Architectural Technologies Studies

Abstract Aims: The rapid advancement of computational technologies and artificial intelligence has fundamentally transformed conventional paradigms for documenting, analyzing, designing, and conserving heritage landscapes. As the volume and complexity of spatial data continue to grow, traditional intuition-based decision-making is gradually being replaced by evidence-based, data-driven approaches. Consequently, the concept of Future Heritage Landscapes has emerged as a new research paradigm in which heritage conservation extends beyond preserving existing physical characteristics toward predicting future conditions, supporting adaptive management, and enabling intelligent planning through computational systems.
Heritage landscapes represent complex socio-ecological systems integrating tangible and intangible values, spatial configurations, ecological processes, and accumulated indigenous knowledge. Historic Persian gardens, recognized as one of the most significant cultural landscape typologies worldwide, embody centuries of environmental wisdom, hydraulic engineering, climatic adaptation, and symbolic spatial organization. However, conventional documentation techniques often fail to capture the multidimensional relationships embedded within these landscapes or to support comprehensive decision-making processes.
Literature Background: Recent developments in UAV photogrammetry, computer vision, machine learning, and Decision Support Systems (DSSs) have introduced new opportunities for collecting high-resolution spatial data and transforming them into actionable knowledge. Rather than serving merely as computational tools for data processing, intelligent DSSs provide integrated frameworks capable of organizing heterogeneous datasets, extracting hidden knowledge, simulating alternative scenarios, and supporting evidence-based decisions. Computational technologies have thus become an essential foundation for developing adaptive and resilient future heritage landscapes. This study investigates the role of computational Decision Support Systems in facilitating the transition from intuition-based to data-driven decision-making within landscape architecture and cultural heritage management. Specifically, the research aims to explain how advanced data acquisition technologies—particularly UAV-based photogrammetry—can provide the spatial information required for intelligent decision-making processes. Furthermore, the study proposes a conceptual framework illustrating the transformation of raw data into information, information into knowledge, and knowledge into informed decisions through computational systems, with particular attention to integrating artificial intelligence into each stage.
Materials & Methods: The research adopts a descriptive–analytical and developmental methodology. Initially, an extensive literature review was conducted concerning computational design, AI, data mining, spatial analysis, and DSSs. Subsequently, various landscape data acquisition methods were comparatively evaluated, and UAV photogrammetry was identified as the most appropriate technique for documenting historic Persian gardens due to its ability to generate dense, accurate, and high-resolution spatial datasets while maintaining relatively low operational costs. Image datasets acquired by UAVs were processed using Structure from Motion and Multi-View Stereo algorithms to produce dense point clouds, orthophotos, digital elevation models, textured meshes, and complete three-dimensional representations of heritage landscapes. The resulting datasets were subsequently integrated into a Decision Support System specifically developed for heritage landscape analysis, comprising multiple computational layers including data management, spatial analysis, information processing, knowledge extraction, and intelligent decision support.
Findings: The findings demonstrate that UAV photogrammetry provides highly accurate, multi-dimensional, and information-rich datasets that substantially enhance the documentation and analysis of heritage landscapes. Beyond geometric reconstruction, the generated datasets facilitate the extraction of valuable information concerning vegetation distribution, irrigation networks, topography, spatial hierarchy, architectural structures, and environmental characteristics. The study further reveals that the true value of spatial data emerges only after systematic integration within an intelligent Decision Support System. Through the integration of spatial, historical, ecological, and visual information, the developed DSS transforms heterogeneous datasets into meaningful information and subsequently into actionable knowledge. AI algorithms identify hidden spatial relationships, recognize recurring landscape patterns, classify environmental conditions, detect anomalies, monitor temporal changes, and support predictive analyses that would be difficult to accomplish using conventional analytical approaches.
