Introduction

The rising intensity of climate and weather‑related extremes, coupled with seismic risks, threatens the resilience of the built environment. This paper presents an integrated framework for Structural Health Monitoring (SHM) and earthquake/climate proofing that unites AI‑driven sensor fusion, Digital Twins (DTs), and Smart Oracles within the blockchain‑enabled decentralized data and knowledge ecosystem developed by the Horizon Europe project BUILDCHAIN.

Methods

Heterogeneous structural, environmental, climatic, and meteorological data streams are merged into a unified analytical pipeline, where AI models process vibration, acceleration, temperature, humidity, and related signals to extract modal properties, detect anomalies, and update structural performance indicators. Trustworthy data flows are ensured through a Decentralized Oracle Network (DON) connected to InterPlanetary File System (IPFS)-based distributed storage. Climate and weather datasets—historical, real‑time, and forecasted—are transformed into Knowledge Assets (KAs), integrated into the Decentralized Knowledge Graph (DKG), validated through a reputation‑based oracle mechanism, and stored immutably on the IPFS. AI‑based fusion algorithms reconcile sensor observations with physics‑based (i.e., Finite Element) models, triggering automated updating routines that refine key structural parameters (i.e., masonry or timber elastic moduli), which critically influence predictive accuracy under seismic and climate‑induced loads.

Results

Pilot applications involving real buildings demonstrate the framework’s ability to reduce discrepancies between as‑designed and as‑built performance. In two tall cross‑laminated timber (CLT) buildings, AI‑driven fusion and DT‑based updating refined elastic modulus scaling factors derived from modal measurements, improving vibration‑serviceability predictions. Deployments in the Hospital Real of Granada and the Palazzo Poniatowski Guadagni validated the approach with open, scalable SHM architectures, enabling quasi‑real‑time operational modal analysis and dynamic DT generation.

Conclusions

Combining AI‑driven sensor fusion, DTs and Smart Oracles with decentralized data governance enhances SHM reliability, improves predictive performance, and supports real‑time assessment of structural condition and vulnerability under seismic and climate stressors.

Contemporary urban contexts are increasingly characterized by spatial fragmentation, environmental stress, and social vulnerability, requiring planning approaches capable of addressing complexity beyond traditional sector-based frameworks. From this perspective, landscape-oriented strategies provide an effective interpretative and operational lens for understanding cities as integrated socio-ecological systems, where environmental processes, spatial structures, and human practices dynamically interact. This contribution explores the role of landscape as a structuring framework for urban regeneration strategies in complex and fragile urban environments. Landscape is interpreted not merely as a physical or perceptual dimension, but as a multi-layered system integrating ecological networks, public spaces, cultural identity, and everyday urban practices. Such an approach enables the identification of spatial relationships, criticalities, and opportunities that can inform adaptive and inclusive regeneration pathways. The study adopts a qualitative and analytical methodology based on spatial interpretation, urban policy analysis, and the examination of contexts characterized by marginalization, infrastructural discontinuities, and environmental vulnerability. Particular attention is given to the capacity of landscape-oriented approaches to support integrated decision-making processes, enhance urban resilience, and promote socially inclusive transformations over time. The findings highlight how landscape-based strategies can contribute to more coherent and context-sensitive urban regeneration processes, fostering stronger connections between environmental quality, social inclusion, and spatial governance. The paper contributes to ongoing debates in urban science by reinforcing the role of landscape as a key driver of sustainable urban transformation.

