J. Exp. Theor. Anal., Volume 4, Issue 2 (June 2026) – 9 articles

Ground improvement for foundations supported on soft soils is traditionally problematic because of low bearing capacity and a large magnitude of settlement. One sustainable method for mitigating these problems is the use of stone columns (SCs), particularly geosynthetic-encased stone columns (GESCs), to improve load transfer, confinement, and consolidation. This review critically synthesizes recent advances in the analysis and design of SC systems using experimental investigations, numerical simulations, and machine learning (ML)-based methodologies. The article indicates that GESCs, when integrated with modern data-driven techniques, especially hybrid metaheuristic ML models, represent a reliable and sustainable solution for soft soil stabilization. Traditional analytical and empirical methods remain useful; however, they are often inadequate for very soft soils (Undrained shear strength (c_u) < 15 kPa), where excessive bulging and large deformations dominate system behavior. Consequently, intelligent hybrid modeling approaches are emerging as the next generation of optimized, data-driven design tools in geotechnical engineering. Different failure mechanisms of SCs, including bulging, punching shear, and general shear failure, are critically discussed along with the governing design parameters. Previous studies consistently indicate that spacing ratios within the range of s/D = 2–3 can improve the bearing capacity ratio (BCR) by approximately 50–100%. Numerical and experimental studies further demonstrate that SC systems can transfer nearly 60–80% of the applied load through stress concentration and soil arching mechanisms. Furthermore, the application of geosynthetic encasement enhances the performance of SCs in very soft soils by increasing confinement, reducing lateral deformation, and enhancing bearing capacity by nearly 3–6 times compared with ordinary SCs. The review also evaluates the growing role of artificial intelligence techniques in forecasting settlement and bearing capacity behavior. ML techniques such as artificial neural networks (ANN), support vector regression (SVR), random forest (RF), XGBoost, and hybrid metaheuristic–ML models have shown high predictive capability, often achieving prediction errors below 5%. Despite these advancements, many existing ML studies still suffer from limited datasets, a lack of generalization, and insufficient incorporation of physical mechanisms. Full article

Surface engineering strongly influences the performance, reliability, and safety of medical and biomedical devices, yet failures often originate at interfaces rather than in bulk materials alone. This review addresses the fragmented evidence base linking coating selection, interphase design, qualification testing, advanced characterization, and data-driven durability analysis. The objective is to provide an integrative, failure-mode-based framework for implants, reusable instruments, inhalation systems, diagnostics, wearables, and implantable electronics. A narrative synthesis of the peer-reviewed literature in coatings, biomaterials, electrochemistry, reliability, standards, and materials informatics was conducted, with qualitative tables used only when protocols were too heterogeneous for numerical pooling. The review compares physical vapor deposition (PVD), chemical and plasma-enhanced chemical vapor deposition (CVD/PECVD), atomic layer deposition (ALD), sol–gel/organically modified silica (ORMOSIL) hybrids, plasma polymers, parylene, bioactive or antimicrobial surfaces, and electronic encapsulation strategies. The main finding is that no universally superior coating exists; reliable performance depends on matching architecture and characterization to the dominant failure pathway, substrate compliance, geometry, sterilization or physiologic exposure, and the standards-constrained endpoint. The review further shows how electrochemical diagnostics, interfacial mechanics, multiphysics models, survival/reliability statistics, and carefully governed AI workflows can be combined to support service-life prediction and decision-oriented qualification. Full article

In both experimental and theoretical analyses of data, we often look to select a set of simple components that can be combined to create an appropriate model for the data. A convenient way to do this is to use quantile functions that can be added or transformed to obtain new distributions. In this work, we connect quantile statistics and mean absolute deviations (MADs) by deriving a general class of MAD-based shape metrics expressed as integrals of the quantile function, with a direct geometric interpretation. Our approach is applicable to distributions with finite mean that include many of the commonly used distributions, including those without a variance, such as the Pareto. When simple midpoint quadrature is used, the proposed metrics recover widely used quantile-shape metrics, including the interquartile range, Galton skewness, and Moore’s octile kurtosis as special cases. We further propose a C-Trapezoid quadrature approximation that combines cubic polynomial endpoint extrapolation with trapezoidal integration, achieving approximation errors that are significantly lower than those of the midpoint approximation for many common distributions. The proposed approximation provides simple-to-compute formulas for shape analysis and yields closed-form, non-iterative parameter-estimation formulas. These formulas are easy to compute and interpret, and they are applicable to a wide class of distributions, including those without an explicit cumulative distribution function or some with heavy tails. Unlike maximum likelihood estimation, the proposed method is more robust and has simple geometric interpretation. We illustrate the methodology with two detailed case studies. The proposed approach gives a simple way to quickly assess distributional shape without any specialized tools. Full article