Conclusion: The proposed framework demonstrates that decision-making in heritage landscape management can be conceptualized as a hierarchical computational process composed of four successive stages: Data → Information → Knowledge → Decision. Unlike traditional conservation practices that often rely primarily on expert intuition and qualitative assessments, AI-driven Decision Support Systems provide transparent, reproducible, and evidence-based recommendations supported by quantitative analyses. Moreover, the integration of historical documentation, remote sensing, three-dimensional models, and environmental monitoring enables continuous evaluation of landscape conditions and facilitates dynamic management strategies capable of responding to future changes. From a broader theoretical perspective, the research suggests that computational systems redefine heritage conservation by shifting emphasis from static documentation toward continuous knowledge generation, significantly enhancing the capacity of landscape architects and heritage managers to understand complex interactions between cultural values, ecological processes, and spatial organization. This study demonstrates that computational Decision Support Systems constitute a transformative framework for the future of heritage landscape conservation and management. Rather than functioning solely as analytical software, AI-enabled DSSs integrate diverse datasets, extract meaningful knowledge, simulate future scenarios, and support evidence-based decision-making throughout the entire heritage management process. UAV photogrammetry emerges as one of the most efficient methods for generating accurate, comprehensive, and analytically valuable three-dimensional datasets of historic landscapes. When combined with AI, computer vision, and data mining algorithms, these datasets become the foundation for intelligent decision-making frameworks capable of supporting conservation, restoration, planning, and adaptive management. The proposed computational workflow provides a comprehensive paradigm for developing Future Heritage Landscapes by preserving, transferring, and regenerating both explicit and tacit ecological knowledge embedded within historic Persian gardens. Ultimately, this research argues that the future of cultural landscape conservation lies in establishing intelligent computational ecosystems capable of continuously learning from heritage data, supporting multidisciplinary collaboration, and facilitating informed decision-making. The proposed framework offers a transferable model for integrating AI, computational design, spatial data science, and landscape architecture into heritage conservation practices.

Abstract Aims: The Trombe wall stands as one of the most recognized passive solar heating systems, valued for its simplicity, low operational cost, and ability to harness solar energy for space heating. However, it faces notable limitations, including reduced thermal efficiency during nighttime hours, the risk of overheating in warm seasons, and its inherently single-function nature. These drawbacks have prompted growing interest in hybrid Trombe wall configurations and multi-layered building envelopes as effective strategies to enhance overall performance. This study aims to provide a comprehensive and critical review of hybrid Trombe wall systems, systematically evaluating their capacity to improve thermal performance, electrical generation, environmental benefits, and indoor air quality (IAQ). By synthesizing recent advancements, the research seeks to clarify how integrating complementary technologies can transform the conventional Trombe wall into a multifunctional building skin suitable for low-energy and nearly zero-energy buildings. Particular attention is given to applicability in diverse climates, with a specific focus on Iran’s varied climatic zones. The overarching objective is to identify optimal hybrid approaches, highlight performance trade-offs, and propose a decision-making framework that assists architects, engineers, and policymakers in selecting appropriate systems tailored to specific environmental and architectural contexts. Ultimately, this review contributes to the broader discourse on sustainable building envelopes by bridging knowledge gaps between passive solar design and modern multi-functional technologies.
Materials & Methods: This research adopted a systematic literature review methodology combined with critical analysis. A comprehensive search was conducted across leading scientific databases to identify relevant peer-reviewed articles, conference papers, and technical reports. More than 120 high-quality publications, predominantly published between 2018 and 2026, were selected based on predefined inclusion and exclusion criteria emphasizing experimental, numerical, and review studies on hybrid Trombe wall systems. Keywords related to Trombe wall, passive solar, hybrid systems, photovoltaic-Trombe, phase change materials, and indoor air quality were utilized. Each selected paper underwent thorough screening for relevance, methodological rigor, and reported performance metrics. Data extraction focused on key performance indicators such as thermal efficiency, heating load reduction, electricity generation (where applicable), IAQ improvements, and environmental impacts. A comparative analysis framework was developed to classify hybrid approaches and evaluate them against conventional Trombe walls. Qualitative and quantitative synthesis methods were employed, including tabular comparisons, performance benchmarking, and climate-responsive matching. Special emphasis was placed on studies conducted under real climatic conditions or using validated simulation tools. To support practical application in Iran, a technology-climate matching matrix and a multi-criteria decision-making (MCDM) framework were constructed, incorporating factors such as climatic parameters, energy performance, cost, architectural integration, and environmental benefits. Critical appraisal of limitations in existing literature, including scale of experiments and long-term monitoring, was also performed.
