Jul 2026· Journal of Marine Science and Engineering· Vol 14, pp. 1352· 0 citations· 23 references
TL;DR
The proposed framework introduces a structured architecture combining engineering knowledge corpora, hybrid AI reasoning, and CAD-based interaction within a traceable and regulatory-aware workflow that gives a basis for integrating AI into marine CAD environments, aligned with newer digital ship design and Shipyard 4.0 approaches.
Abstract
This paper proposes a governance-aware methodological framework for the integration of Cognitive Artificial Intelligence into marine CAD environments. Unlike existing approaches that focus on isolated optimization or prediction tasks, the proposed framework introduces a structured architecture combining engineering knowledge corpora, hybrid AI reasoning, and CAD-based interaction within a traceable and regulatory-aware workflow. The contribution of this work is methodological rather than domain-exhaustive. The framework is illustrated through a conceptual pipe routing use case, selected as a representative high-constraint design subsystem involving spatial reasoning, compliance verification, and iterative coordination. A normalized performance evaluation model is defined to estimate improvements in design efficiency, convergence, compliance validation and rework reduction. Although the validation is conceptual, the results are consistent with reported improvements in AI-assisted engineering workflows. The proposed framework gives a basis for integrating AI into marine CAD environments, aligned with newer digital ship design and Shipyard 4.0 approaches.
Municipal governments face mounting pressure to deliver infrastructure services with constrained resources, driving interest in large language models (LLMs) for planning and operational support. Yet empirical evidence reveals a persistent gap between analytical capability and governance readiness: while LLMs can replicate structured reasoning, they frequently produce unverifiable outputs, lack regulatory awareness, and misinterpret local operational constraints. This study presents a hybrid human-AI governance framework designed for decision-making in urban infrastructure and municipal governance settings. Building on a thematic analysis of 78 coded responses from 20 infrastructure professionals and six commercial LLMs across three infrastructure decision scenarios, the study identifies where human and machine reasoning converge and diverge, and translates those empirical patterns into an operational governance model. The framework defines a five-stage workflow supported by a RACI (Responsible, Accountable, Consulted, Informed) matrix that clarifies roles: AI systems perform data synthesis and option generation, while human professionals ensure contextual judgment, ethical oversight, and final authority. Each workflow stage and role assignment is explicitly linked to specific thematic findings from the comparative analysis, ensuring the framework follows from evidence rather than prescriptive assumption. The model addresses algorithmic accountability, data governance, and digital inclusion by embedding oversight, training, and documentation standards into municipal workflows. Grounded in international AI governance frameworks including the EU AI Act, the NIST AI Risk Management Framework, and the OECD AI Principles, the framework offers a replicable model for cities to adopt LLM-assisted decision support in ways that safeguard equity, transparency, and public trust. An 18-month phased implementation roadmap provides municipal agencies with a realistic adoption pathway from readiness assessment through evaluation and scaling.
Alence Poudel, Carla Barrios, Paola De La Torre et al.· Discover Cities· 0 citations
Wastewater planning in the age of growing complexity is becoming increasingly difficult, calling for the joint consideration of environment-related, economic, technical, and social dimensions under conditions of uncertainty. Existing sustainability evaluation approaches such as life-cycle assessment (LCA), techno-economic analysis (TEA), and multi-criteria decision analysis (MCDA) provide a structured approach to the issue, but are mainly used in well-defined and simplified decision situations, limiting the comprehensive exploration of the system interactions. At the same time, digitalisation, together with artificial intelligence (AI), is transforming wastewater systems into information-rich settings, enabling new kinds of analysis and decision support. Yet, despite the growing use of AI in the wastewater sector, its application for sustainability-oriented infrastructure planning is still poorly conceptualized. This paper introduces a conceptual framework for decision support in sustainability-oriented wastewater infrastructure planning that brings together three components: data generation, AI-supported analysis, and evaluation with MCDA. Specifically, in this framework, the task of MCDA is the clear evaluation of heterogenous indicators, while the added value of AI lies in expanding the analytical horizon with structured and large-scale exploration of the decision space. This reframes the role of sustainability assessment from evaluating a few predefined system configurations to analysing trade-offs, uncertainties, and the robustness of alternative solutions across a wider range of system scenarios. The paper demonstrates the application of the proposed concept in an exemplary analysis of decentralized wastewater system planning. The results highlight the potential and pitfalls of the AI-based analysis and demonstrate that AI does not substitute for other types of assessment but complements them.
