International Journal of Innovative Technology and Interdisciplinary Sciences

Deep Excavation Protection for Urban Underground Construction: A Case Study of an Anchored Secant Pile Wall

Enkeleda Kokona — 2026-08-03

Modern urban underground construction requires retaining systems that satisfy both ultimate and serviceability limit states while ensuring reliable performance throughout excavation. This study presents the numerical evaluation and performance verification of an anchored secant pile wall through the case study of the deep excavation at “Çerçiz Topulli” Square, Gjirokastra, Albania. The study involved a 12.9 m deep excavation for a three-level underground structure in multilayered soil conditions. A reproducible plane-strain finite-element model, incorporating beam-on-elastic-foundation elements, was developed to simulate staged excavation, soil stratification, groundwater conditions, anchor activation, and construction sequences. The retaining system performance was assessed using normalized wall deflection, serviceability criteria, deformation prediction accuracy, and anchor efficiency. The maximum calculated lateral wall displacement was 12.1 mm, corresponding to a normalized wall deflection (δmax/H) of 0.094%, which is substantially below the commonly accepted serviceability limit of 0.5%. Comparative analyses indicate that the prestressed anchor system reduced wall deformation by approximately 25% relative to an equivalent unanchored excavation, while controlled dewatering was required to maintain excavation stability. The proposed framework provides a validated methodology for the design verification and performance assessment of anchored secant pile walls in deep urban excavations. The findings offer practical guidance for optimizing retaining systems in multilayered soils and establish a transferable approach for similar underground infrastructure projects in complex urban environments.

An Empirical Decision-Support Framework Integrating Statistical Learning and Digital Twin for Predictive Maintenance Planning in Industry 4.0

Ram Ballabh Sinha — 2026-08-08

The integration of Digital Twin technology with advanced analytics has created new opportunities for predictive maintenance in Industry 4.0. This study develops and empirically validates a framework that examines how statistical learning capability and Digital Twin integration influence predictive maintenance performance. Data were collected through a quantitative cross-sectional survey of 210 industry professionals, and the proposed relationships were evaluated using Structural Equation Modelling (SEM) with bootstrapping. The findings show that statistical learning capability, when integrated, greatly improves predictive maintenance efficiency (β = 0.61, p < 0.001). Additionally, incorporating Digital Twin into predictive maintenance affects both predictive efficiency (β = 0.47, p < 0.001) and fault detection accuracy (β = 0.58, p < 0.001). Maintenance performance is largely mediated by the influence of predictive maintenance efficiency (β = 0.66, p < 0.001) on the link between statistical learning capability and performance (indirect effect β = 0.40, p < 0.001). The model explains well and adds to the body of literature providing an empirically proven relationship between statistical learning and Digital Twin technologies. The cross-sectional design of the study, however, may limit the generalizability of the results and studies based on real-time data and longitudinal data across sectors are recommended. In a practical sense, the results demonstrate the importance of having an integrated analytical and digital strategy for organizations to improve predictive capability, minimize downtime, and optimize maintenance planning.

Comparative Techno-Economic Feasibility Assessment of Onshore Wind Energy Deployment: A Multi-Site Analysis

Elena Bebi — 2026-08-08

Given Albania’s high dependence on hydropower and its sensitivity to hydrological fluctuations, identifying alternative renewable energy sources has become increasingly important. This study presents a comparative techno-economic feasibility assessment of onshore wind farm development in ten selected areas in Albania. Since studies simultaneously evaluating the technical and economic feasibility of multiple potential wind energy sites remain limited in Albania, this work provides a broader assessment of wind energy development feasibility. Wind resource assessment was performed using long-term wind atlas-based data where available complemented by on site measurements. WAsP software was used to assess the wind energy resources and to simulate the wind farm energy output. Weibull methodology was used for probabilistic assessment of wind speeds. Five wind turbine models were simulated. The Vestas V163 4.5 MW turbine at 126 m hub demonstrated the best technological and economic overall performance and was selected as the reference wind turbine for the economic assessment. The economic assessment was performed using RETScreen Expert software while sensitivity analyses were performed with respect to variations in installation cost, electricity price and discount rate. Wide range of values of the technical wind farm indicators and economic indicators was reported across the studied sites. Vau i Dejës, Mali i Rencit and Pukë can be considered to have the most favourable technical and economic indicators due to their high wind farm capacity factor and low LCoE value, combined with high NPV and IRR values. The LCoE varied from approximately 49 to 63 €/MWh. Notably, Mali i Rencit emerged as one of the best-performing sites despite not belonging to the highest wind power classes, indicating that feasibility also depends on other site-specific factors beyond wind potential. Furthermore, the comparison between WAsP and RETScreen highlighted the importance of a detailed and representative wind resource assessment for improving the reliability of wind farm evaluations. Overall, the findings provide a useful basis for identifying priority areas and supporting future investment decisions in Albania’s wind energy sector.

Public Debt and Foreign Direct Investment in Transition Economies: A Data-Driven Comparative Analysis of Crisis Dynamics and Threshold Effects

Bardhyl Dauti — 2026-08-09

This study examines the relationship between public debt and foreign direct investment (FDI) in six Western Balkan (WB-6) countries and eleven Central and Eastern European Union (CE-EU-11) countries during the period 2000–2023. The results, based on a dynamic panel threshold regression model with endogenous regressors, indicate that public debt has a statistically significant adverse effect on FDI across both debt regimes, with the estimated threshold ranging between approximately 69% and 72% of GDP. These findings provide evidence of a persistent crowding-out effect of public debt on foreign investment. While the magnitude of the effect varies across debt regimes, public debt consistently discourages FDI inflows. The findings also reveal strong persistence in FDI, with moderate inflation and institutional quality exerting positive effects. Furthermore, the positive interaction between EU membership and public debt suggests that foreign investors are more tolerant of higher public debt levels in Central and Eastern European EU countries than in the Western Balkans.