ntroduction: Urban flooding and recurrent stormwater drainage failures are often associated with clogged storm drains and microdrainage constraints. Municipal urban operations departments routinely register unclogging work orders, creating an operational dataset that can be leveraged to detect recurrence patterns and support preventive decision-making. This study proposes a practical method to map recurrent “hotspots” using municipal unclogging work-order records in Guarujá, Brazil, covering January 2021 to December 2025.
Methods: Work orders were consolidated at the street-name (logradouro) and neighborhood levels. House-number information was intentionally excluded due to historical inconsistencies in address completion. To minimize noise from spelling variants and abbreviations, we applied text normalization and string-similarity grouping to merge equivalent street names. Hotspots were defined and ranked using recurrence metrics (work-order frequency per street) and temporal persistence indicators (reappearance across different months and years). Neighborhood-level summaries were produced to identify spatial concentration of recurrent streets.
Results: The approach generates a prioritized list of streets with the highest recurrence and persistence, distinguishing isolated episodes from chronic locations repeatedly demanding reactive maintenance. The resulting hotspot inventory can support preventive maintenance scheduling, coordinated field operations, and the identification of sites where structural interventions may be more effective than repeated reactive responses.
Conclusions: Municipal work-order records can function as an administrative “sensor network” to detect street-level drainage hotspots and inform evidence-based prioritization for urban resilience and climate adaptation. The method is low-cost, scalable, and transferable to other cities facing recurring flooding and drainage maintenance burdens.

Placemaking, as a tool within the discipline of urban design, promotes participation that moves from consultation towards co-production. Although the idea of participation where citizens are seen as co-authors rather than merely respondents has long been discussed in urban planning and design, in practice it still largely remains within a consultative framework. This paper argues that when placemaking is embedded within institutional structures, it can function as a governance model rather than simply as a project-based intervention. By examining governance transitions within urban contexts, the study explores how co-production can achieve meaningful urban design outcomes and strengthen inclusivity in public space development.

The study adopts a qualitative multi-case methodology combining document analysis, policy review, and comparative case synthesis of participatory models, tactical urbanism initiatives, and community-led stewardship mechanisms. The governance structures are analysed through the degree of citizen agency. institutional integration and continuity of engagement. A governance transition matrix is developed to distinguish between consultative, collaborative, and co-productive models within placemaking processes and organisational structures. The findings suggest that projects where participatory mechanisms are institutionally embedded demonstrate greater spatial adaptability, stronger long-term community ownership, and enhanced social inclusion. The research concludes that placemaking becomes a transformative urban design tool only when governance structures move beyond occasional consultation towards structured co-production. The paper positions placemaking as an institutional strategy capable of recalibrating power, responsibility, and spatial authorship.

Introduction: Indonesia's ambitious goal of becoming a "100 Smart Cities" nation faces a major socio-technical barrier: a significant civic-digital literacy gap that renders a viable trillion-dollar investment in infrastructure an operational liability. Although Internet penetration is high, a lack of understanding of urban complexities among citizens prevents their ability to participate in circular economies and climate mitigation. This paper introduces WeAR Smart, part of a pioneering Game-As-Reality (GAR) platform that connects the physical city with digitally enabled citizens through the use of an immersive, interactive, and educational application for climate adaptation. It aims to educate and engage citizens in “green” actions.

Methods: Using the GAMERS protocol (geste, ambiance, mechanics, engine, reality, and sustainability), we conduct a quasi-experimental pilot study (pre-post design) using 32 participants to utilize multimodal AI detection and augmented reality (AR) in the urban and climate resilience actions. Outcomes were measured using pre-post Urban Climate Literacy (UCL) surveys in knowledge (100-point scale), attitude (5-point scale), and in-app behavioral logging (15-point scale).

Results: Our program demonstrated significant improvements across all UCL dimensions. Knowledge scores increased from a baseline mean of 41.61 to 84.52 (p < 0.0001). Attitudes toward climate-positive behaviors improved from 2.96 to 3.81 (p < 0.0001). Verified and self-reported behaviors increase significantly from 4.28 to 13.84 (p < 0.0001).

Conclusion: This pilot study demonstrates that the GAR paradigm, with its WeAR smart, is prospective to increase urban and climate literacy. While it used a limited sample, this pilot provides a justified statistical foundation for future large-scale studies. It paves the way to increase its scale and integrate AI-driven solutions into formal policy management to ensure that cities move beyond being simply smart to becoming resilient against climate-induced disruptions with its citizen-centered intelligence.