M. Starkl, N. Brunner, Anju Singh· Water· 0 citations
The increasing complexity of organizational, environmental, and socio-technical decision environments has created a need for intelligent systems capable of integrating heterogeneous information, identifying contextual relationships, and supporting decisions that balance operational objectives with long-term sustainability. This paper examines AI-drven semantic frameworks as an architectural approach for sustainable decision intelligence, with particular emphasis on semantic representation, contextual learning, multi-task learning, domain adaptation, and lightweight natural language processing. The study adopts a conceptual research-and-review methodology based exclusively on the supplied literature. The reviewed studies provide complementary theoretical foundations: parsing research demonstrates structured linguistic representation, multi-task learning explains how related tasks can share useful information, large language models illustrate scalable semantic inference, and domain-oriented tuning demonstrates mechanisms for adapting AI systems to heterogeneous contexts. These foundations are synthesized into a conceptual semantic decision framework comprising data interpretation, semantic representation, contextual reasoning, adaptive learning, decision synthesis, and sustainability-oriented evaluation layers. The analysis indicates that semantic intelligence can improve decision consistency by connecting heterogeneous information according to contextual meaning rather than treating individual observations independently. However, challenges remain regarding domain transfer, interpretability, computational efficiency, task interference, and the absence of explicit sustainability objectives within many existing AI learning paradigms. The paper therefore positions semantic AI as an enabling infrastructure rather than an autonomous decision-maker and proposes a research direction in which semantic representations, adaptive learning, and sustainability constraints are integrated into a unified decision architecture.
Dammi Ha· International journal of dat...· 0 citations
The first comprehensive synthesis linking AI technical capabilities with anticipatory governance theory is provided, offering a theoretically grounded, actionable pathway for practitioners navigating the digital transformation of project delivery, shifting the focus from controlling machines to cultivating symbiotic ecosystems.
Ken A. Charles· International Research Journ...· 0 citations
The transformation of multimedia design is experiencing structural shifts based on AI, interoperability, and regulatory considerations. Previous research focused on each of these factors individually; however, little attention was paid to their interactions in an integrated approach. In this paper, the Capability-Compliance-Sustainability (CCS) framework was proposed to discuss the evolution of multimedia design from 2026 onwards. The framework represents a result of a comprehensive review of current trends in AI architecture design, neural rendering, web-based GPUs, interoperability, accessibility, and AI governance tools. The three interconnected constructs were formed based on a synthesis of the reviewed literature: Capability, which implies the development of computational capabilities through AI; Compliance, which refers to regulation, accessibility, and provenance; and Sustainability, which includes infrastructure resilience, energy-efficient computation, and scalability. To illustrate the analytical utility of the conceptual model, selected multimedia design use cases are presented. The paper presents the potential of the CCS framework for multimedia creation for education, generative content, and 3D/XR. From a theoretical standpoint, the framework describes multimedia evolution as an ecosystem equilibrium management process.
John Rover R. Sinag· 2026 International Conferenc...· 0 citations
AI-enabled geospatial applications increasingly inform high-stakes decisions in crop type classification, flood risk assessment, and land use monitoring; yet current practice prioritises predictive accuracy whilst leaving responsible AI (R-AI) largely unaddressed. This work aims to operationalise R-AI practice for geospatial AI by proposing a conceptual view and a four-phase methodology that embeds four distinct R-AI characteristics into the model development lifecycle. A conceptual R-AI view is proposed that situates four characteristics - Privacy, Fairness, Transparency, and Explainability - across three operational levels: data, model, and prediction. A four-phase methodology operationalises each through concrete techniques and quantifiable acceptance criteria. The threshold values and phase ordering of the methodology are governed by the proposed Pareto-Adaptive R-AI Compliance (PARC) algorithm, which derives dataset-specific thresholds and characterises the interactions between the four characteristics. Evaluation is conducted through two experiments using the Temporal-Spatial Vision Transformer (TSViT): crop type classification on the publicly available PASTIS/EuroCrops benchmark, and urban flood risk classification on a real-world UK Sustainable Drainage Systems (SuDS) pilot with six fused geospatial data sources. Across both experiments, all twelve R-AI acceptance criteria are satisfied. Privacy protection reduces membership inference attack accuracy from 0.712 to 0.503, approaching the random-guess baseline. Fairness interventions narrow the geographic performance gap from 23.1% to 3.1%. Transparency verification reduces physically inconsistent model behaviour from 24.1% to 3.8%. Explainability mechanisms improve temporal attribution consistency from 0.41 to 0.857, and the attribution methods are independently validated through deletion, insertion, and perturbation-stability analyses. All four characteristics are achieved whilst retaining over 97% of baseline predictive utility. These findings demonstrate that responsible AI and predictive accuracy are compatible objectives in geospatial deep learning. The proposed approach offers a replicable and measurable pathway for embedding R-AI practice into geospatial AI development, validated across two structurally distinct real-world tasks.
M. Hassan, Bilal Sardar, Shareeful Islam et al.· SN Computer Science· 0 citations
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