Search Results (53)

Artificial intelligence (AI) is increasingly acknowledged as a transformative driver in design processes, yet its integration in architectural education often remains limited to image generation and generic productivity tasks. This study explores the feasibility and perceived usefulness of integrating AI into architectural modeling and 3D parametric workflows through an exploratory, observational workshop evaluation. The article reports on an AI-driven parametric architecture workshop held at Smart and Future Cities Laboratory for Sustainable Urban Solutions [SFCL] designed as a research setting aimed at introducing architects to the integration of artificial intelligence within computational design and situating AI as an augmentative, rather than substitutive, design agent. Over five days, participants engaged in a hands-on, design-thinking workflow using Rhino 8 and Grasshopper 1.0 in combination with AI tools including large language models and 3D translation tools to support prompt writing, concept generation, parametric modeling, and high-quality visualization. Data were collected through direct observation of workshop activities and a post-workshop survey assessing participants’ perceptions of usability, relevance, and attitudinal change across design stages, as well as how their views on AI changed from the early design stages to the final visualization. All survey respondents rated the workshop methodology as practical and relevant, and 66.7% reported an improved understanding of AI’s role within parametric workflows, highlighting AI-assisted prompt scripting and visualization as key learning outcomes. The findings indicate that embedding AI tools within architectural education may lower technical barriers and support students’ engagement with contemporary computational design practice; as an exploratory case study with a small convenience sample, these findings are preliminary and not generalized. It introduces a modern methodological framework for AI-driven parametric design. Full article

Building permit approval remains one of the most time-consuming and inconsistent processes in the Architecture, Engineering, and Construction (AEC) industry, requiring designers and municipal reviewers to manually interpret planning regulations and building codes. This paper presents CODE-COMPANION, an AI-assisted computational framework that transforms natural-language regulations into structured, machine-readable rules and evaluates them deterministically within Building Information Modeling (BIM) environments. The framework formalizes selected regulatory requirements as deterministic computable rules and connects these rules to geometric and parametric information derived from the architectural model. The checking workflow was initially prototyped in Rhino 8 SR29 (v8.29.26063.11001) using Grasshopper (build 8.29.26063.11000) and subsequently implemented in Autodesk Revit 2026.4 using Python 3.13.12 and pyRevit 6.1.0 to provide interactive design-time compliance feedback. Validation was conducted using a real-world case study in Kadikoy, Istanbul, where two alternative building designs were assessed under identical parcel-specific regulations. The prototype successfully identified compliance violations, highlighted non-compliant elements within the 3D model, and generated explainable pass/fail results reporting the evaluated rule, measured value, and governing threshold. By providing continuous, real-time regulatory feedback during design, the framework transforms compliance checking from a post-design verification task into a proactive design-support process, enabling architects to resolve issues early, evaluate design alternatives more efficiently, reduce redesign iterations, shorten permitting time, and make more informed decisions. The results demonstrate that a substantial proportion of deterministic permit regulations can be evaluated automatically while discretionary regulations remain under human judgement. Full article

Spontaneous combustion in fire-affected coal seams can degrade coal quality, alter rock mechanical parameters and reduce mining profitability. Traditional pit limit optimization methods ignore coal fire-induced quality degradation, ignore the coupling effect of economic fluctuation and slope stability, and lack quantitative optimization for fire-affected open-pit mines. Here, we optimize loss-reducing mining boundaries for the southern fire-affected highwall in the first mining district of the Dananhu No. 2 Mine, Hami, Xinjiang. The aim is to move beyond binary decisions that either sterilize or fully extract fire-affected reserves. We instead integrate economic return, slope stability and coal-price uncertainty into a single boundary-optimization framework. First, we established a three-dimensional Cartesian coordinate system for the study area. We then modeled and fitted the coal-seam roof and floor using MATLAB-based multiple integration, reducing edge errors in solid surfaces. Laboratory analyses of borehole coal samples defined how calorific value varied with advance distance. These data were used to derive the coal-quality curve. Net mining profit was then formulated as the objective function, replacing the conventional stripping-ratio criterion. Profit was calculated across advance distances to identify the economically optimal boundary. Mechanical parameters of thermally altered rocks were obtained from laboratory deformation tests. Rhino and FLAC3D 6.0 were then used to evaluate three-dimensional slope stability at critical locations. Coal-price perturbation scenarios were finally introduced to test the sensitivity of net profit and optimal advance distance. Under the baseline coal price, the slope remained stable at an advance distance of 193 m. At this boundary, net profit reached a maximum of RMB 676.608 million. The southern surface boundary contracted by 47 m relative to the initial boundary, reducing unnecessary land disturbance. Sensitivity analysis showed that lower coal prices sharply reduced both the optimal advance distance and maximum net profit. When coal price decreased by 30%, the optimal advance distance contracted to approximately 116.9 m. Maximum net profit fell to approximately RMB 248.239 million. Higher coal prices expanded the optimal boundary outward. Once coal price reached approximately 128.7 yuan/t, or 18.5% above baseline, the optimum reached the upper constraint of 240 m. Net profit then increased substantially with further price growth. These results provide a quantitative basis for dynamic boundary optimization and disturbance-reducing extraction in fire-affected open-pit coal mines. Full article

