Accessibility evaluation in built environments increasingly requires performance-based methods that capture dynamic user–environment interaction rather than static compliance with dimensional standards. Existing computational approaches, including BIM-based rule checking and path-finding analysis, are effective for geometric verification but cannot quantify the physical demands imposed on users during task execution. To date, no existing framework combines task-driven motion generation with quantitative biomechanical analysis specifically for accessibility evaluation in built environments. This paper presents a modular simulation framework that integrates procedural kinematic planning with inverse dynamics analysis to enable performance-based accessibility assessment. The system generates deterministic motion trajectories using a gait-based abstraction layer and inverse kinematics, then computes net joint forces and torques via a stabilized Newton–Euler recursive algorithm. External contact forces, including ground reactions during locomotion, are modeled through a dedicated force management subsystem. The framework is implemented in Unity and evaluated in three representative scenarios: level walking, stair ascent, and stair descent. Computed knee joint forces fall within magnitude and temporal ranges reported in the biomechanical literature (peak forces of 2.5–4.0 body weights for stairs and 1.5–2.0 body weights for level walking), indicating biomechanical plausibility rather than subject-specific experimental validation. The framework offers a reproducible, extensible foundation for evidence-based accessibility design, allowing designers to detect and quantify biomechanically demanding interactions in architectural environments prior to construction, and supporting a shift from prescriptive compliance toward dynamic, human-centered evaluation.

Background: Accessibility evaluation of Graphical User Interfaces (GUIs) is a field of ongoing research. Towards an evaluation framework for automatic accessibility assessment, a novel methodology using Virtual User Models (VUMs) is presented. Object

The construction industry stands to greatly benefit from the technological advancements in deep learning and computer vision, which can automate time-consuming tasks such as quality control. In this paper, we introduce a framework that incorporates two advanced tools - the Visual Quality Control (VQC) tool and the Digital Twin visualization with Augmented Reality (DigiTAR) tool - to perform semi-automated visual quality control in the construction site during the execution phase of the project. The VQC tool is a backend service that detects potential defects on images captured on-site using the Mask R-CNN algorithm trained on annotated images of concrete and railway defects. The surveyor, aided by the Augmented Reality (AR) technology through the DigiTAR tool, can in-situ confirm/reject the detected defects and propose remedial actions. All the quality control results are recorded in the relevant BIM model and can be viewed on-site overlaid on the physical construction elements. This solution offers a semi-automated visual inspection that can speed up and simplify the quality control process, especially in case of large linear infrastructures, illustrating the added value of AR-based applications in Digital Twins

The construction industry stands to greatly benefit from the technological advancements in deep learning and computer vision, which can automate time-consuming tasks such as quality control. In this paper, we introduce a framework that incorporates two advanced tools - the Visual Quality Control (VQC) tool and the Digital Twin visualization with Augmented Reality (DigiTAR) tool - to perform semi-automated visual quality control in the construction site during the execution phase of the project. The VQC tool is a backend service that detects potential defects on images captured on-site using the Mask R-CNN algorithm trained on annotated images of concrete and railway defects. The surveyor, aided by the Augmented Reality (AR) technology through the DigiTAR tool, can in-situ confirm/reject the detected defects and propose remedial actions. All the quality control results are recorded in the relevant BIM model and can be viewed on-site overlaid on the physical construction elements. This solution offers a semi-automated visual inspection that can speed up and simplify the quality control process, especially in case of large linear infrastructures, illustrating the added value of AR-based applications in Digital Twins