Agentic AI describes a new generation of AI that automates tasks, pursues goals independently, makes decisions and proactively interacts with its environment. This study - based on a qualitative exploratory research design with expert interviews - aims at understanding how the use of such systems affects internal decision-making logic and organizational structures in companies. Results show that agentic AI changes decision-making processes. Decisions are being made by systems and humans, with humans mainly taking on supervisory or control functions. We provide a theoretical contribution to existing decision-making and organizational theories with practical recommendations for dealing with such autonomous AI systems.
Metro tunnel inspections face growing demands from urbanization and tight maintenance windows, yet manual and time-consuming inspection methods remain. While rail-bound vehicles, wheeled and tracked robots,´quadruped robots, and Unmanned Aerial Vehicles (UAVs) show promise for inspection tasks, no systematic evaluation addresses metro-tunnel specific constraints like track geometries, tunnel access and inspection windows for mobile platform choice. This paper reviews inspection platforms, derives evaluation criteria from literature, operator interviews, and observations, and quantitatively compares platforms across sensing, payload, speed, runtime, deployment, and locomotion. Results identify optimal use-cases for each system, guiding deployment strategies for automated metro-tunnel inspections.
Europe’s renovation rate remains below what is needed for climate-neutrality targets; scaling façade refurbishment demands standardised, data-driven automation. Current practice relies on bespoke BIM modelling and file-based scan-to-BIM pipelines, which are labour-intensive, sensitive to as-built deviations, and fragmented across design, production, and logistics. We present an end-to-end workflow that ingests 3D point-cloud surveys and performs geometric panelisation to derive storey-scale and small prefabricated façade panels. The system outputs IFC-compliant panel objects with cut-outs and metadata, synchronised via a self-hosted, versioned Speckle hub. A residential case study shows reduced manual modelling and reliable handover.
Cyberattacks on critical infrastructure (CI) generate rapid and immediate public reactions that shape trust, risk perception, and crisis communication. Existing literature lacks an empirical evidence-based understanding of how decentralized online communities shape narratives following cyber incidents on CI. This study examines social media public response and discourse patterns to CI cyber disruptions through a reddit-based multi-dimensional sentiment analysis of Florida water-treatment-plant incident. Results from 300-relevant posts indicate that discourse is highly time-sensitive, 50%-participation and 90%-engagement occurred within six-hours, demonstrating rapid narrative formation. Findings highlight how online community signals can inform cybersecurity-communication strategies and support more transparent, trust-oriented CI protection efforts.
As teleoperated robots become more common on construction sites, human-robot interfaces must better support operators' sensory needs. This paper presents results from a pilot study comparing a proposed sensory-enhanced teleoperation interface for compact demolition robots with a standard teleoperation interface. Recommendations from demolition stakeholders informed the design of the sensory-enhanced interface, while the standard interface was based on commercially available teleoperated construction equipment. The goal of this study was to refine the sensory-enhanced interface based on user feedback. Ten construction workers tested the interfaces, and we measured their perceived usability, telepresence, and trust in technology.
Weather disruptions endanger transport network operations and accessibility, hindering people's ability to reach opportunities and services. While most research focuses on infrastructure impacts, little has examined how different socioeconomic groups experience such disruptions. This paper introduces a comprehensive method to assess transport resilience to extreme weather and includes a case study that highlights social disparities in accessing healthcare services. The case study reveals that access to essential city services varies by income level and that extreme weather events can exacerbate these inequalities.
The transition from raw as-built data to semantic object representations is a bottleneck in construction digitalization, currently hampered by manual dependencies and the "black-box" nature of existing point cloud segmentation networks. This paper introduces an autonomous self-correcting 3D segmentation agent tailored for construction environments, utilizing 3D Gaussian Splatting (3DGS) as the underlying representation and Vision Language Models (VLMs) as the reasoning core. Unlike static segmentation methods, our agent adopts a dynamic approach by performing active scene roaming. Through a closed-loop feedback mechanism, the VLM analyzes visual data, detects objects, and rigorously assesses the plausibility of segmentation results. The agent iteratively corrects its own errors until a high-fidelity, logically consistent segmentation is achieved. This method demonstrates superior interpretability and accuracy, enabling downstream applications such as as-built verification, progress tracking, and quantity takeoff. By bridging generative rendering with semantic reasoning, our approach provides a robust framework for automated 3D scene understanding.
Fire safety compliance checks in existing buildings are often labor-intensive and prone to error, due to the lack of design data and the reliance on traditional/manual inspection tools. This work proposes an alternative to conventional approaches through a semi-automated Scan-to-BIM methodology to create a BIM model directly from point cloud data. This model is specifically designed to facilitate the regulatory checks required for fire safety, particularly at the level of compartmentation, and can be applied to evacuation routes in later stages. The methodology involves four main phases: i) point cloud acquisition and registration; ii) point cloud processing; iii) geometric features acquisition, and iv) creation of spatial entities compatible with the IFC format. The methodology demonstrates robustness when applied to benchmark datasets. The results confirm the efficiency and accuracy of the methodology in developing early design and BIM models suited to support fire safety engineering and verification of compliance with fire safety regulations.
The adoption of ontologies in the AEC domain is increasing, yet ontology reuse remains constrained because identifying suitable ontologies and concepts is still largely manual. This paper investigates whether shared embedding spaces can capture semantic relatedness across AEC ontologies, thereby supporting ontology discovery and alignment. We test three hypotheses regarding expert-aligned similarity, the spatial proximity of related classes and the superiority of structure-aware over purely lexical models. We embedded several widely used AEC ontologies and evaluated ontology and class-level similarities. Results suggest that embeddings operationalize expert intuition and lower barriers to ontology reuse.