Findings: The review identified nine primary categories of hybrid Trombe wall systems: integration with photovoltaic (PV) cells, phase change materials (PCM), water-based systems, nanofluids, catalytic and air-purification materials, earth-to-air heat exchangers (EAHE), natural composite materials, porous materials, and rotary desiccant dehumidifiers. Comparative analysis revealed distinct strengths for each configuration. PV-Trombe systems demonstrated superior performance in simultaneous thermal and electrical energy production, enhancing overall energy yield while mitigating overheating through electricity generation. PCM-integrated designs significantly improved thermal stability and extended the duration of useful heat delivery, particularly during evening hours. Catalytic and air-purification technologies showed notable potential in enhancing indoor air quality and inactivating airborne pathogens. Water-based and EAHE hybrid systems performed effectively in specific climatic conditions, offering improved heat transfer and passive cooling capabilities. Quantitative outcomes indicated heating load reductions of up to 72% in optimized hybrid configurations compared to conventional setups. Substantial improvements in indoor air quality were also reported across multiple studies, attributed to enhanced ventilation and pollutant filtration in advanced hybrids. The developed technology-climate matching matrix illustrated that different hybrid solutions are optimal for Iran’s diverse climates — from cold mountainous regions to hot arid zones. The multi-criteria decision framework further facilitated systematic selection by balancing energy, environmental, economic, and architectural considerations. However, variations in performance across climates underscored the necessity of context-specific design. While many studies relied on numerical simulations and small-scale experiments, field data remained relatively limited.
Conclusion: Hybrid Trombe wall systems exhibit considerable potential to function as advanced multi-functional building envelopes, playing a pivotal role in the transition toward low-energy and nearly zero-energy buildings. By addressing the inherent limitations of traditional Trombe walls through strategic integration of complementary technologies, these systems can deliver enhanced thermal comfort, renewable energy generation, improved indoor environmental quality, and reduced environmental impact. The classification into nine main hybrid categories, supported by comparative performance analysis, provides a clear roadmap for researchers and practitioners. Nevertheless, several challenges persist, including high initial costs, architectural integration complexities, climate-dependent performance variations, and insufficient long-term field studies. Overcoming these barriers requires targeted efforts in several directions: conducting extensive experimental and long-term monitoring studies, developing locally sourced novel materials, incorporating smart control technologies, and adapting designs to regional needs and construction practices. In summary, the strategic hybridization of Trombe walls represents a promising pathway for sustainable architecture. The proposed technology-climate matching matrix and multi-criteria decision framework offer practical tools for implementation, particularly in countries like Iran with abundant solar resources and diverse climates. Future research should prioritize real-scale demonstrations, lifecycle assessments, and economic viability analyses to accelerate the mainstream adoption of these innovative systems. Ultimately, well-designed hybrid Trombe walls can significantly contribute to energy-efficient, healthy, and environmentally responsive buildings of the future.

Abstract Aims: The increasing use of freeform geometries in contemporary architecture has transformed the conversion of continuous curved surfaces into manufacturable panels into a major challenge of digital design and fabrication. Although numerous quadrilateral panelization techniques have been proposed, selecting an appropriate method remains difficult because each algorithm balances geometric accuracy, visual smoothness, and fabrication considerations differently. Existing studies have primarily focused on developing new algorithms or improving individual characteristics, while little attention has been devoted to systematic frameworks supporting multi-criteria decision-making. This study aims to develop a multi-criteria decision-making framework for evaluating and selecting quadrilateral panelization methods according to complementary geometric accuracy metrics. The novelty lies in integrating three quantitative indicators—Hausdorff Distance (HD), Root Mean Square Error (RMSE), and Kink Angle—with comparative analysis of spatial error distribution patterns, enabling objective assessment according to project-specific priorities rather than assuming a universally optimal method.
