Laser-induced breakdown spectroscopy (LIBS) is a remarkable elemental identification and quantification technique used in multiple sectors, including science, engineering, and medicine. Machine learning techniques have recently sparked a widespread interest in the development of calibration-free LIBS due to their ability to generate a defined pattern for complex systems. In geotechnical engineering, understanding soil mechanics in relation to the applications is of paramount importance. The knowledge of soil unconfined compressive strength (UCS) enables engineers to identify the behaviors of a particular soil and propose effective solutions to given geotechnical problems. However, the experimental techniques involved in the measurements of soil UCS are incredibly expensive and time-consuming. In this work, we propose a pioneering technique to estimate the soil UCS using machine learning algorithms based on the emission spectra obtained from the LIBS system. Support vector regression (SVR) and decision tree regression (DTR) learners were initially employed, and consequently, the adaptive boosting method was applied to improve the performance of the two single learners. The performance of the models was assessed based on the standard metric performance indicators R2-score, mean absolute error (MAE), root means square error (RMSE), and correlation coefficient (CC) between the predicted and actual soil UCS values. Our results revealed that the boosted DTR exhibited the highest coefficient of correlation of 99.52% and R2 value of 99.03% during the testing phase. To validate the models, the UCS of soils stabilized with cement and lime were predicted with a high degree of accuracy, confirming the models' suitability and generalization strength for soil UCS investigations.
Social sustainability remains the least consistently defined and measured pillar of urban development, limiting comparability across studies and constraining evidence-based policy. This paper addressed this gap through a systematic analysis of twenty academic frameworks, consolidating 365 extracted metrics into a Holistic Social Sustainability Assessment Framework (HSSF) comprising 147 specialized metrics organized across four domains and seventeen subdomains. Using HSSF as a benchmark, the frameworks were evaluated along two complementary dimensions: spread and density. The findings revealed pronounced fragmentation, with 84% of the specialized metrics appearing in only one or two tools, indicating limited consensus on the core components of social sustainability. Methodologically, the field relied heavily on subjective inputs, with approximately 85% of the metrics classified as subjective, while only 5% were objective and verifiable. Although several frameworks appeared comprehensive at the domain level ten out of twenty covered all four domains coverage was consistently weak at the subdomain level, where no framework exceeded 60% coverage and substantial portions remained unmeasured, with zero-metric subdomains ranging from 41% to 88%. Metric density was similarly uneven, with detailed assessment concentrated in a small number of attributes, while entire domains were omitted in up to half of the reviewed frameworks. These results indicate that existing tools frequently operationalize divergent constructs and are therefore not directly comparable. The HSSF, together with the proposed diagnostic coverage-analysis approach, provides a foundation for developing a more standardized, balanced, and globally applicable set of social sustainability metrics capable of supporting robust assessment and evidenceinformed urban policy
Ultra-high-performance concrete (UHPC) is one of the contemporary overlay materials for repairing and retrofitting of reinforced concrete (RC) members. It possesses excellent compressive and tensile strength, as well as long durability. Nevertheless, the bonding performance between the overlay interface (UHPC) and the substrate concrete must be adequate under various loading, curing, and exposure circumstances. Therefore, this research examined, experimentally, the interfacial bonding behavior of UHPC overlay and two distinct substrates, namely concrete screed (CS) and self-compacted concrete (SCC). Four parameters impacting bond strength behavior, including three different substrate surface preparations, curing conditions, exposure environments, and testing techniques, were used to fulfill the goal of this study. The tests findings revealed that the substrate surface preparation and the exposure conditions had significant effects on the bond behavior of both UHPC-SC and UHPC-SCC while curing conditions seemed not to have any significant effects. The highest bond strength was obtained for specimens having sandblasted substrate preparation technique regardless of the bond test method. However, specimens tested under the bi-surface shear strength technique exhibited high bonding strength with the drill holes substrate preparation technique due to the presence of drilled holes that were being filled with UHPC. The splitting tensile test is a reliable test technique to examine the impact of repeated cyclic exposure samples. As a result of this, a substantial reduction in bond strength of nearly 32%, 55%, and 26% of the UHPC-SCC and 31.84%, 51.5%, and 41.42% for UHPC-SC interfaces for as-cast (AC), drilling hole (DH), and sandblasting (SB) substrate surface preparations, respectively, were obtained. ANOVA was carried out and found to be aligned with the experimental findings.
Reinforced concrete structures face significant annual expenditures in combating rebar corrosion, with chloride penetration identified as a major contributor. This study employs laser-induced breakdown spectroscopy (LIBS) to quantitatively predict the chloride-induced corrosion rate in such structures. Eighty samples, characterized by known chloride content (ranging from 0 % to 1.0 %), underwent controlled corrosion processes with predetermined degrees measured as bar weight loss. The investigation included two cement types, Type I and Type V, along with samples incorporating pozzolanic materials (FA, SF, and GGBFS,) in addition to plain OPC samples. LIBS was subsequently employed to detect chlorine presence in each concrete sample, recording the corresponding intensity of the chlorine line. After the initial data acquisition, peak analysis was performed to identify the dominant spectral peaks, ensuring that the most relevant signals were isolated. Following this, the intensity of the chloride emission line from the LIBS spectrum was recorded and correlated with its corresponding chloride signal, establishing a quantitative relationship between the LIBS output and chloride presence. A model was then developed to link the LIBS signal intensity to its corresponding chloride concentration in the sample. In parallel, the corrosion rate associated with each specific chloride concentration was measured and then linked to the LIBS-derived chloride concentrations, creating a framework that ties the LIBS output to the actual rebar degradation within the concrete. Comparisons with previous studies demonstrated superior conformity of the developed models. The study also presents optimized parameters for the LIBS setup. Notably, results indicated a high correlation between LIBS intensity and chloride concentration, with the developed model accurately predicting the degree of corrosion.