Heritage Building Information Modelling (H-BIM) is increasingly used for the documentation, conservation and management of historical assets. However, the integration of geometric singularities, degradation phenomena and vulnerability-related information within H-BIM environments remains only partially addressed, as current workflows are often manual, fragmented and poorly connected to structured conservation assessment procedures. This paper introduces V-HBIM, a vulnerability-oriented Heritage BIM approach aimed at integrating geometric data, defect mapping and conservation parameters into a single digital model. A semi-automated scan-to-model workflow is proposed, combining point cloud processing, geometric modelling, mesh-based defect representation, BIM conversion, structural vulnerability calculations and automated information enrichment. The workflow uses CloudCompare, Rhinoceros 3D, Rhino.Inside.Revit, Autodesk Revit and Dynamo scripts to connect digital survey outputs with a structured database derived from the Italian Ministry of Culture Risk Map. The method is validated on the Roman Arch of San Damiano, in the Archeological Park of Carsulae, Italy. Results show that defects can be represented as queryable BIM entities and associated with severity, extent and urgency parameters. The proposed approach supports conservation planning, inspection updating and vulnerability-oriented management of cultural heritage assets. Full article

An automated CFD-based workflow for the design, evaluation, and comparative optimization of 2D tidal-stream turbine blade sections is presented for early-stage design exploration. The workflow is intended to efficiently derive an improved section using a consistent and higher fidelity evaluation approach, which is particularly relevant for floating tidal concepts where the effective angle of attack can vary. HEEDS is used to manage a SHERPA optimization loop, while candidate geometries are regenerated in Rhino Grasshopper through a control point parameterization with thickness bounds and smooth interpolation. STAR-CCM+ simulations are executed in an automated manner and the resulting lift and drag responses are returned to HEEDS to evaluate performance over four representative angles of attack, 0, 3, 6, and 9 deg. A total of 1000 design evaluations are conducted for a baseline NACA 63–815 section at Reynolds number 1 × 10⁷, using a two metric formulation that targets high mean lift to drag ratio while limiting the maximum drag coefficient within the same angle set. The optimization history shows rapid early improvement followed by a plateau and identifies a final best design at Design 746. Compared with the original section, the optimized section increases lift and improves the lift-to-drag ratio across the operating range, while keeping the peak drag constrained. Cavitation inception characteristics also improve, with the optimized section delaying inception at the same lift criterion and sustaining a cavitation free state at higher lift for the same cavitation number. Pressure coefficient distributions indicate that these changes are primarily associated with altered suction side loading in the front to mid chord region and modified pressure recovery behavior. A preliminary full 3D RANS CFD rotor comparison under a prescribed rotor geometry further shows that the optimized section can improve rotor power performance in the main operating TSR range, although the benefit becomes limited at high TSR. Full article