Threaded cast iron fittings, including tees and elbows, are commonly used in fire suppression systems to connect piping components due to their simplicity, reliability, and compatibility with National Pipe Thread (NPT) standards. While extensively used in practice, their structural performance under seismic demands, particularly for large-diameter fittings, remains insufficiently understood. Previous studies have primarily focused on smaller-diameter fittings or have idealized the fitting body as rigid, overlooking the potential for internal deformation and rupture. This study addresses this knowledge gap through an experimental investigation of 3-inch and 4-inch gray cast iron threaded elbow and tee fittings subjected to loading. A total of 18 full-scale assemblies were tested across multiple configurations. Results showed that structural rupture of the fitting body was the dominant failure mode, with no specimens reaching the defined threshold for substantial leakage. Limited early-stage leakage was observed in select 3-inch tee configurations prior to rupture. Damage State 3 (DS3), defined as rupture, occurred at rotational capacities ranging from 0.0061 to 0.0179 radians. Fitting stiffness to failure averaged approximately 643 kip-ft/rad. Fragility models were developed within a probabilistic framework, incorporating variability due to material imperfections and assembly tolerances. The findings reveal a size-dependent shift in behavior: 3-inch fittings demonstrated greater pipe rotation prior to failure, while 4-inch fittings exhibited higher internal stiffness but reduced flexibility. These results provide moment–rotation relationships and fragility models to support analytical modeling and seismic vulnerability assessments, advancing the predictive understanding of large-diameter threaded cast iron fittings used in fire suppression systems. Full article

Soft operational faults can noticeably degrade the performance of heat pumps and influence key monitored variables, emphasizing the need for reliable Fault Detection, Diagnosis, and Evaluation (FDDE) strategies. The BEYOND project tackles this challenge by analyzing simultaneous soft faults using a calibrated simulation model informed by data from a dedicated test rig. Achieving reliable results depends on both accurate measurements and proper model calibration. However, sensor uncertainty and errors in sub-models and correlations calibration can compromise model reliability. This work investigates the influence of measurement accuracy and calibration quality on both experimental variables and simulation outcomes for a residential air-to-water heat pump operating in cooling mode, with particular focus on refrigerant charge estimation. Two sensor configurations—“low accuracy” and “high accuracy”—are assessed, representing commercial- and laboratory-grade instruments, respectively, along with two corresponding calibration strategies. In the low-accuracy case, uncertainties around 10% were found for cooling capacity, energy efficiency ratio, and refrigerant mass flow rate, whereas high-accuracy setups reduced these to approximately 3%. Ultimately, the comparison between experimental and model-derived uncertainties confirms that achieving reliable predictions requires a balanced investment in both high-quality instrumentation and careful model calibration. Overall, this study serves as a crucial tool during the preliminary design of an experimental setup, assisting in the selection of a sensor suite that ensures not only the reliability of secondary variables and KPIs but also a robust and accurate calibration of physics-based models using the acquired experimental data. Full article