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The work presented in this paper was aimed to study the influence of sugar dosage on the setting time and strength of two types of Portland cements (Type I and Type V) widely used in Saudi Arabia, leading towards selection of an optimal dosage of sugar as an alternative set retarder. Paste and mortar specimens were prepared with different dosages of sugar and tested for initial and final setting times, compressive strength and microstructural examination of set cements. The morphology of sugar-admixed mortar specimens was studied by utilizing a scanning electron microscope. It was noted that the addition of up to 0.05% sugar by weight of cement increased the setting time sharply, however, a further addition of sugar decreased the setting time of both types of cements. Strength of the mortar specimens, particularly at an early age, decreased with an increase in sugar content. Overall results of this study indicate that, for avoiding adverse effects on setting time and strength, dosage of sugar should be in the range of 0.05–0.1% by weight of both types of cements considered in the present case study.
The production of silica aerogels (SA) has gained significant interest recently because of their ultra-high porosity, very low density, and multifunctional uses. This review highlights recent methods of synthesis, focusing on scalable and reproducible techniques suitable for practical applications. Special attention is given to how catalysts and pH influence hydrolysis, condensation, and gelation, as these factors determine the structure of the gel network. Post-gel treatments like aging and solvent exchange are identified as critical steps to reinforce the skeleton and remove residuals before drying. Surface modification through silylation is emphasized for enabling ambient-pressure drying (APD), making the aerogels hydrophobic, and helping to partially restore the porous structure. A comparison of drying techniques is provided: supercritical drying (SCD) preserves excellent pore structure but requires high energy and safety precautions, while freeze-drying (FD) offers a cost-effective alternative when ice crystallization is carefully controlled. Finally, the link between synthesis parameters and key properties, such as density, pore size distribution, surface area, and wettability, is summarized to facilitate reproducibility.
Circular cylindrical shells are prone to buckling failure under different types of external pressure. These can include uniform lateral pressure, hydrostatic pressure (uniform lateral plus end pressure), and non-uniform wind pressure. The focus of this study is to thoroughly examine the available formulas for calculating the buckling pressure of circular cylindrical shells under these various types of external pressure and to assess their limitations. The study delves into a dozen of simplified formulas drawn from literature, discussing their derivations and comparing them with the exact solution of the problem to unveil their constraints. The study also investigates the correlations between uniform lateral and wind pressures, the effects of geometrical and material nonlinearities, and the impact of imperfections. The findings confirm that hydrostatic buckling pressure is consistently lower than lateral buckling pressure, and the conditions under which the difference between the two is negligible have been refined. It was found that the difference in buckling pressures is insignificant (less than 5 %) for length-to-radius ratios greater than 1.0. Additionally, two simplified formulas from the dozen compared were identified as particularly reliable, yielding accurate results across a wide range of shell geometries. The study further revealed that the discrepancy between theoretical and experimental buckling stresses increases as the length-to-radius ratio decreases, with the effect becoming particularly pronounced for short shells with length-to-radius ratios below 1.0. Finally, the study summarizes key aspects related to the stability of circular cylindrical shells, including imperfection sensitivity, lower bound estimates, and stiffener requirements.
This work presents a machine-learning framework to explore cathode materials for zinc-ion batteries from a data set of 6858 zinc-containing compounds. Utilizing the extensive Materials Project (MP) database, we employed a two-step machine learning (ML) approach that uses transfer learning to compensate for missing electrochemical properties. Initially, a random forest regressor was used to fill in missing features in the zinc compounds, harnessing the full battery explorer in predictions. Two hybrid models were then developed: the sparrow search algorithm-light gradient boosting machine (SSA-LGBM), and Harris Hawk optimization-deep neural networks (HHO-DNN). The data set contains 107 feature vectors, which were minimized through principal component analysis. These features include descriptors related to structural, chemical, and electronic properties. Both models were trained using the 4351 known battery compounds from MP to predict key properties such as average voltage and gravimetric capacity. After initial prediction of 62 potential electrodes, further screening criteria were applied to identify 18 promising electrodes based on their voltage, specific capacity, electronic conductivity, safety, stability, cost, and abundance. The validation of our approach was carried out by applying the models to known cathode materials, verifying the accuracy of the predictions. This innovative approach significantly accelerates the discovery of efficient and stable cathode materials for zinc-ion batteries, paving the way for more sustainable and high-performance energy storage solutions. This method also provides a robust framework for future materials exploration across various battery technologies.
Modular construction is becoming famous for buildings because it allows a high degree of prefabrication, with individual modules easily transported and installed. Building envelope optimization is vital as it protects buildings from undesirable external environments by expressly preventing the incursion of outside elements. This research uses a systematic literature review to appraise the characteristics of modular envelope panels, focusing on hygrothermal and energy performance. A total of 265 articles were subjected to rigorous filtering and screening measures. The findings reveal notable inconsistencies in modular envelope terminologies and a lack of consistent performance measures, which present significant challenges for research and development efforts. Furthermore, the results indicate a predominant focus on hygrothermal and energy performance in existing studies, with limited attention to environmental impacts and other performance factors. Moreover, the existing literature primarily addresses modular envelope solutions in temperate climates, offering inadequate information for hot and hot–humid climate contexts. To address these gaps, this study proposes categorizing modular envelope panels into four distinct categories: active, passive, smart, and green/vegetated wall panels. These findings will benefit researchers, architects, building envelope designers, policymakers, and organizations developing building performance-related assessment ratings, standards, and codes. The study suggests adopting the categorization of modular envelope panels provided in this study and developing modular panels suitable for hot and humid climates to fill the existing knowledge gap.
