Problems related to waste, particularly metal and battery waste, represent a significant and growing environmental challenge. While recent studies have explored either waste-derived materials in geopolymer concrete or conventional sacrificial anodes for corrosion protection, the integrated application of hybrid waste-based sacrificial anodes with supplementary cementitious systems for effective and sustainable cathodic protection in reinforced concrete remains largely unexplored. This study presents a novel approach by simultaneously utilizing aluminum can waste and used battery materials as an Al-Zn hybrid sacrificial anode system combined with fly ash and rice husk ash–modified geopolymer concrete for corrosion mitigation. The primary objective of this research is to evaluate the effectiveness of waste-based sacrificial anodes in inhibiting reinforcement corrosion while maintaining the mechanical performance of concrete. The study employed an experimental investigation with mechanical property evaluation and electrochemical monitoring. The results of early connection demonstrated that geopolymer concrete containing 6% rice husk ash and 94% fly ash of precursor achieved the optimal performance, exhibiting a depolarization value exceeding 100 mV, reduced corrosion potential in half-cell measurements, and a stable anode current output, confirming effective cathodic protection according to SACP criteria. Corrosion behavior was assessed through half-cell potential and depolarization measurements, while concrete performance was verified using key mechanical and durability indicators. These findings indicate that a waste-based sacrificial anode system offers a promising, environmentally sustainable alternative for corrosion protection in reinforced concrete infrastructure.
Effective control of methanol–water distillation is essential in dimethyl ether (DME) production due to strong nonlinearities and multivariable interactions. This study presents a comparative evaluation of fuzzy self-tuning proportional–integral (FSTPI) controllers with different initialization strategies in a 4×4 multivariable distillation system. Three variants—working-space (FSTPI-WS), working-range (FSTPI-WR), and PI-initialized FSTPI (FSTPI-PI Tuned)—are benchmarked against conventional PI controllers using a validated dynamic model implemented in MATLAB/Simulink with a C-based S-function for real-time adaptation. Controller performance is evaluated under ±5% setpoint changes and realistic disturbances in feed temperature and flow rate using integral absolute error (IAE), integral squared error (ISE), and interaction analysis. The results demonstrate that initialization strategy critically determines adaptive control performance. The FSTPI-PI Tuned consistently achieves the best performance, reducing IAE and ISE by more than 98% in the most interactive loop (condenser liquid level). For temperature-induced disturbances, IAE and ISE reductions exceed 98% and 99%, respectively. These findings indicate that fuzzy self-tuning is most effective when applied as an adaptive refinement layer atop a well-tuned PI controller, rather than as a standalone compensation mechanism, thereby providing a practical and computationally efficient solution for multivariable distillation control.
Crude oil with high pour point oil (HPPO) characteristics exhibits elevated viscosity at low temperatures, promoting the deposition of wax and asphaltenes along pipeline walls. These deposits restrict flow and can lead to partial or complete blockages, posing serious challenges to flow assurance, operational safety, and environmental sustainability. Despite extensive studies on deposition mechanisms, blockage detection methods remain fragmented, with no universally reliable or integrated solution. This study presents a systematic review combined with a bibliometric analysis of Scopus-indexed publications to map research evolution, trends, and technological advancements in blockage detection methods for crude oil pipelines. Bibliometric network mapping is applied to identify dominant research clusters and emerging themes. The review evaluates key detection techniques, including pressure wave analysis, gamma-ray inspection, computational fluid dynamics (CFD), and machine learning approaches. Reported results indicate that pressure wave methods achieve detection accuracies of around 5% with prediction errors up to 35%, while gamma-ray techniques show errors of approximately 5% with maximum deviations of 0.59 cm. CFD models effectively represent flow and deposition behavior but are limited in quantifying blockage size. Machine learning approaches demonstrate improved performance, with accuracies ranging from 80.67% to 89.96%, although their robustness depends on data quality and generalizability. The analysis highlights critical gaps, including limited accuracy under varying conditions, lack of methodological integration, and reliance on single-technique approaches that fail to capture the multi-physics nature of blockage formation. The novelty of this study lies in its integrated bibliometric–systematic synthesis linking detection performance, methodological limitations, and research trends, while proposing a hybrid framework combining physical sensing, numerical modeling, and artificial intelligence. This work provides a comprehensive reference and strategic direction for developing next-generation pipeline blockage detection systems.
Structural crack detection remains a key component of structural health monitoring for large-scale civil infrastructure, where manual inspection is costly and hazardous. While aerial imaging enables rapid data acquisition, reliable crack analysis is limited by the difficulty of obtaining high-resolution images at close range without risking UAV damage. To address this, we introduce a UAV equipped with a protective spherical shell that allows safe surface contact. Although CNN-based architectures such as FPN and U-Net are established for crack segmentation, their encoders struggle to model long-range dependencies and fine edge structures, which result in poor delineation of thin, low-contrast, and discontinuous cracks. This limitation remains a persistent research gap in aerial crack analysis. To address this gap, this study investigates two complementary strategies: (i) replacing conventional CNN encoders with hierarchical Transformer backbones to capture global contextual relationships, and (ii) introducing edge-aware input augmentation using luminosity-based grayscale and Sobel gradient maps to optimize boundary sensitivity. We conducted experiments on our CrackUAS dataset, which consists of aerial images collected from infrastructure inspection scenarios. Integrating a hierarchical Transformer encoder with Sobel edge augmentation yields consistent performance gains across architectures. The ablation study concludes that F1-scores increase by approximately 33% for U-Net and 13% for FPN relative to their baseline configurations. These results show that integrating Transformer-based feature encoding with edge-aware input augmentation improves aerial crack segmentation.
Road surface deterioration, particularly pothole formation, poses substantial threats to traffic safety and driving comfort, highlighting the need for timely and objective road condition assessment. However, past studies have predominantly focused on pothole detection without estimating pothole dimensions, while conventional inspection methods remain manual, inefficient, and subjective. To address these limitations, a deep-learning-based framework is proposed herein for joint pothole detection and dimension estimation using monocular road images. The proposed framework integrates the You Only Look Once (YOLO)v8 and Mask Region-Based Convolutional Neural Network (Mask R-CNN) models to detect potholes and estimate their geometric dimensions from road surface images. Image data were acquired using a GoPro Hero 8 camera across multiple road segments in Sumatra Selatan, Indonesia. The models were trained on Google Colaboratory using the YOLOv8x-seg architecture and subsequently integrated into a Flutter-based mobile application for practical deployment, while Leaflet JS was employed to interactively visualize pothole locations. Experimental results demonstrated detection confidence levels exceeding 92.22%, with stable performance observed on both local computing platforms and mobile devices. Application-level evaluations further confirmed robust cross-device functionality and yielded a usability score of 4.8875. These results indicate that the proposed approach provides an accurate and reliable solution for automated road condition monitoring, supporting more objective and data-driven road maintenance planning.
