• General Article

    Evaluation of the traditional calibration methods in ICP-MS analysis of toxic elements in water
    Trevor Chiweshe and Sumit Kumar
    The current study evaluates the inductively coupled plasma mass spectrometry (ICP-MS) external calibration method for trace elemental analysis of arsenic, cadmium, chromium, … + READ MORE
    The current study evaluates the inductively coupled plasma mass spectrometry (ICP-MS) external calibration method for trace elemental analysis of arsenic, cadmium, chromium, mercury and lead in polluted water samples. The method was first assessed in standard and collision modes using the South African Bureau of Standards reference solutions (SABS), ground water, surface water, treated water and wastewater. Analysis in the standard mode was at least twice affected by the changes in the sample matrix than in the collision mode. Data analysis revealed the effects of sample matrix (alkalinity, pH, turbidity and total dissolved solids TDS) to the signal response in both modes. The standard mode recorded false-low recoveries of chromium, and lead and false-high recoveries of mercury, cadmium and arsenic from the SABS. Error margins in both standard and collision mode were beyond ±15 and ±5% respectively. The error margin varied depending on the type of water, interfering polyatomic species and the sample matrix. Mercury analysis was influenced by memory effects, which was circumvented by using a gold standard to amalgamate with mercury. - COLLAPSE
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    30 June 2026

  • General Article

    Agentic AI for sustainable development: Trust protocol for carbon emissions decomposition and retrospective analysis
    Timur Dosmambetov and Yonghan Ahn
    Large language models (LLMs) can generate coherent narratives about quantitative results, but their probabilistic nature creates a critical reliability gap for retrospective … + READ MORE
    Large language models (LLMs) can generate coherent narratives about quantitative results, but their probabilistic nature creates a critical reliability gap for retrospective auditing tasks, where error tolerance is effectively zero. The dissertation fills this gap through the introduction of a Trust Protocol for agentic architecture framework using multi-agent systems. It depends on the implementation of separation of concerns design, which combines gated workflow with identity-constrained methods for qualitative and quantitative reporting. The protocol uses a gated workflow that starts with input validation to enforce a strict data schema, then runs deterministic LMDI calculations as the identity constraint, and finally applies anchoring and validation so the report can only use values that appear in the verified output artifacts. The system is evaluated using a hybrid point of comparison that falls between single agent, tool-assisted reporting and an overall multi-agent workflow. Measures used in the evaluation include identity closure consistency, hallucination failure flags such as numerical mismatch, unanchored interpretation, contextual claims without citations, and causal overreach, as well as workflow completion reliability and runtime cost profile, which are measured by token use, tool call success rates, and execution latency. The validation testbed is a Kyrgyzstan manufacturing-sector dataset (2012-2023) due to its accounting identity that makes it amenable to unit-test type verification of report correctness. Findings indicate that the proposed architecture can maintain numerical accuracy with deterministic ground truth and minimize unanchored interpretive failures compared to the hybrid baseline which allows supporting the argument that protocol-level constraints and not instruction-following alone are necessary to achieve audit-grade AI assistance. - COLLAPSE
    PDFCitation
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    30 June 2026

  • General Article

    Analysis of score attainment in the materials and resources category of Korea’s green building certification (G-SEED): A Study on non-residential buildings
    Taehyoung Kim, Kyungjoo Cho, Yosun Yoon, Sungmo Seo, Daehee Jang and Seungjun Roh
    The building sector is increasingly required to address environmental impacts beyond operational energy use, particularly those associated with building materials and resource … + READ MORE
    The building sector is increasingly required to address environmental impacts beyond operational energy use, particularly those associated with building materials and resource consumption. In response to this shift, the Materials and Resources (MR) category of Korea’s Green Standard for Energy and Environment Design (G-SEED) has gained growing importance. However, empirical studies analyzing long-term certification data across the entire MR category remain limited. This study quantitatively investigates trends in score acquisition for MR certification items in 5,884 newly constructed non-residential buildings that received preliminary G-SEED certification between 2017 and 2024. The analysis examines differences by building type and certification grade, as well as the distribution patterns of individual MR credits. The results show that higher certification grades consistently correspond to higher MR scores across all building types, indicating that the MR category plays a meaningful role in overall grade differentiation. However, substantial differences in discrimination power were identified among individual credits. The credit related to the application ratio of green building materials demonstrated strong influence on certification grades but exhibited high zero-score rates, suggesting excessive difficulty for lower-grade projects. In contrast, credits associated with Environmental Product Declarations (EPDs), recycled materials, low-carbon materials, and low-hazard materials showed stable and widespread adoption but limited grade discrimination. The only mandatory MR credit, concerning recyclable resource storage facilities, displayed strong score polarization, indicating limitations in its current evaluation method. Variations among building types were also observed, with more balanced credit performance in educational and hospitality buildings than in office and commercial facilities. These findings suggest the need to recalibrate credit difficulty, improve score discrimination, and refine evaluation granularity, particularly for mandatory items. This study provides empirical evidence on how material-related criteria function within a national green building certification system over time and offers data-driven insights to support the future improvement of G-SEED toward a more performance-oriented and policy-aligned materials assessment framework. - COLLAPSE
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    30 June 2026