Materials and Methods: The research adopted a computational experimental methodology based on a controlled case-study model. A NURBS reference surface with compound curvature was created in Rhinoceros 3D, incorporating regions with positive, negative, and zero Gaussian curvature. Four representative quadrilateral panelization approaches were implemented in Grasshopper: Brick Pattern, Regular Grid, Skew Quadrilateral, and Adaptive Remeshing. All algorithms were evaluated under identical conditions: approximately 950 panels, identical geometric tolerances, and uniform computational settings. Two thousand uniformly distributed random sample points were generated for geometric evaluation. Hausdorff Distance measured maximum local geometric deviation, RMSE evaluated overall average deviation, and Kink Angle quantified angular discontinuity along panel boundaries. This simultaneous application enabled comprehensive evaluation of both dimensional accuracy and geometric continuity.
Findings: The investigated panelization methods exhibit significantly different geometric behaviors, with no single algorithm consistently outperforming others across all criteria. Regular Grid achieved highest geometric accuracy with maximum Hausdorff Distances of 4.73 cm and 4.15 cm and lowest RMSE of 1.23 cm, indicating superior dimensional fidelity. Adaptive Remeshing provided best visual continuity with mean Kink Angle of 3.27° and maximum of 16.36°, indicating smoother transitions between panels. Brick Pattern exhibited weakest performance with maximum Hausdorff Distances of 27.51 cm and 18.24 cm and RMSE of 5.39 cm, revealing systematic one-sided deviation patterns suggesting cumulative geometric errors. Skew Quadrilateral demonstrated intermediate performance without clear advantages. Comparative analysis revealed distinct spatial error patterns: some methods concentrate deviations locally while others distribute errors uniformly. These findings confirm an inherent trade-off between dimensional accuracy and visual smoothness.
Conclusion: The proposed framework demonstrates that evaluating quadrilateral panelization methods requires simultaneous consideration of multiple complementary geometric criteria rather than reliance on a single metric. The concept of the “best” panelization method is context-dependent and should be interpreted according to project priorities. The framework supports informed decision-making by revealing strengths and limitations of different strategies under identical experimental conditions. Beyond comparative evaluation, it provides a transferable methodology applicable to other freeform geometries and alternative algorithms, enabling designers to compare solutions objectively and select appropriate strategies according to specific design objectives and geometric constraints, thereby bridging quantitative geometric evaluation and practical design decision-making.

Abstract Aims: Recent advances in generative artificial intelligence have created new opportunities for architectural visualization and conceptual design. Among these technologies, text‑to‑image models such as Midjourney are increasingly used to generate architectural images from textual descriptions. Despite their expanding application in architectural education, research, and professional practice, limited attention has been given to the linguistic mechanisms through which these systems interpret and represent culturally specific architectural identities. Iranian architecture, characterized by rich spatial traditions, symbolic forms, climate responsiveness, and distinctive aesthetic principles, provides a valuable context for examining how artificial intelligence translates architectural concepts into visual representations. This study aims to analyze the linguistic structure of prompts used to generate images of Iranian architecture in text‑to‑image models, with particular emphasis on Midjourney. The research seeks to identify the linguistic logic underlying the representation of Iranian architecture in these systems and to clarify the role of prompt engineering in either preserving or diminishing the architectural identity of Iran within AI‑generated visual outputs.
Methods: This study was conducted using a qualitative–analytical approach with an applied orientation. Data collection was carried out through Midjourney’s Describe feature, which generates textual descriptions for uploaded images and reveals how the model interprets architectural visual content. To construct the research dataset, four selected images representing characteristic examples of Iranian architecture were chosen. These images depicted recognizable architectural features commonly associated with Iranian architectural heritage, including traditional spatial configurations, arches, ornamental details, and culturally significant design elements. For each image, four textual descriptions were generated using the Describe function, resulting in a total of sixteen textual prompts that constituted the primary dataset of the study.