Multimodal large language models can read architectural images and design instructions but they still struggle to turn architectural rules into editable, executable models in professional modeling environments. To address this limitation, this paper presents MLLMto3D, an MCP-driven closed-loop framework that connects multimodal reasoning with Rhino-based modeling, feedback, and revision. The framework consists of five phases: visual parsing, JSON-based intent serialization, code synthesis, MCP-driven Rhino execution and feedback, and verification with bounded repair. Its core mechanism is JSON-based intent serialization, which converts image-derived architectural information into machine-readable modeling parameters under a predefined JSON schema. The schema separates geometric and compositional constraints, including height, bay rhythm, facade zones, and alignment rules, from design variables such as materials, openings, and ornament. Building on this mechanism, Skills modules externalize facade typology knowledge and safe Rhino scripting patterns, providing callable professional constraints for code synthesis to reduce design-intent deviation and API hallucination. The framework is evaluated through an experimental design case study on a site in Shanghai’s Hengfu Historic District, where the generation of new façades is informed by a nearby heritage architectural reference. The results show that MLLMto3D can generate a parametrically adjustable Rhino model while preserving the main compositional constraints, thereby advancing AI-assisted architectural 3D generation toward a controllable, verifiable, and iterative modeling process. Full article

The accelerating deterioration of Chinese historical villages necessitates advanced digital approaches for systematic documentation and conservation. The present research proposes a novel Digital Heritage Framework that integrates UAV-based 3D oblique photogrammetry, LiDAR point cloud modeling, and computer vision. Unlike single-technology approaches, our methodology solves modeling issues for complex terrain mapping. This especially applies to the interior and roof works of buildings. The framework implements a customized Rhino-Grasshopper. The 3D model is able to resolve issues of shadow occlusion and spatial discontinuity by integrating aerial and ground-based datasets into spatially coherent formats. This makes use of the Meta-AI-SAM2 deep learning model for semantic segmentation and identification of materials. The computer vision (CV) approach gives semi-automated façade analysis. It enables documentation of complex architectural features non-invasively. We developed a Unity-based visualization platform. It features multiscale representations, ranging from village-scale layouts to centimeter-accurate scans of heritage structures such as the Qinchuan Ancestral Hall. Integration with the Unity platform optimizes dataset organization and hierarchical structuring. This significantly enhances database operational efficiency. This integration reduces manual processing complexity and hardware demands. Demonstrating documented efficiency and precision, this workflow presents a scalable solution for endangered heritage sites. Future research will explore AI-assisted detail reconstruction and cross-cultural adaptations. It potentially establishes this framework as a comprehensive tool for sustainable digital conservation. Full article

To investigate the slope stability of the north bank anchorage of the Yellow River Three Gorges Bridge during foundation pit excavation and operational stages, a true three-dimensional geological model was established using Rhino6 and numerical simulations were performed using FLAC3D7.0, supplemented by stereographic projection kinematic analysis and the shear strength reduction (SSR) method. Systematic simulations were conducted for foundation pit excavation, main cable load application, heavy rainfall, and two seismic loading conditions, and the deformation characteristics and plastic zone evolution patterns of the slope under different conditions were analyzed. The stereographic projection kinematic analysis indicates that the dominant discontinuity sets do not constitute kinematically admissible planar sliding, wedge sliding, or toppling failure modes, confirming the validity of adopting a continuum model. The numerical simulation results show that the maximum slope displacement after foundation pit excavation is 13.13 mm, with the plastic zone exhibiting a discontinuous scattered distribution, and the slope is overall stable. After the application of the main cable load, the maximum displacement decreases to 7.86 mm; the counterweight effect of the anchorage self-weight significantly improves the deep stability, while the horizontal cable force generates a wedge-shaped shear plastic zone at the slope toe. Under heavy rainfall conditions, rock mass saturation leads to an increase in the maximum displacement to 11.76 mm with expanded plastic zone volume, where the deterioration of strength parameters and the increase in pore water pressure are the primary causes of reduced stability. Under seismic conditions, the maximum displacements under the natural and artificial seismic waves are 15.83 mm and 17.29 mm, respectively, exhibiting a significant elevation amplification effect with extensive plastic zone development in the shallow surface layer. The shear strength reduction analysis yields factors of safety of 2.4 and 2.27 for the heavy rainfall and seismic conditions, respectively, both significantly exceeding the code requirements, demonstrating that the slope possesses an adequate safety margin under extreme loading conditions. Full article