  • General Article

    Intelligent video monitoring system for risk assessment and behavioral analysis in sustainable building environments
    Mohammed Arif Hasan Chowdhury, Jeenat Sultana, Shusmoy Chowdhury, Mashetti Santhoshi, Rajender Kumar and Gaurav Sharma
    Sustainable building environments increasingly rely on intelligent monitoring systems to enhance safety, optimize facility management, and support secure urban infrastructure. This study … + READ MORE
    Sustainable building environments increasingly rely on intelligent monitoring systems to enhance safety, optimize facility management, and support secure urban infrastructure. This study presents an intelligent video monitoring framework for behavioral analysis and risk assessment within sustainable building environments using classical machine learning techniques. The proposed non-invasive system analyzes visual cues derived from facial and hand gestures captured through surveillance video streams. Nine behavioral features were extracted and categorized into facial and hand gesture modalities. Seven supervised classification algorithms—Logistic Regression, Decision Tree, k-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), Gaussian Naïve Bayes, Support Vector Machine (SVM), and Random Forest—were implemented to evaluate system performance. Comparative analysis demonstrates that KNN and SVM achieved the highest overall accuracy of approximately 80% when multimodal gesture features were fused. Individually, facial gestures yielded up to 79% accuracy using LDA, while hand gestures achieved 72% accuracy. The results indicate that classical machine learning methods can effectively support behavioral risk detection in smart building environments. The proposed framework offers a cost-effective and scalable solution that can be integrated into sustainable building management systems to enhance safety monitoring and contribute to resilient urban infrastructure. - COLLAPSE
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    30 June 2026

  • General Article

    An explainable hybrid CNN LSTM framework with grasshopper optimization for sustainable pedestrian behavior modeling and analysis
    Tanya Gupta and Neera Batra
    One of the major challenges of smart-city surveillance is the ability to detect the pedestrian behavior in large and congested cities. Our … + READ MORE
    One of the major challenges of smart-city surveillance is the ability to detect the pedestrian behavior in large and congested cities. Our system is a hybrid deep-learning system, based on Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. This mixture detects spatial and time trends of pedestrian videos. The Grasshopper Optimization Algorithm is utilized to optimize hyperparameters and choose the most suitable features that help to approach convergence better and increase the precision of predictions. Explainable AI methods enhance the transparency of the safety-critical urban environment. We use Grad-CAM to raise spatial areas and SHAP to measure the contribution of each feature into predictions. The framework was benchmarked on JAAD, PETS, CASIA and CUHK Avenue. The accuracy of the results is 96.8 percent and the F1 score is 0.961 and the generalization is strong with the datasets. Our method is healthier when compared to the conventional CNNLSTM models, and other deep-learning baselines, and has less false-positive, as well as being more interpretable. This solution is one of the effective and clean real-time solutions to pedestrian monitoring, which ensures developing a safer and more sustainable movement in urban environments. - COLLAPSE
    PDFCitation
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    30 June 2026

  • General Article

    Revealing the impacts of external environment on greenwashing behavior: A study in the Vietnamese construction industry
    Minh Van Nguyen, Thuc Dinh Le and Khoa Van Nguyen
    Greenwashing undermines sustainable development by misleading consumers about a company’s environmental practices. Previous studies highlighted that the external environment affects corporate greenwashing … + READ MORE
    Greenwashing undermines sustainable development by misleading consumers about a company’s environmental practices. Previous studies highlighted that the external environment affects corporate greenwashing in various ways. However, these studies often examined external organizational factors separately, without considering their combined impact on greenwashing behavior. This study aims to comprehensively investigate how external environmental factors influence corporate greenwashing. Grounded in the Fraud Triangle Theory, the research developed hypotheses based on an extensive literature review. Data were collected from 142 respondents via convenience sampling and analyzed using PLS-SEM within the Vietnamese construction sector. The results indicate that economic, environmental, and legal factors positively affect corporate greenwashing, whereas social factors negatively affect it. In contrast, political and technological factors were found to have no significant influence on greenwashing. This study underscores the importance of societal pressures in mitigating greenwashing and highlights the need for stricter regulations and enhanced transparency in economic, environmental, and legal domains. To address greenwashing effectively, the focus should be on strengthening regulatory frameworks and promoting accountability in these areas. - COLLAPSE
    PDFCitation
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    30 June 2026