The collected prompts were examined using qualitative content analysis. Keywords, recurring expressions, descriptive patterns, and conceptual references were identified and systematically coded. Through iterative comparison and categorization, the extracted concepts were organized into broader thematic groups. The analysis focused on identifying the internal structure of the generated descriptions and the linguistic components that repeatedly appeared across different prompts. Based on these findings, an analytical framework based on linguistic layers was developed to explain how architectural meaning is constructed through prompt language and how these linguistic structures influence the visual outputs generated by the AI system.
Findings: The findings indicate that prompts related to Iranian architecture generally follow a relatively consistent linguistic and descriptive pattern. Although wording varied across individual prompts, the generated descriptions repeatedly emphasized a set of architectural, environmental, and visual characteristics that function as identity markers of Iranian architecture. The extracted components were classified into seven principal layers: spatial concept, spatial organization, architectural elements, materials, landscape elements, lighting and environmental conditions, and viewpoint or image‑rendering style.
The spatial concept layer describes the overall atmosphere, typology, and identity of the architectural environment. The spatial organization layer refers to the arrangement of spaces, circulation patterns, and hierarchical relationships among architectural components. Architectural elements include features such as arches, domes, iwans, columns, and ornamental surfaces. The materials layer highlights commonly referenced materials and textures, including brickwork, tile decoration, and plaster ornamentation. Landscape elements encompass gardens, vegetation, courtyards, and water features that contribute to the spatial composition of Iranian architectural environments. The lighting and environmental conditions layer addresses sunlight, shadow, climate‑related qualities, and atmospheric effects. Finally, the viewpoint or image‑rendering style layer relates to camera angle, perspective, framing techniques, and digital visualization characteristics.
The analysis also demonstrated that Midjourney relies heavily on recognizable architectural markers when representing Iranian architecture. Features such as repetitive arches, geometric ornamentation, reflective water pools, and traditional garden elements frequently appeared across the generated descriptions. This pattern suggests that the model tends to construct visual interpretations primarily through widely recognizable symbolic features rather than through deeper cultural, social, and spatial complexities embedded within the architectural tradition.
Conclusion: The study demonstrates that the linguistic structure of prompts plays a decisive role in shaping AI‑generated representations of Iranian architecture. The formulation and structuring of prompts directly influence which architectural characteristics, spatial logics, environmental qualities, and cultural references are emphasized in the resulting images. Consequently, prompt engineering functions as a critical factor in determining whether AI‑generated outputs preserve meaningful aspects of Iranian architectural identity or reduce them to simplified visual stereotypes. A deeper understanding of the linguistic logic embedded within these systems can assist architects, researchers, and designers in developing more informed and culturally sensitive approaches to AI‑assisted design. Furthermore, awareness of the layered linguistic structure of prompts may contribute to more accurate, context‑aware, and identity‑conscious representations of Iranian architecture within emerging generative design environments.

Abstract Aims: Accurate cost estimation is one of the fundamental prerequisites for successful planning and management of construction projects. The emergence of generative artificial intelligence (AI) models and online cost estimation systems has heightened the need to evaluate the performance of these tools in comparison with human expert judgment. Despite the proliferation of these technologies, no comprehensive study has simultaneously compared human experts, online systems, and generative AI models based on actual project costs. Existing research has largely focused on individual approaches in isolation, failing to provide an integrated comparative framework to guide practitioners. Moreover, the repeatability of AI-generated responses across multiple iterations with a fixed prompt has received limited attention, and the attitudes and trust levels of practicing engineers toward these novel tools remain underexplored. This research aims to address these gaps by systematically comparing the three aforementioned approaches in the context of the "Atashgah" building project in Semnan, Iran. Specifically, the study pursues four main objectives: (1) to evaluate differences in mean cost estimates among human experts, online systems, and generative AI models; (2) to assess the accuracy of each approach using MAE and MAPE relative to actual project cost, and measure their stability through coefficient of variation (CV); (3) to examine the repeatability of five AI models across 10 iterations with a fixed prompt; and (4) to investigate engineers' attitudes toward these tools and determine their level of trust in the outputs of online systems and generative AI.
