http://www.ijrrs.ir/ International Journal of Reliability, Risk and Safety: Theory and Application en daily 1 Sun, 01 Feb 2026 00:00:00 +0330 Sun, 01 Feb 2026 00:00:00 +0330 http://www.ijrrs.ir/article_239291.html The rapid integration of Artificial Intelligence (AI) into psychotherapy has transformed psychological evaluation and intervention, while simultaneously raising critical concerns regarding clinical reliability, ethical integrity, and patient safety. This qualitative case study examined the perspectives of an experienced clinical psychologist on the risks, trustworthiness, and safety considerations associated with AI-based psychological evaluations. Data were collected through a semi-structured, in-depth interview and were analyzed thematically following Braun and Clarke’s (2006) framework. Three overarching themes emerged: (1) clinical and social risks, including algorithmic mislabeling, decontextualized interpretation, and erosion of the therapeutic alliance; (2) technical and clinical reliability, reflecting the discrepancy between AI accuracy in objective testing and its limitations in capturing emotional and cultural nuance; and (3) ethical and justice-oriented concerns, encompassing algorithmic bias, data confidentiality, client dependency, and the need for professional training and regulatory oversight. The resulting conceptual model, described as the “Risk–Trust–Ethics Triad,” demonstrates a dynamic, interdependent relationship among these dimensions, suggesting that any imbalance—technical, moral, or human—undermines the stability of the system. Findings highlight the necessity of human supervision, ethical governance, and culturally sensitive design as prerequisites for the deployment of trustworthy AI in psychotherapy. The study concludes that the sustainable integration of AI requires a human-centered framework in which technology functions not as a replacement for clinical judgment, but as an ethical partner that enhances therapeutic insight and judgment. http://www.ijrrs.ir/article_239077.html Electric Submersible Pumps (ESPs) are essential artificial lift systems that enable sustained hydrocarbon production across diverse reservoir conditions. However, ESPs operate in some of the most severe downhole environments in the oil and gas industry, characterized by extreme temperatures, high pressures, corrosive fluids, and abrasive particulates, resulting in frequent failures and costly workovers. Traditional maintenance strategies, including reactive and preventive approaches, have proven inadequate for addressing the operational, economic, and environmental challenges posed by ESP failures. Predictive maintenance, enabled by advances in IoT sensor technologies, edge and cloud computing, digital twins, and artificial intelligence, represents a significant advancement in condition-based monitoring and reliability management of ESP systems. By continuously monitoring system health, detecting anomalies early, and accurately forecasting failures, predictive maintenance significantly reduces downtime, lowers operational expenditure, enhances energy efficiency, and supports environmental stewardship. This paper presents a comprehensive descriptive analysis of predictive maintenance for ESP systems. It begins by examining the role of ESPs in hydrocarbon production, the limitations of traditional maintenance, and the economic drivers for a reliability-centered strategy. It then explores the technical foundations of predictive maintenance, including data acquisition, analytical models, and key performance indicators. Operational challenges, benefits, and global adoption trends are analyzed through real-world data, and a detailed case study highlights successful implementation in offshore operations. Future directions such as explainable AI, blockchain for maintenance traceability, edge computing advancements, and holistic adoption pathways are also discussed. By integrating technical depth with practical insight, this paper positions predictive maintenance as a cornerstone of modern upstream digital transformation, enabling safer, more reliable, and more sustainable ESP operations. The study combines a structured review of predictive maintenance technologies with a validated offshore case study to present an integrated, reliability-centered framework for ESP asset management. http://www.ijrrs.ir/article_240060.html Safety engineers try to ensure that systems are designed, developed, and operated in a manner that is safe, reliable, and compliant with applicable regulations and standards. Safety is defined as the backbone of the aviation industry. To achieve and maintain the desired level of safety, risk analysis stands out as a critical analytical tool. This scientific approach is employed to identify, evaluate, and manage hazards effectively. The primary objective of risk assessment techniques, in addition to ensuring compliance with legal requirements, is to mitigate operational risks and enhance overall safety. Engine failure constitutes a hazardous and emergent situation in aviation and aeronautical engineering, with the potential for uncontrollable consequences. While the aviation safety of modern aircraft is significantly influenced by the low-pressure turbine section, this particular section has not received proportionate consideration. This paper addresses identification and managing risks in the third and fourth stages of the low-pressure turbine in a JT8D engine. A framework is