This study demonstrates the application of Laser-Induced Breakdown Spectroscopy (LIBS) for the elemental analysis of agricultural soils in South Punjab, Pakistan. Soil degradation due to intensive farming, imbalanced fertilizer use, and declining organic matter has reduced crop productivity in the region. To address this, rapid and accurate soil diagnostics are essential. LIBS, coupled with Calibration-Free analysis (CF-LIBS), was employed to quantitatively determine the concentrations of major and trace elements—including calcium, silicon, iron, aluminum, magnesium, titanium, potassium, sodium, lithium, and barium—without requiring chemical standards. Plasma characterization was performed using the Boltzmann plot method, yielding temperatures between 7750 and 9000 K, and electron number densities were derived from Stark-broadened spectral profiles. The results reveal significant spatial variability in elemental composition, reflecting differences in land use and irrigation sources. This work confirms LIBS as a versatile, efficient, and reliable tool for soil health assessment, offering a practical solution for monitoring soil nutrients and supporting sustainable agricultural management in resource-limited settings. Full article

The TWC Sigma model, part of the Topological Weighted Centroid (TWC) family, is introduced as a spatial framework for source localization in systems where network information is incomplete or unavailable. Its architecture relies on two alternative approaches: one based on nonlinear correlation, capable of capturing complex spatial dependencies among observed signals, and another based on supervised neural networks, which use adaptive learning on a discretized spatial grid to estimate the probability of hidden source localization. In both cases, TWC Sigma provides a robust and consistent mechanism to estimate the probable positions of hidden sources using only spatial coordinates and signal intensity. Applications on both synthetic and real-world datasets—such as those collected by Minna-no Data Site on post-Fukushima radiocesium contamination—confirm the model’s ability to identify both primary and secondary emission zones with strong spatial coherence. These results highlight TWC Sigma as an efficient and interpretable model that can be used both independently and as a complementary tool to more complex network-based frameworks, offering rapid and reliable localization even in the presence of sparse, noisy, or heterogeneous data. Full article

Robust control, adaptive control, and adaptive control allocation methods can create resilient systems that are able to handle uncertainties as well as unknown deficiencies in actuator effectiveness. The capabilities of these methods can further enable advanced missions for autonomous space systems. Thus, in this paper, a resilient control with an adaptive control allocation method is proposed and implemented on a vehicle with 3 degrees of freedom (DoF) that operates with eight thrusters to reduce the impact of external uncertainties as well as unknown effects of the actuator. Specifically, the method includes a combination of sliding mode and novel adaptive control design elements to ensure trajectory tracking in the presence of uncertainties. Moreover, an adaptive control allocation method is also introduced to obtain the desired forces and moments in the presence of unknown effects of the actuator. The boundedness of the closed-loop system is proven with Lyapunov stability analysis. The proposed controller results are compared to a baseline sliding mode controller without adaptive control and adaptive control allocation enhancement, where different uncertainties and unknown actuator degradation, as well as failure cases, are considered within several experimental cases under external fan-induced disturbances. The experimental metrics, including integral squared tracking error, maximum tracking error, actuator effort, actuator impulse, and settling time, are provided. Across all cases, the proposed method reduces the integral squared tracking error, improves settling time, and significantly improves yaw regulation compared to a baseline sliding mode controller. This, in turn, yields a slightly increased control effort for the proposed method. Full article

Electrocardiogram (ECG)-based biometrics have emerged as a promising solution for continuous and intrinsic human identification; nevertheless, the robustness of these systems under realistic noise conditions remains a critical challenge for practical deployment. This work presents a theoretical and experimental analysis of how different noise types and levels affect ECG biometric recognition by comparing three methodological families: fiducial-based approaches using morphological features with traditional classifiers such as SVM and k-NN, non-fiducial methods based on signal compression and global descriptors, and Deep Learning models. Controlled distortions and additive noise injection into public ECG databases enable systematic quantification of feature degradation. Experimental validation is performed using the CardioWheel system, a real-world in-vehicle ECG acquisition platform, to evaluate performance under realistic motion and noise conditions. The methodological framework proposed for robustness evaluation and noise-aware training is inherently generic and can be extended to other biometric tasks subject to noise. Results show that different algorithmic families exhibit distinct resilience profiles under noise contamination and reveal a practical signal quality boundary for reliable ECG biometric recognition, with performance deteriorating under severe noise conditions. Noise-aware training improves robustness, particularly for Deep Learning and SVM-based classifiers, highlighting the trade-off between interpretability and robustness. By bridging theoretical analysis and applied experimentation, this work provides practical signal quality guidelines for real-world ECG biometric systems. Full article