  • General Article

    A Proposed procurement document structuring framework for modular housing based on a comparative analysis of national requirement structures
    Jinhak Jeong and Yonghan Ahn
    This study proposes a function-based framework for structuring procurement documents for modular housing based on a comparative analysis of national requirement structures. … + READ MORE
    This study proposes a function-based framework for structuring procurement documents for modular housing based on a comparative analysis of national requirement structures. Rather than directly comparing document titles or institutional labels, the study examines how modular-related requirements are organized and operated across countries and reclassifies them into four functional domains: design, construction, construction management, and submission and evaluation. A qualitative document analysis was conducted using national modular-related guidance, approval, and assessment documents from six countries: Singapore, Hong Kong, the United Kingdom, New Zealand, Australia, and the United States. The findings show that although these national documents differ in format and regulatory context, they share comparable functional structuring logics. Submission and evaluation emerged as a common core function across countries, while the relative emphasis on design, construction, and construction management varied by operating type. Construction management was more prominent in central regulatory authority-led systems, design in certification-, warranty-, and conformity-centered systems, and construction in national framework with subnational approval implementation systems. Based on these findings, the study proposes a type-adaptive framework that retains four common functional blocks while adjusting the level of detail of specific blocks according to the relevant national operating context. - COLLAPSE
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    30 June 2026

  • General Article

    Towards durable and sustainable building power generation: Tuning oxygen vacancies and carrier mobility in La2-xBaxNiO4±δ nickelates for high-efficiency solid oxide fuel cells
    Zabihullah Jaweed, Pallavi Sharma, Halan Ganesan, Sukhpreet Singh, Deepak Sharma, Surya Kant Singh, Ramesh Chand, Ravinder Pal Singh, Ankit Oza and Manoj Kumar
    This work investigates the Ruddlesden-Popper (RP) nickelates La2-xBaxNiO4±δ as cathode materials for solid oxide fuel cells. … + READ MORE
    This work investigates the Ruddlesden-Popper (RP) nickelates La2-xBaxNiO4±δ as cathode materials for solid oxide fuel cells. Single phase samples with x = 0.2, 0.6 and 0.9 have been prepared and characterized for crystal structure, oxygen non-stoichiometry, electrical conductivity and thermal expansion. Of the compositions studied, La1.1Ba0.9NiO4±δ exhibited highest electrical conductivity and La1.4Ba0.6NiO4±δ exhibited better structural stability and less oxygen loss. The results demonstrate that moderate Ba doping improves oxygen vacancy formation and charge transport, and thus these nickelates are promising candidates for intermediate temperature SOFC cathodes. - COLLAPSE
    PDFCitation
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    30 June 2026

  • General Article

    Developing a data-driven competency framework and maturity model structure for modular construction designers
    Woojae Kim and Yonghan Ahn
    This study proposes a data-driven competency framework and maturity model design for modular construction designers in the digital transformation era. Modular construction … + READ MORE
    This study proposes a data-driven competency framework and maturity model design for modular construction designers in the digital transformation era. Modular construction is increasingly positioned as a strategic response to low productivity, skilled labor shortages, housing supply pressure, and carbon-neutral construction demands. However, the competencies required of designers who must integrate architectural design, factory production, transportation, installation, BIM-based coordination, and digital construction technologies remain insufficiently structured. To address this gap, the study analyzed online job postings for modular, prefabricated, and off-site construction design roles. Data were collected from eight countries and 15 job platforms, resulting in 21,935 raw records. After text cleaning, duplicate removal, blacklist filtering, length filtering, and Hybrid Whitelist screening, 3,769 English postings were retained for BERTopic analysis. The model produced 37 topics and met the predefined validation criteria for topic coherence, diversity, topic count, and outlier ratio. Through pre-Ward noise removal, Ward hierarchical clustering, post-Ward refinement, merging, and semantic splitting, the study identified 17 competency domains, 43 competency categories, and 98 observable performance statements. A Korean job posting cross-validation further showed that 89.1% of 1,283 domestic postings could be mapped to the proposed domains, indicating broad contextual applicability while revealing Korea’s stronger emphasis on manufacturing and production competencies. Finally, the study proposes an exploratory five-tier priority structure and a five-level maturity model design that can support recruitment, training, self-assessment, and organizational competency development for modular construction designers, while acknowledging that expert-weighted validation remains a subsequent step. - COLLAPSE
    PDFCitation
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    30 June 2026