Integrating direct air carbon capture (DAC) into buildings offers a promising pathway for reducing atmospheric CO₂, yet the role of architectural design in enhancing passive carbon-capture performance remains underexplored. This study presents a computational framework developed to optimize architectural design and enclosure geometry for enhanced passive airflow, using mass-flow rate as a proxy for the comparative assessment of carbon absorption potential. Implemented within Rhino3D and Grasshopper using Ladybug and Eddy3D, the workflow integrates weather data and CFD simulation to compute segmented mass-flow rates through stacked capture trays. The framework simplifies traditionally complex CFD processes by introducing a custom segmented mass-flow calculation approach that enables comparative performance assessment during early-stage design. Results confirm the validity of the proposed workflow, revealing that façade rotation can modify total mass flow by up to 96.5%; seasonal wind variability can cause airflow to range from approximately 8.5 kg/s in January to 169.5 kg/s in May in Seattle. Spatial configuration can alter airflow by up to an order of magnitude and introduce substantial spatial heterogeneity within capture zones. This research establishes a performance-driven design framework that enables architectural geometry to actively enhance passive carbon-capture integration, positioning building design as a measurable contributor to climate mitigation strategies. Ultimately, this work bridges architectural design and carbon-capture engineering, supporting interdisciplinary approaches to scalable, climate-responsive building systems. Full article

Solar energy plays a fundamental role in achieving decarbonization in the construction sector, and therefore, a detailed assessment of solar potential at the urban scale is a key tool in supporting this process. Within this framework, the present study focuses on the high-resolution evaluation of photovoltaic (PV) potential in urban environments, specifically targeting the city of Milan, Italy, where two representative study areas are selected. In detail, 3D city models are developed using Rhino3D 7 software, and a solar radiation analysis was performed using Ladybug components. The solar radiation received by the surfaces that comprise the roofs and facades of buildings is estimated for each floor and orientation, taking into account local climate conditions and shadows cast by surrounding buildings. To define the economic viability of PV system deployment, two threshold criteria were introduced: one concerning the size (area) of the PV system and the other the minimum annual solar radiation level that each surface receives. Based on the obtained data, it is found that approximately 28% of roof surfaces and 5% of facades meet these cost-effective thresholds for PV integration. Further analysis indicates that the balcony self-shading can be considered negligible in the high-density urban context analyzed. The results are beneficial for urban energy management, considering energy savings and investment approaches, and the possibility to transform existing buildings into zero-carbon buildings powered by renewables. Full article

Agrivoltaics, an emerging approach that integrates solar energy generation with agricultural production, offers an effective solution to land-use conflicts by enabling the simultaneous production of clean energy and crops. However, the shading effect of photovoltaic (PV) modules significantly alters both the quantity and distribution of light within crop canopies, creating challenges in balancing power output with crop light requirements. This study employs the Rhino–Grasshopper parametric modeling platform, combined with Ladybug and PVsyst, to conduct batch simulations of 44 configuration schemes for an agrivoltaic system in Lianyungang, Jiangsu Province. Annual simulations of the light environment and energy generation were performed, and model accuracy was validated through field measurements using Daily Light Integral (DLI), light uniformity (coefficient of variation, CV), and annual energy yield as key indicators to assess the effects of different module layouts and tilt angles. The results reveal pronounced seasonal variations in the system’s light environment. The tilt angle exhibits a seasonal reversal pattern: higher tilt angles in winter and spring substantially reduce DLI (up to a 44% decrease under high ground coverage ratio, GCR, conditions), whereas moderate tilt angles in summer and autumn enhance light transmission, with low-GCR layouts enabling DLI values exceeding 30.6 mol·m⁻²·d⁻¹. Light uniformity was highest in the dual-row layout with 0.2 m spacing, maintaining a CV between 0.16 and 0.18. Energy yield peaked at a 30 tilt angle, showing a parabolic response pattern. Overall, this study proposes a photovoltaic module layout design method based on seasonal light distribution characteristics and the balance between agricultural and energy production goals. This method provides a scientific basis for structural layout planning and planting-model design in agrivoltaic systems and contributes to improving light-energy utilization efficiency and agricultural output, thereby achieving synergistic benefits between photovoltaic power generation and crop production. Full article