proposed for this purpose, utilizing historical failures' data, failure mode and effect analysis, and event tree analysis techniques. Both economic and technical effects are taken into account during the thorough investigation of hazards. Investigations have shown that blade shroud wear can cause vibration and fatigue, which can eventually result in blade fracture and engine cover rupture. Based on the safety risk assessment, some recommendations as inspection and maintenance procedures are suggested. Regularly inspecting, monitoring vibration, accurately measuring torque, and visiting shops are the recommended actions. Some airlines have conducted this procedure, and the experience shows that it effectively reduces the risk rate in the field. http://www.ijrrs.ir/article_240656.html Strategic alliances are vital for organizations seeking competitive advantage, access to new markets, and opportunities to share resources. However, forming such alliances entails considerable risks, particularly in the area of partner selection. This study integrates insights from existing literature with empirical evidence from Iranian small and medium-sized enterprises (SMEs) in the aerospace sector to identify key criteria influencing partner selection and their evolving importance after alliance formation. The findings highlight that technological capability, financial health, and prior experience are critical factors in the initial stages of partner selection, while aspects such as managerial compatibility and cultural alignment gain significance as alliances progress. The research underscores the dynamic nature of strategic partnerships and emphasizes the necessity of continuous evaluation to ensure long-term effectiveness. By addressing the interplay between trust and risk and exploring the complexities of interrelated variables, the study contributes to the refinement of risk management frameworks. The results provide both theoretical insights and practical guidance for organizations aiming to improve their strategic decision-making in forming and sustaining successful alliances. http://www.ijrrs.ir/article_241212.html This paper develops a framework for the reliability of a stress-strength model that assumes the strength variable (Y) is constrained by two dependent stresses (X_1 and X_2). In contrast with classical forms of this study, which typically assume independence of the stress variables, this one considers the realistic aspect of the dependence relationship between the two variables that, if ignored, can result in biased estimates of reliability. There are two dependence structures that we use. The Marshall–Olkin model is a formulation that includes common shock effects and the Farlie–Gumbel–Morgenstern (FGM) copula provides analytical tractability and flexible dependence models without using the marginal distributions. These two structures are compared in this study. Both maximum likelihood estimation and Bayesian estimation are employed to acquire point and interval estimates for the model parameters. The Bayesian approach is implemented with the Metropolis–Hastings algorithm. The results of the simulation indicate that the Bayesian method tends to give more precise estimates with less bias, lower Mean Squared Error, and more efficient interval estimates, particularly when the sample size is small. Both models are adequate for describing the underlying dependence; however, the FGM copula provides more accurate results for moderate dependence systems with more efficient interval estimates. The practical utility and soundness of the suggested methodologies are also demonstrated through a real-data example. In general, the paper provides a comprehensive comparison under a unified estimation framework that analyzes dual dependence structures in the estimation of R=P(X_1 http://www.ijrrs.ir/article_242932.html This paper addresses the critical challenge of determining the optimal number of re-inspections in industrial applications to detect non-apparent failures. These failures, often subtle or latent, can have significant consequences if they reach the end-user. We develop a comprehensive mathematical model aimed at minimizing the total expected cost associated with defect detection. This model intricately considers the probability of detecting defective items at each inspection stage, acknowledging the inherent imperfections and potential for false positives and false negatives in any inspection process. The core of our approach lies in balancing the direct cost of conducting an inspection against the indirect but potentially substantial risk and cost of releasing undetected nonconforming items into the market or the next stage of production. Through a detailed mathematical formulation, we derive an optimal re-inspection policy. An illustrative example is provided to demonstrate the practical applicability of our model, followed by a thorough sensitivity analysis to assess the impact of key process parameters on the derived inspection policy. This work contributes to improving quality control by providing a data-driven framework for optimizing inspection resources. http://www.ijrrs.ir/article_243018.html Reliability of a PG 6541B gas turbine (Unit No. 03) at the Shazand Petrochemical Complex was assessed using time-between-failures (TBF) data collected after the 2019 major overhaul. Thirty uncensored TBF intervals (31 forced-outage events) were recorded over 1692 operating days (June 2020–January 2025). Treating