Materials & Methods: This applied descriptive–analytical study adopted a survey-based comparative case study design. The case project was the Atashgah building, a six-story mixed-use (residential–commercial) structure located in Semnan, Iran, with a total floor area of 2,622 m². The building consists of a reinforced concrete frame with block-joist slabs and includes first-grade Iranian construction materials, a brick-and-stone façade, complete mechanical and electrical installations, a six-person elevator, and double-glazed windows. The actual construction cost (81.5 billion IRR), equivalent to 31.08 million IRR/m², served as the reference value for evaluating estimation accuracy. Data were collected through three parallel sources. First, a questionnaire was completed by 29 construction professionals representing different educational levels (65.51% bachelor's, 24.14% master's, 6.90% diploma, and 3.45% doctoral degrees), work experience (17.24% <5 years, 31.03% 5–10 years, 27.59% 11–20 years, and 24.14% >20 years), and professional roles (contractors, supervisors, site managers, and project managers). Participants estimated the project cost per square meter and reported their confidence in their own estimates (1–10), trust in online estimation systems (1–10), trust in AI-based estimations (1–10), trust in the official engineering tariff system (1–10), previous experience using online platforms and AI, and reasons for distrust through open-ended questions. The questionnaire demonstrated satisfactory validity (CVR = 0.82, CVI = 0.86) and reliability (Cronbach's α = 0.84). Second, eight active Iranian online cost estimation platforms (namaplan.com, dezharco.com, bkesh.ir, ahan100.com, karkoshte.com, pardissazeh.com, peymanu.com, and khanehtarh.ir) were evaluated. The same project specifications were entered into each platform according to its input requirements, and the estimated cost per square meter and total project cost were recorded. Because peymanu.com and khanehtarh.ir generated estimates based on a standard area of 100 m² using 2024 prices, their outputs were scaled to the project's actual area (2,622 m²) and updated to 2025 values using the official annual construction inflation rate of 47%. Third, five widely used generative AI models—ChatGPT, Gemini, Claude, DeepSeek, and Grok—were evaluated. These models were selected to represent different AI architectures (proprietary and open-source), geographic origins (U.S. and Chinese models), accessibility, and frequency of use reported by the surveyed engineers. All models received an identical prompt describing the complete project specifications, including building type, location, structural system, floor area, material quality, façade, elevator, building services, and window specifications. Each model generated 10 independent estimates using a new conversation for every iteration, resulting in 50 AI-generated responses. Web search functionality was disabled, and all evaluations were conducted using free-tier accounts. Estimation performance was assessed using the mean, standard deviation (SD), coefficient of variation (CV = SD/Mean × 100), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) relative to the actual project cost. Differences among estimation methods were examined using the Kruskal–Wallis test followed by Mann–Whitney U tests with Bonferroni correction. Engineers' confidence and trust ratings were summarized using descriptive statistics, while qualitative content analysis of the open-ended responses was conducted to evaluate the extent to which AI models considered local construction conditions, material quality, and market-related risks.
