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Intelligent, Resilient, and Integrated Infrastructure Systems
IJTDI
Intelligent, Resilient, and Integrated Infrastructure Systems (IR2IS)
JAFAS
ISSN (print): 3107-3476
ISSN (online): 3107-3468
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2026: Vol. 1
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Intelligent, Resilient, and Integrated Infrastructure Systems (IR2IS) is a peer-reviewed, open-access journal that publishes research on infrastructure systems viewed as interconnected and evolving systems. The journal focuses on analytical, modelling, and system-oriented studies that examine how infrastructure systems are designed, analysed, monitored, operated, and managed under conditions of uncertainty, interdependence, and increasing performance requirements. IR2IS welcomes contributions that present rigorous system-level analysis supported by empirical, computational, or applied investigation. Particular attention is given to research on system integration and interdependencies, intelligent monitoring and operation, resilience and reliability assessment, risk and safety analysis, and decision-support approaches for infrastructure systems. Relevant application domains include water, energy, transportation, environmental, and urban infrastructure systems. The journal encourages interdisciplinary work that draws on infrastructure engineering, systems analysis, data analytics, and decision science, provided that the infrastructure-systems perspective and analytical contribution are clearly articulated. IR2IS is published quarterly by Acadlore, with issues released in March, June, September, and December.

  • Professional Editorial Standards - All submissions are evaluated through a standard peer-review process involving independent reviewers and editorial assessment before acceptance.

  • Efficient Publication - The journal follows a defined review, revision, and production workflow to support regular and predictable publication of accepted manuscripts.

  • Open Access - IR2IS is an open-access journal. All published articles are made available online without subscription or access fees.

Editor(s)-in-chief(1)
ruikun mai
College of Transportation, Tongji University, China
mairk@tongji.edu.cn | website
Research interests: Control Theory and Control Engineering; Rail Transit Electrification and Information Technology; Power System and Automation

Aims & Scope

Aims

Intelligent, Resilient, and Integrated Infrastructure Systems (IR2IS) is an international, peer-reviewed, open-access journal that publishes research on how contemporary infrastructure systems are conceived, analysed, monitored, operated, and governed through intelligent, resilient, and integrated approaches in the presence of complexity, uncertainty, and evolving performance requirements.

The journal treats infrastructure not as isolated physical assets, but as interconnected and dynamic systems composed of technical components, operational processes, information flows, and decision mechanisms. It focuses on system-level perspectives that examine how infrastructure systems function, interact, and adapt over their life cycle, including planning, design, operation, maintenance, adaptation, and renewal.

IR2IS places particular emphasis on the analytical and methodological foundations underlying intelligent and resilient infrastructure systems. This includes how system structures and interdependencies are modelled, how monitoring and sensing information is incorporated into system operation, how decisions are supported under uncertainty, and how system performance, reliability, safety, and resilience are assessed and enhanced in practice.

The journal aims to advance understanding of how intelligent mechanisms—such as data-informed analysis, adaptive operation, and decision-support frameworks—are integrated within infrastructure systems to improve coordination, robustness, and long-term functionality. Contributions are expected to engage substantively with system modelling, analysis, evaluation, or decision processes, rather than providing purely descriptive accounts of technologies or case studies without analytical depth.

IR2IS provides a forum for research at the intersection of infrastructure engineering, systems science, data analytics, and decision-making, where the system perspective remains central. Submissions may address how multiple infrastructure domains are integrated, how system resilience is designed and evaluated, and how intelligent approaches improve infrastructure performance in real-world engineering and operational contexts.

The journal publishes conceptual, methodological, computational, and applied studies that advance knowledge of infrastructure systems as complex and evolving entities, supporting transparent, evidence-based, and analytically grounded decisions in engineering practice, infrastructure management, and policy formulation.

The journal is published quarterly by Acadlore and follows a structured peer-review process and established editorial standards to ensure clarity of argument, transparency of methods, and consistency in the evaluation of submitted work.

Key features of IR2IS include:

  • The journal addresses infrastructure systems as integrated and interdependent systems, rather than focusing on isolated components, individual assets, or narrowly defined engineering disciplines;

  • It emphasises intelligent and resilient approaches to infrastructure analysis and operation, with particular attention to how information, models, and decisions are embedded within system processes;

  • The journal values contributions that present explicit analytical, modelling, or evaluative frameworks, supported by clearly stated assumptions, methodological justification, and systematic evidence;

  • Research on resilience, reliability, safety, and robustness of infrastructure systems is encouraged, where these aspects are examined through formal system analysis, modelling, or decision-support approaches;

  • Governance, management, and policy dimensions of infrastructure systems are considered where they are analytically integrated into system operation, performance evaluation, or decision processes, rather than treated as standalone policy commentary;

  • The journal encourages comparative, cross-system, and cross-method studies that clarify how different analytical or operational approaches influence infrastructure system behaviour and outcomes;

  • The editorial and review process emphasises methodological transparency, clarity of system definition, and reproducibility of analysis, enabling published research to be critically assessed and meaningfully reused.