the turbine as a repairable system and adopting a system-level functional renewal (as-good-as-new) approximation, the empirical mean time between failures (MTBF) was 56.40 days (95% confidence interval (CI): 40.63–83.59 days). Comparative distribution fitting (maximum likelihood estimation) selected the two-parameter Weibull model as the best parametric summary of inter-failure times, with shape β = 1.74 (95% CI: 1.32–2.21) and scale η = 1084 days; in this repairable-system context, η is interpreted as the scale of the inter-failure distribution and not as a deterministic lifetime. To address conceptual ambiguity, MTBF is defined as the arithmetic mean of observed TBFs, whereas the Weibull-derived mean inter-failure time (denoted as MTTF in this paper) represents a model-based expectation under the stated renewal-type approximation and can differ substantially in right-skewed small samples. Goodness-of-fit tests (Anderson–Darling and Kolmogorov–Smirnov) supported the adequacy of the selected model. Trend diagnostics showed no statistically significant monotonic trend in the failure process (Kendall τ = −0.097, p = 0.46; Laplace U = 0.761). Sensitivity checks indicated robustness to extreme observations (β changed from 1.74 to 1.68 when the shortest and longest TBFs were excluded). This work contributes an uncertainty-aware industrial case study based on screened long-term field records, providing defensible reliability indicators and interpretation guidance for maintenance planning in petrochemical gas turbines. http://www.ijrrs.ir/article_243255.html Gas Pressure Reduction Stations (GPRS) play a critical role in natural gas transmission and distribution systems by ensuring safe and reliable pressure regulation at the interface between high-pressure pipelines and downstream networks. Failures in these stations can lead to supply disruptions, equipment damage, safety hazards, and broader societal and environmental impacts. While existing research predominantly addresses GPRS safety and reliability through technical and quantitative approaches, the underlying policy assumptions, boundary judgments, and stakeholder considerations often remain implicit. This study applies Critical Systems Heuristics (CSH) to systematically analyze safety and reliability policies in GPRS as socio-technical systems. The research adopts a qualitative design combining a systematic literature review with semi-structured interviews conducted with experts from operational, maintenance, safety, and regulatory domains. The twelve CSH boundary questions are used within an “is/ought” framework to contrast current policy framings with expert-informed normative expectations. The analysis reveals that prevailing GPRS safety and reliability policies are largely characterized by instrumental purposes focused on operational continuity and regulatory compliance, organization-centered beneficiary definitions, reliance on lagging and compliance-based performance indicators, centralized decision-making structures, and limited mechanisms for learning, challenge, and stakeholder representation. In contrast, experts emphasized that effective safety governance should explicitly prioritize system resilience, public and environmental protection, prevention and learning, transparent governance of trade-offs, and inclusive decision-making. Based on these findings, the study proposes a CSH-informed conceptual framework that synthesizes key boundary gaps and identifies policy enhancement directions aimed at strengthening adaptive capacity and social legitimacy. The results highlight the value of integrating CSH with conventional technical approaches to improve safety and reliability governance in complex and uncertain GPRS contexts. http://www.ijrrs.ir/article_243323.html Quantitative assessment of seismic performance is vital for establishing the safety and reliability of structural systems, especially in high-hazard regions. This study employs the probabilistic Demand Capacity Factored Design (DCFD) framework, integrating structural uncertainty, to evaluate the seismic safety of 12 medium-ductility steel moment-resisting frames designed according to the third (ASD) and fourth (LRFD) editions of the Iranian seismic code (Standard No. 2800) and the Iranian National Building Codes. The frames, varying in height (3, 5, and 8 stories) and bay count (3 and 5 bays), represent typical residential buildings modeled in OpenSEES and subjected to Incremental Dynamic Analysis (IDA) using 20 carefully selected ground motion records. Key uncertainties in demand and capacity are explicitly quantified following FEMA guidelines. Seismic performance is evaluated at Life Safety (LS, 10% AEP in 50 years) and Collapse Prevention (CP, 2% AEP in 50 years) levels. Results demonstrate that structures designed per the fourth code edition consistently achieve superior seismic performance and higher reliability indices than those designed per the third edition, owing to more robust drift limits and response modification factors. At the CP level, all frames meet safety objectives, while at the LS level, taller and more complex frames require fourth edition provisions to ensure reliability targets. These findings highlight the effectiveness of the DCFD framework, combined with IDA, for comprehensive, uncertainty-aware seismic safety assessment and underscore the enhanced protective capacity of updated code provisions for steel moment-resisting frames in high-risk seismic zones.