Findings: The results revealed that none of the three approaches individually possessed sufficient reliability for standalone application in real projects. Human expert estimates showed a mean of 30.83 million IRR/m², remarkably close to the actual cost of 31.08 million IRR/m² (MAPE = 0.80%), but exhibited considerable dispersion (CV = 34.64%), with estimates ranging from 10 to 65 million IRR/m² and a median of 30 million IRR/m². Despite this variability, engineers reported relatively high confidence in their own estimates (mean = 7.04 out of 10). Online systems exhibited the lowest reliability, with estimates ranging from 25 to 53.49 million IRR/m²—a 157% difference between the lowest and highest estimates. The mean estimate was 37.40 million IRR/m², with a standard deviation of 9.09 million and CV = 24.29%. This represented a 20.34% error relative to actual project cost, indicating a systematic tendency toward overestimation. Among the AI models, Grok demonstrated the best performance, with a mean estimate of 31.80 million IRR/m² (MAPE = 2.32%, CV = 5.32%), and a narrow range of 71–99 billion IRR total cost. Gemini ranked second with MAPE = 12.48% and CV = 19.4%. ChatGPT and DeepSeek showed moderate performance with MAPEs of 21.66% and 22.72%, and CVs of 14.09% and 34.14%, respectively. Claude performed poorly, with MAPE = 63.77%, CV = 71.23%, and high instability across iterations (range: 9.5–75 billion IRR); it also refused to provide numerical estimates in 3 out of 10 iterations. Engineers expressed low trust in non-human approaches, with mean trust scores of 4.89 for the official tariff system, 4.68 for online sites, and 4.67 for AI—all below 5 out of 10. Only 35.71% had used AI before, and merely 20.69% had used online sites. The Kruskal–Wallis test confirmed significant differences among the three approaches (H = 24.6, df = 2, p < 0.001). However, post-hoc Mann–Whitney tests with Bonferroni correction revealed that only the difference between online systems and AI models was statistically significant (p < 0.001). Qualitative analysis of AI responses indicated that Gemini and DeepSeek showed greater attention to local conditions (e.g., "price fluctuations in Iran" and "cost differences in provincial cities"), while ChatGPT and Grok provided standard, repetitive explanations, and Claude offered general, non-specialized content.
Conclusion: This study concludes that no single approach to construction cost estimation is sufficiently reliable for independent application. Human experts, while confident, exhibit significant variability. Online systems lack both accuracy and consistency. AI models show promise but vary widely in performance and stability. The hybrid "human-in-the-loop" approach is proposed as the most effective strategy, wherein a stable AI model (such as Grok) generates an initial estimate, which is then validated, refined, and completed by human experts based on their contextual knowledge and experience. Engineers and project managers are advised to evaluate AI model stability across multiple iterations and select models with CV < 15% for practical applications. Professional engineering organizations should develop formal guidelines and training programs to enhance trust and adoption of AI technologies. Future research should expand the sample size, replicate the study across diverse projects and cities, and develop explainable AI tools to improve transparency and trust. Despite rapid AI advancements, engineering judgment remains indispensable, and new technologies should serve as complementary tools augmenting, not replacing, human expertise.

Abstract Aims: Mosques have long been among the most significant religious, cultural, and urban elements of Iranian cities. Beyond their spiritual function, they have historically served as platforms for the expression of artistic values, social identity, and political authority. During the Qajar period (1789–1925), Iranian architecture experienced a distinctive condition characterized by the simultaneous preservation of traditional architectural principles and the emergence of new decorative approaches. Architectural ornamentation—including tilework, stucco decoration, mirror work, inscriptions, geometric patterns, floral motifs, and colored glass—played a fundamental role not only in beautifying religious spaces but also in communicating ideological, social, and political messages. Although numerous studies have investigated the architectural characteristics and decorative elements of Qajar monuments, relatively few have examined ornamentation as a medium through which art and power were simultaneously represented. Therefore, the present study aims to investigate the stylistic characteristics of Qajar mosque architecture with particular emphasis on architectural ornamentation as a reflection of both artistic expression and symbolic power. The study seeks to identify patterns of continuity and innovation in Qajar mosque decoration and to explain how ornamentation contributed to the representation of religious legitimacy, social prestige, and political authority.