The powder-laying arm of a metal 3D printer is heavy, which can easily cause long-term damage to the powder-laying servomotor or belt, so it is necessary to design a lightweight powder-laying arm. To this end, we first use 3D modeling Rhino software to rebuild the powder-laying arm, and then, we carry out topology optimization design on the rebuilt powder-laying arm in Altair Inspire software. Finally, we use the Aurora Elva 3D printer to complete manufacturing and assembly to verify compatibility. The results show that the maximum displacement of the original powder-spreading arm is concentrated in the lower right corner at 4.319 × 10⁻⁵ mm; the maximum stress is concentrated in the middle transition part, decreasing toward the ends; the maximum stress is 3.843 × 10⁻² MPa; the stress concentration and deformation of the powder-spreading arm when spreading powder is small, which provides a large optimization space. The topology-optimized powder-spreading arm, with a 25% quality objective, maintains the integrity of the connection with the fixing hole while having a large mass reduction. The surface of the parts of the completed 3D-printed powder arm is bright, with low roughness, and there is no obvious warping and deformation or other defects; the completed 3D-printed powder-spreading arm and the assembly of the wall are closely coordinated with each other, and the location of the screw holes is appropriate, having no obvious assembly conflicts between the parts, which lays the foundation for the mass production of the powder-spreading arm of high-performance metal 3D printers. Full article

The traditional construction industry significantly contributes to global resource consumption and climate change. Conventional methods limit the development of complex and multifunctional architectural forms. In contrast, 3D concrete printing (3DCP), an additive manufacturing technique, enables the creation of intricate building envelopes that integrate architectural and energy-efficient functions. Bioinspired design, recognized for its sustainability, has gained traction in this context. This study investigates the thermal and energy performance of various bioinspired and regular 3DCP infill patterns compared to conventional concrete building envelopes in tropical climates. A three-stage methodology was employed. First, bioinspired patterns were identified and evaluated through a literature review. Next, prototype models were developed using Rhino and simulated in ANSYS to assess thermal performance. Finally, energy performance was analyzed using Ladybug and Honeybee tools. The results revealed that honeycomb, spiral, spiderweb, and weaving patterns achieved 35–40% higher thermal and energy efficiency than solid concrete, and about 10% more than the 3DCP sawtooth pattern. The findings highlight the potential of bioinspired spiral infill patterns to enhance the sustainability of 3DCP building envelopes. This opens new avenues for integrating biomimicry into 3DCP construction as a tool for performance optimization and environmental impact reduction. Full article

Enhancing urban air quality and thermal comfort involves addressing multifaceted environmental and design challenges. Investigating the effects of urban morphological and building geometrical parameters on enhancing air quality and thermal comfort is a multifaceted problem, influenced by different parameters. This study aims to develop optimized design solutions for university buildings and courtyards to enhance outdoor thermal comfort and reduce CO₂ concentration levels as an indicator of air quality. Consequently, the methodology involved a combination of field monitoring at two university faculties in Egypt and a computational parametric methodology using Rhino 3D+Grasshopper(V8) for enhancing thermal comfort, reducing CO₂ concentration levels, and improving wind velocity. The in situ measurements revealed significantly high CO₂ levels (780 ppm) and wind speed (3.8 m/s). The parametric methodology’s findings revealed a substantial reduction in the Universal Thermal Climate Index (UTCI) by 2.04 to 10.3 °C, a decrease in CO₂ concentration by 57 to 197 ppm, and an increase in wind speed by 0.4 to 4.07 m/s. The most suitable vegetation ratio for trees within narrow courtyard designs was found to be 30%. This ratio effectively enhances thermal comfort (UTCI) and reduces CO₂ concentrations, while also maintaining adequate airflow and avoiding excessive obstruction of natural ventilation within the courtyard. These findings provide valuable guidance for optimizing courtyard designs in hot arid climates. Full article

Neuroscience education must convey 3D structure with clarity and accuracy. Traditional 2D renderings are limited as they lose depth information and hinder spatial understanding. High-resolution resources now exist, yet many are difficult to use in the class. Therefore, we developed an educational brain video that moves from gross to microanatomy using MRI-based models and the published literature. The pipeline used Fiji for preprocessing, MeshLab for mesh cleanup, Rhino 6 for target fixes, Houdini FX for materials, lighting, and renders, and Cinema4D for final refinement of the video. We had our brain models validated by two neuroscientists for educational fidelity. We tested the video in a class with 96 undergraduates randomized to video and lecture or lecture only. Students completed the same pretest and posttest questions. Student feedback revealed that comprehension and motivation to learn increased significantly in the group that watched the video, suggesting its potential as a useful supplement to traditional lectures. A short, well-produced 3D video can supplement lectures and improve learning in this setting. We share software versions and key parameters to support reuse. Full article