Urban pluvial flooding occurs persistently in many secondary cities in the Global South. While previous urban resilience research has generally highlighted hydrological modelling and infrastructure performance, institutional decision-making processes remain underexplored, particularly in identifying when and how governments intervene. This study employs an Institutional Decision-Gate perspective to explore how fragmented authority, strategic priority programs, and budgeting structures affect adaptive flood governance in Gowa Regency, Indonesia.

This study employs a qualitative case study approach grounded in semi-structured interviews with key stakeholders from the Public Works Department, Regional Disaster Management Department, Regional Development Planning Department, Environmental Department, and sub-district officials. The Annual Government Work Plan, Medium-Term Regional Development Plan, Work and Budget Plan, and Participatory Development Planning Forum Reports were reviewed for triangulation. The data were analyzed using thematic coding and process tracing to identify iterative decision thresholds and filtering mechanisms.

The analysis shows a three-phase Institutional Decision-Gate model of flood mitigation governance. The first gate represents a legitimacy threshold; responses are elicited not only by the hydraulic severity of an event but also by public visibility, disruption to strategic sectors, and leadership attention. The second gate reveals constraints imposed by sectoral budgeting and annual fiscal allocations. Despite the existence of technical knowledge and experience within departments, interventions are shaped around fixed maintenance and capital budgets that limit flexibility. Finally, strategic prioritization shapes decision-making, as drainage programs must compete with other sectoral priorities and be consistent with development visions.

By integrating urban flood resilience within institutional decision-making processes, this research shows that adaptive capacity in secondary cities is influenced less by infrastructure issues than by sectoral budgeting mechanisms and fragmented fiscal authority. The Decision-Gate framework shows how legitimacy, capacity constraints, and strategic prioritization function collectively to structure adaptive governance outcomes.

Urban communities are facing intensifying risks from floods and other disasters, creating a growing need to connect scientific knowledge with everyday community experience. The continuing increase in anthropogenic activities on the planet has intensified climate change, leading to a higher frequency and severity of natural hazard-related disasters. Digital technological advancements, including monitoring systems that integrate sensors and machine learning, are being implemented in disaster management practices, yielding beneficial outcomes, yet risk communication and preparedness mechanisms remain fragmented across institutions, limiting public literacy, regulatory compliance, and informed development decisions. This study presents a participatory, AI-driven WhatsApp-based chatbot designed to enhance urban adaptive capacity by translating institutional hazard knowledge into actionable, location-specific community guidance. The methodology is grounded in a hybrid architecture that combines a user-centred design approach, with a modular multi-agent system. Additionally, the architecture integrates a collection of modular agents: a language agent facilitating multilingual interaction, an API agent providing live weather updates, and a scheduler agent. Moreover, the system design is supported by measures for overall performance, including response speed, retrieval confidence, language detection accuracy, and system dependability. A full expert review will be undertaken for planning and engineering professionals to systematically assess the relevance, accuracy, understandability, and comprehensiveness of the responses from the user's perspective. For planners and developers, this chatbot supports risk-sensitive zoning interpretation and decision-making, contributing to improved regulatory adherence and safer land-use practices. For individuals, the chatbot provides fundamental disaster awareness, preparedness tips, relief information, and participatory reporting, thereby increasing awareness before and after the disasters. This study demonstrates how digital innovation can facilitate the operationalisation of Sustainable Development Goals, which directly address SDG 11 and 13—Sustainable Cities and Communities and Climate Action—and indirectly SDG 9 and 17—Industry, Innovation and Infrastructure and Partnerships for the Goals.

Mediterranean historic districts are commonly perceived as climatically responsive due to compact urban form, shaded street canyons, and courtyard typologies. Yet their actual performance under intensifying heat extremes remains insufficiently evaluated within multidimensional Urban Heat Vulnerability (UHV) frameworks. This study develops an artificial intelligence–assisted spatial modelling approach to examine how heritage urban fabric influences heat vulnerability across two climatically comparable but morphologically distinct contexts: Amman and Seville.