Scope

IR2IS welcomes original research articles, theoretical contributions, systematic reviews, and well-founded empirical or computational studies that provide analytical insight into intelligent, resilient, and integrated infrastructure systems, including but not limited to the following areas:

Infrastructure Systems and Integration

  • Water, energy, transportation, environmental, and urban infrastructure systems

  • Integrated and interdependent infrastructure systems

  • Cross-sectoral infrastructure coupling and coordination

  • System architecture and functional integration across infrastructure domains

  • Life-cycle analysis and system evolution of infrastructure systems

Intelligent Monitoring, Operation, and Control

  • Monitoring, sensing, and diagnostic methods for infrastructure systems

  • Data-informed and adaptive operational strategies

  • Intelligent control and optimisation of infrastructure system operation

  • Predictive maintenance and condition-based management

  • Integration of monitoring data with operational decision-making

System Modelling, Simulation, and Analytics

  • Modelling and simulation of infrastructure systems and networks

  • System dynamics, network-based, and agent-based approaches

  • Statistical and data-driven analysis of infrastructure performance

  • Hybrid modelling frameworks combining physical models and data analytics

  • Digital representations and virtual system environments

Resilience, Reliability, and Safety Analysis

  • Assessment of infrastructure system resilience and robustness

  • Reliability analysis and failure propagation in interconnected systems

  • Risk assessment and safety analysis of infrastructure operations

  • System response to uncertainty, disturbances, and extreme events

  • Performance-based design and evaluation of resilient infrastructure systems

Decision Support, Governance, and Management

  • Decision-support frameworks for infrastructure planning and operation

  • Integration of engineering analysis with management and policy decisions

  • Evidence-based infrastructure management and regulatory support

  • Institutional and organisational factors embedded in system operation

  • Case studies demonstrating integrated and resilient system decision-making

Evaluation, Validation, and Methodological Reflection

  • Validation and benchmarking of infrastructure system models

  • Comparative analysis of modelling, monitoring, and decision-support approaches

  • Sensitivity, robustness, and uncertainty analysis at the system level

  • Critical examination of methodological assumptions and limitations

  • Reproducibility, transparency, and interpretability of system analyses

Related and Emerging Topics

  • Intelligent and resilient infrastructure systems across spatial and temporal scales

  • Longitudinal and dynamic analysis of infrastructure system behaviour

  • Integration of infrastructure systems with environmental and societal contexts

  • Scenario-based analysis and long-term infrastructure system planning

  • System-oriented approaches to infrastructure transformation and adaptation

Articles
Recent Articles
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Abstract

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Spectrophotometric online sensors are widely deployed for nutrient monitoring, but their accuracy deteriorates under turbid conditions. Existing compensation algorithms, whether single-coefficient linear subtractions or log-ratio expressions embedded in the analytical formula, often introduce new errors at low turbidity or saturate beyond the calibration range. This study develops a unified residual-learning framework that retains the certified instrument output as a baseline and adds a data-driven correction. The framework is applied to three nutrient parameters: ammonia nitrogen (NH4-N), total nitrogen (TN), and total phosphorus (TP). Controlled turbidityaddition experiments yielded 72 NH4-N samples (4 concentrations × 6 nephelometric turbidity units (NTU) levels, 0–500 NTU), 56 TN samples (4 concentrations × 7 NTU levels, 0–500 NTU, on two instruments), and 219 TP samples (4 concentrations × 8 NTU levels, 0–1034 NTU). Three model families were trained on each dataset: a three-parameter polynomial (Approach A), a Ridge regression on eight engineered log-features (Approach B), and a residual eXtreme Gradient Boosting (XGBoost) with monotone constraints (Approach C). Performance was evaluated by random 5-fold and leave-one-concentration-out (LOCO) cross-validation, stratified by low (<100 NTU) and high (≥100 NTU) turbidity. Under random-fold testing, the Ridge production model reduced the mean absolute error (MAE) by 42.5% for TN, 75.6% for TP, and 7.6% for NH4-N. The XGBoost variant achieved 45.2%, 87.2%, and 75.6%, respectively. At the saturated 2.35 mg·L−1 NH4-N point, where the embedded log-quadratic formula fails, XGBoost reduced the MAE from 1.885 to 0.055 mg·L−1 (97.1% reduction), indicating that residual learning can correct this observed in-grid failure more effectively than the fixed-coefficient baseline tested here. LOCO testing showed analyte-specific generalization: TP retained its random-fold gains, whereas NH4-N exhibited concentrationextrapolation limits. The Ridge model offers predictable, interpretable correction; the XGBoost variant provides additional accuracy where calibration saturation dominates the error. Joint reporting of random-fold and LOCO accuracy is recommended as standard practice for AI-augmented water-quality sensors.

Abstract

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Airport service quality (ASQ) plays a central role in shaping passenger satisfaction, airport competitiveness, and the long-term performance of aviation infrastructure systems. Existing studies increasingly recognise airports not merely as service environments but as integrated infrastructure systems in which transportation networks, operational processes, information systems, and passenger-facing functions interact dynamically. However, current knowledge remains fragmented across service domains, evaluation methods, passenger characteristics, and industry benchmarking practices. This study investigates the analytical foundations and research evolution of ASQ from an infrastructure systems perspective. A systematic literature review was conducted following the PRISMA protocol using a Scopus-based corpus of 303 peer-reviewed publications published between 1976 and 2024, supplemented by major industry reports and benchmarking frameworks. The review synthesised evidence across airport service domains, service attributes, and key performance indicators (KPIs), passenger heterogeneity, survey methodologies, social media-based assessment approaches, and the interaction between airline and ASQ. The results showed that ASQ research has evolved from isolated service evaluation toward increasingly integrated and multi-dimensional assessment frameworks. Processing and non-processing service domains were found to exert asymmetric effects on passenger satisfaction, while substantial variations were identified across demographic, behavioural, geographic, and travel-related passenger profiles. The review further showed that industry benchmarking systems provide operational comparability but remain only partially aligned with academic analytical approaches. Several research gaps were identified, particularly in landside infrastructure evaluation, arrival-stage service assessment, integrated objective–subjective performance measurement, and system-level understanding of airport operations. The findings indicate that ASQ should be interpreted as an emergent property of interconnected infrastructure subsystems rather than as isolated service encounters. This study provides an integrated conceptual foundation for future research on intelligent, resilient, and evidence-based airport infrastructure management and supports more transparent and analytically grounded decision-making for airport operators, policymakers, and researchers.
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