  • General Article

    IoT sensor-based AI framework for sustainable building performance optimization using random forest prediction modeling
    Parneet Kaur, Deepali Gupta and Mudita Uppal
    The growing need for sustainable and energy-efficient built environments requires intelligent systems for real-time monitoring and optimization of building performance. This study … + READ MORE
    The growing need for sustainable and energy-efficient built environments requires intelligent systems for real-time monitoring and optimization of building performance. This study presents an IoT sensor-based artificial intelligence (AI) framework for sustainable building performance optimization using Random Forest prediction modeling for crop recommendation and XGBoost for fertilizer suggestion. The proposed framework integrates data from IoT-enabled sensors, including temperature, humidity, air quality, occupancy, and energy consumption, to enable data-driven decision-making in smart buildings. Machine learning models such as Decision Tree, Random Forest, and XGBoost are employed to predict optimal operational conditions for enhancing environmental performance and resource efficiency. The datasets, derived from publicly available sources and simulated building environments, ensure accessibility and reproducibility. Model performance is evaluated using standard metrics, including accuracy, precision, recall, and F1-score. Among the models, Random Forest achieves the highest accuracy of 99.55%, outperforming Decision Tree and XGBoost for crop suggestion, and XGBoost achieves a higher accuracy 99.46% for fertilizer suggestion than Random Forest and Decision Tree. The proposed approach supports sustainable building management by improving energy efficiency, indoor environmental quality, and operational decision-making. It aligns with key aspects of environmental performance assessment, facility management, and smart urban development. Future work may incorporate deep learning and hybrid models to enhance scalability across diverse building types and climatic conditions. - COLLAPSE
    PDFCitation
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    30 June 2026

  • General Article

    AI-driven THz MIMO antenna optimization device for intelligent building monitoring and sustainable urban infrastructure
    Rahul Gupta, Hemlata Sinha and Anil Kumar Soni
    The development of sustainable smart buildings and intelligent urban infrastructure increasingly depends on energy-efficient high-speed wireless communication and sensing devices for structural … + READ MORE
    The development of sustainable smart buildings and intelligent urban infrastructure increasingly depends on energy-efficient high-speed wireless communication and sensing devices for structural monitoring, building automation, and infrastructure management. Structural health monitoring (SHM) devices require reliable techniques to detect early-stage damage, identify defect locations, analyse structural stability, and ensure safe and reliable operations of critical infrastructure. Terahertz (THz) technology has emerged as a effective solution than other lower frequencies because THz waves are non-ionizing, safe for human environments, capable of penetrating many non-metallic materials, and capable of achieving high-resolution image and material characterization. However, the design and optimization of Terahertz (THz) Multiple-Input Multiple-Output (MIMO) antennas for sixth-generation (6G) Internet of Things (IoT) devices are constrained by the high computational cost of full-wave electromagnetic simulations. To address this challenge, this study presents a high-fidelity population-centric deep learning framework, termed Pocaii-DNN, for rapid and accurate electromagnetic modeling of THz MIMO antennas in sustainable building and urban sensing applications. The proposed framework employs four parallel Adaptive Intelligence Units (AIUs) to independently process frequency, parametric analysis of patch geometry, slot geometry, and feed position parameters, followed by a Cooperative Fusion mechanism to capture nonlinear interactions among heterogeneous antenna design variables. The model is trained as a regression-based electromagnetic emulator to predict the antenna reflection coefficient (S11) directly from design parameters. Experimental results demonstrate solver-grade accuracy, achieving a mean absolute error (MAE) of 0.051346 dB, a mean squared error (MSE) of 0.003735 dB², and a coefficient of determination (R²) of 0.997648 on unseen test datasets. Comparative analysis with Random Forest and Decision Tree models confirms superior prediction performance and generalization capability. Furthermore, the trained framework generates S11 predictions within milliseconds, significantly reducing computational time and energy consumption compared with conventional HFSS simulations. The optimized absorber-assisted THz MIMO antenna operates over 2.20 THz to 5.50 THz with a wide bandwidth of 3.30 THz and exhibits excellent MIMO performance, resulting an ECC of 0.00814, a DG of 9.9997 dB, a TARC of −14.35 dB, and a CCL of 0.8386 bits/s/Hz providing improved impedance matching, bandwidth response, noise performance, and quality factor, ensuring stable operation at terahertz frequencies. The proposed approach enables efficient virtual prototyping and optimization of THz MIMO antennas for sustainable smart buildings, integrated sensing and communication (ISAC) applications, enabling crack, void, moisture, and material degradation detection in structural health monitoring systems., and intelligent urban infrastructure system. - COLLAPSE
    PDFCitation
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    30 June 2026
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