High-resolution spatial data are integrated with three-dimensional urban form metrics derived from digital surface models, alongside thermal indicators and socio-demographic proxies structured under the exposure–sensitivity–adaptive capacity paradigm. Fine-scale segmentation of urban fabric enables the delineation of coherent morphological units, facilitating micro-scale analysis of enclosure ratios, canyon geometry, vegetation distribution, and topographic modulation. These spatial descriptors are subsequently incorporated into predictive vulnerability modelling to quantify the extent to which urban morphology mediates heat exposure and adaptive capacity.

Comparative findings indicate that while compact heritage configurations may attenuate direct solar gain in certain geometries, limited vegetation coverage, material thermal inertia, and socio-economic constraints can amplify the risk of residual heat. Marked differences between the morphology of Amman and the courtyard-dominated fabric of Seville underscore the critical role of three-dimensional structure in shaping intra-urban thermal inequities. By integrating urban fabric analysis within a vulnerability-oriented framework, the study advances a transferable methodology for evaluating climate resilience in historic districts. It supports heritage-sensitive adaptation strategies in rapidly warming Mediterranean cities.

Rapid urbanization and climate change are increasing environmental stressors such as heat waves, air pollution, and noise in densely populated cities. These stressors disproportionately affect vulnerable communities and are closely linked to neurological and mental health challenges, including anxiety, cognitive strain, sleep disruption, and reduced resilience. However, most climate and health prediction models focus mainly on improving accuracy and often overlook fairness, which can unintentionally reinforce existing social inequalities embedded within urban systems and public infrastructure.

This study presents FairGNN Climate, a graph based framework designed to predict urban neural resilience under climate stress while incorporating fairness considerations directly into the learning process. The system represents urban populations as interconnected graphs that combine environmental exposure, socioeconomic conditions, health indicators, and demographic information. By modeling relationships between neighborhoods and shared climate exposure, the framework captures patterns of correlated vulnerability that traditional independent sample models often miss. Fairness measures, including demographic parity and equalized odds, are integrated during training to reduce disparities across different population groups.

The framework is developed as a complete decision support platform that enables resilience prediction, fairness evaluation, geospatial vulnerability analysis, and counterfactual policy simulation. Applied to real urban data from Indian cities, the results demonstrate that the model produces interpretable resilience estimates and highlights structural disparities that require continued monitoring, responsible governance, and sustained policy level attention.

Global urbanization has grown rapidly over the past century, increasingly expanding into areas prone to natural hazards. However, insufficient planning has led to informal expansion and a lack of up-to-date cadastral data and base maps necessary for informed urban governance and effective disaster risk management. In response to this problem, automated Scan-to-BIM workflows have emerged as a strategy for the 3D reconstruction of buildings to support the updating of urban cadastres. Although unmanned aerial vehicle (UAV) technologies enable efficient data acquisition using photogrammetric techniques and LiDAR sensors, a gap remains in the comparative evaluation of their performance in detecting building footprints within automated Scan-to-BIM frameworks for urban applications. In this context, the present study develops a comparative evaluation of both approaches.

Two flight campaigns were conducted over the same study area: (1) a DJI Matrice 4E for photogrammetric data acquisition and (2) a DJI Matrice 400 equipped with a Zenmuse L2 LiDAR sensor. Georeferenced point clouds were generated from both flights and processed using an automated Scan-to-BIM workflow to produce georeferenced BIM models at Level of Development (LOD) 100, representing building volumes. Performance was quantitatively evaluated based on geometric accuracy, level of detail, and model integrity.

The results highlight the differences between the two technologies: LiDAR demonstrated greater consistency in capturing complex geometries and fewer gaps in dense urban areas, achieving better volumetric definition, while UAV photogrammetry offered competitive planimetric accuracy and advantages in terms of cost and acquisition time. Overall, this confirms the feasibility of integrating UAV technologies into Scan-to-BIM workflows for the generation of 3D urban cadastres, contributing to urban planning and risk management.