Materials & Methods: This research adopts a qualitative approach and is based on a comparative case-study methodology. The study combines stylistic analysis, semiotic interpretation, and historical-archival investigation to provide a multidimensional understanding of architectural ornamentation. Three prominent Qajar mosques were selected as case studies: Agha Bozorg Mosque in Kashan, Nasir al-Mulk Mosque in Shiraz, and Sepahsalar Mosque in Tehran. These monuments were chosen because of their architectural significance, geographical diversity, historical importance, and distinctive decorative programs. Data collection was conducted through documentary studies, architectural surveys, visual analysis of decorative elements, and examination of historical and archival sources. The analytical framework was structured around three principal dimensions. The first dimension focused on stylistic characteristics, including spatial organization, architectural form, decorative techniques, and material application. The second dimension addressed semiotic aspects, examining inscriptions, geometric patterns, vegetal motifs, color schemes, and symbolic meanings embedded within decorative compositions. The third dimension investigated patronage, historical context, and manifestations of power through the analysis of patrons, socio-political circumstances, urban location, and intended audiences. Comparative analysis was then undertaken to identify similarities and differences among the selected mosques and to evaluate the relationship between architectural ornamentation, artistic expression, and power representation.
Findings: The findings reveal that all three mosques preserved the fundamental structural principles of traditional Iranian-Islamic mosque architecture, including courtyards, iwans, domed chambers, and prayer halls. Nevertheless, significant differences emerged in the decorative language and symbolic functions of ornamentation. In Agha Bozorg Mosque, ornamentation primarily reflected local religious legitimacy and scholarly authority. Decorative elements such as tilework, stucco carvings, geometric compositions, and Quranic inscriptions reinforced the educational and religious identity of the mosque-school complex. The decorative program emphasized continuity with established architectural traditions while simultaneously demonstrating the craftsmanship and cultural prestige of local patrons. Consequently, ornamentation functioned as a medium for expressing social respectability and intellectual status rather than overt political power. Nasir al-Mulk Mosque presented a different approach. The extensive use of colored stained-glass windows, vibrant tilework, and elaborate floral motifs created a unique sensory experience based on the interaction of light and color. Here, ornamentation transcended conventional aesthetic functions and became an instrument of social distinction and familial prestige. The decorative environment reflected the economic resources, artistic aspirations, and cultural ambitions of its patron. Through the careful orchestration of color, light, and ornament, the mosque established a highly recognizable architectural identity within the urban landscape of Shiraz. The findings concerning Sepahsalar Mosque demonstrated the strongest relationship between ornamentation and political authority. Constructed under the patronage of Mirza Hossein Khan Sepahsalar, a prominent statesman of the Qajar court, the mosque employed monumental scale, extensive inscription bands, elaborate tilework, and highly visible urban forms to project governmental legitimacy and institutional power. Decorative elements were integrated into a broader political narrative aimed at strengthening connections between religion and state authority. In this case, ornamentation served not merely as an artistic expression but also as a deliberate strategy of symbolic representation. The comparative analysis further demonstrated that ornamentation fulfilled three major functions across the studied examples: reinforcing local religious and educational legitimacy, expressing social distinction and family prestige, and communicating state-sponsored political authority. These functions varied according to the nature of patronage, urban context, and intended audience.
Conclusion: The study concludes that architectural ornamentation in Qajar mosques cannot be interpreted solely through aesthetic criteria. Rather, ornamentation constituted a complex visual language through which artistic creativity, cultural identity, religious legitimacy, social status, and political power were simultaneously communicated. The stylistic analysis revealed that the Qajar period should not be understood as a complete departure from previous architectural traditions but rather as a hybrid condition in which traditional Iranian forms were preserved while decorative innovation expanded significantly. The comparative investigation demonstrates that the meaning and function of ornamentation were strongly influenced by patronage structures and socio-political contexts. In locally sponsored religious complexes, decoration primarily reinforced scholarly and religious legitimacy; in elite family-sponsored monuments, it expressed social prestige and cultural distinction; and in state-sponsored projects, it became an instrument of political representation and symbolic authority. Therefore, ornamentation operated as both an artistic achievement and a mechanism of power. The research contributes to the existing literature by integrating stylistic, semiotic, and historical perspectives into a unified analytical framework. It also highlights the importance of incorporating archival documentation and patronage studies into future investigations of Islamic architectural ornamentation. Further interdisciplinary research involving architectural history, social history, and political studies may provide a deeper understanding of how visual culture participated in the construction and communication of power in Qajar Iran.