and [Cl - ]/[OH - ]) shows that they may diverge when alkalinity, binding, and porosity evolve; thus, [Cl - ]/[OH - ] provides a chemistry-consistent depassivation index. For a representative cover depth of d=50 mm, depassivation conditions are reached within decades under warm exposure.

• GenAI and prompt engineering transform construction and mining industries. • Prompts include key factors influencing slope stability outcomes. • Models show high accuracy in predicting slope stability metrics • Stresses need for transparent and accountable AI in critical tasks. Generative AI (GenAI) and prompt engineering are rapidly advancing in industries such as construction and mining, leading to significant improvements in efficiency, accuracy, and decision-making processes. These technologies are transforming the construction sector by automating tasks and optimizing workflows, thereby enhancing productivity and risk management. This study explores the application of Google’s Gemini AI tool, a notable breakthrough in GenAI, specifically for predictive modeling of slope stability. The Gemini AI tool is utilized within the Python programming language to generate prompts that incorporate key factors influencing slope stability, with the Google Colab interface facilitating prompt generation and testing. Initially, these prompts are employed for data analysis and visualization, followed by their application in both unsupervised and supervised machine learning approaches. The performance evaluation metrics indicate that the integrated approaches, which combine GenAI and prompt engineering, predict slope stability with a high level of accuracy. The model achieved 99% accuracy, with precision, recall, and F 1 -scores ranging from 0.98 to 1.00 for both stable and unstable slope classes. This innovative methodology seeks to advance the implementation of GenAI in civil and mining engineering, offering more precise and efficient solutions for managing slope stability and supporting safe, sustainable, and climate-smart mining operations.

• Fourteen studies show that fine phonetic detail is systematically regulated in shaping sound systems. • Fine phonetic detail links production, perception, and learning, sustaining contrasts and enabling sound change. • Sound systems emerge from controlled allocation of continuous phonetic parameters within prosodic structure. • Prosodic structure guides segmental and suprasegmental realization across languages and domains. • Phonetic grammar is a language-specific, system-internal control system shaped by motor, perceptual, and cognitive pressures. This special issue examines how fine phonetic detail participates in the shaping of sound systems. Across fourteen studies, the central theme is that subtle temporal, spectral, and articulatory patterns are not incidental by-products of articulation, but are systematically regulated aspects of speakers’ phonetic knowledge. They provide the means through which phonological contrasts and prosodic structure are realized, maintained, and sometimes reorganized. The contributions show how languages allocate continuous phonetic parameters—such as timing, coordination, voice quality, and nasality—within prosodic domains (e.g., phrases, words, and syllables) and under general biomechanical and communicative pressures. Studies of Irish, Hawaiian, Japanese, and Mandarin illustrate how prosodic structure guides segmental and suprasegmental realization. Work on English, German, Danish, and Cantonese demonstrates how fine phonetic detail underlies patterns of variation and creates potential pathways for change. Production connects naturally to perception and learning: findings from English accent adaptation and Samoan iterated learning reveal how listeners stabilize or reinterpret detail, linking individual processing to community-level patterning. A set of studies on Italian, Korean, English, and L2 German show how prominence reorganizes cues across articulation, interaction, and acquisition, shaping how speakers signal and listeners recover linguistic structure. These studies converge on a view in which fine phonetic detail arises from a central phonetic component (or the phonetic grammar) of linguistic structure—controlled by speakers, shaped by universal motor and perceptual constraints, and continually adjusted through perception and learning. In this perspective, sound systems emerge from the interplay of these regulated patterns, which sustain contrasts, support communication, and open principled routes for change.

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.