This study presents a novel cyber-resilient, data-driven optimisation framework for real-time energy management in electric vehicle (EV)-integrated smart grids. The proposed framework integrates a hybrid optimisation engine—combining genetic algorithms and reinforcement learning—with a real-time analytics module to enable adaptive scheduling under uncertainty. It accounts for dynamic electricity pricing, EV mobility patterns, and grid load fluctuations, dynamically reallocating charging demand in response to evolving grid conditions. Unlike existing GA/RL schedulers, this framework uniquely integrates adaptive optimisation with resilient forecasting under incomplete data and lightweight blockchain-inspired cyber-defence, thereby addressing efficiency, accuracy, and security simultaneously. To ensure secure and trustworthy EV–grid communication, a lightweight blockchain-inspired protocol is incorporated, supported by an intrusion detection system (IDS) for cyber-attack mitigation. Empirical evaluation using European smart grid datasets demonstrates a daily peak demand reduction of 9.6% (from 33 kWh to 29.8 kWh), with a 27% decrease in energy delivered at the original peak hour and a redistribution of demand that increases delivery at 19:00 h by nearly 25%. Station utilisation became more balanced, with weekly peak normalised utilisation falling from 1.0 to 0.7. The forecasting module achieved a mean absolute error (MAE) of 0.25 kWh and a mean absolute percentage error (MAPE) below 20% even with up to 25% missing data. Among tested models, CatBoost outperformed LightGBM and XGBoost with an RMSE of 0.853 kWh and $R^2$ of 0.416. The IDS achieved 94.1% accuracy, an AUC of 0.97, and detected attacks within 50–300 ms, maintaining over 74% detection accuracy under 50% novel attack scenarios. The optimisation runtime remained below 0.4 s even at five times the nominal dataset scale. Additionally, the study outlines a conceptual extension to support location-based planning of charging infrastructure. This proposes the alignment of infrastructure roll-out with forecasted demand to enhance spatial deployment efficiency. While not implemented in the current framework, this forward-looking integration highlights opportunities for synchronising infrastructure development with dynamic usage patterns. Collectively, the findings confirm that the proposed approach is technically robust, operationally feasible, and adaptable to the evolving demands of intelligent EV–smart grid systems.

The rapid growth of the global population and associated increases in resource consumption have accelerated environmental degradation, making sustainable design and construction processes increasingly essential. The construction sector holds significant potential for reducing environmental impacts, especially through sustainability-focused certification systems such as LEED. This study evaluates the projected energy efficiency and sustainability performance of the Surgical Sciences Building at Istanbul University’s Çapa Campus, which was designed with the goal of achieving LEED Gold certification. The assessment is based on design-phase data and conducted prior to construction. Energy performance analyses were carried out using DesignBuilder software, supported by the LEED Assessment Report and Energy Audit Report. According to simulation results, approximately 30% savings in energy consumption and water usage are expected. In addition, the process-oriented LEED approach is expected to result in a total CO₂ emission savings of approximately 570 tonnes, while renewable energy systems are expected to meet approximately 13% of the building’s primary energy demand and reduce CO₂ emissions by approximately 151 tonnes per year. Waste management strategies developed for both the construction and operational phases are aligned with LEED criteria and aim to achieve up to 80% recycling rates. The findings demonstrate that LEED certification, when employed as a process-oriented design and decision-making tool rather than a result-oriented label, can enable sustainable strategies to be integrated from the earliest stages of project development. Particularly for complex healthcare buildings, embedding LEED principles into the design process has strong potential to enhance environmental performance. Although based on a single case study, this research provides valuable insight into the broader applicability of LEED in diverse building types and geographic contexts.

The adoption of automation technologies across various industries has significantly increased in recent years. Despite the widespread integration of robotics in many sectors, the construction industry remains predominantly reliant on manual labour. This study is motivated by the need to accurately recognise construction worker activities in labour-intensive environments, leveraging deep learning (DL) techniques to enhance operational efficiency. The primary objective is to provide a decision-support framework that mitigates productivity losses and improves time and cost efficiency through the automated detection of human activities. To this end, sensor data were collected from eleven different body locations across five construction workers, encompassing six distinct construction-related activities. Three separate recognition experiments were conducted using (i) acceleration sensor data, (ii) position sensor data, and (iii) a combined dataset comprising both acceleration and position data. Comparative analyses of the recognition performances across these modalities were undertaken. The proposed DL architecture achieved high classification accuracy by incorporating long short-term memory (LSTM) and bidirectional long-term memory (BiLSTM) layers. Notably, the model yielded accuracy rates of 98.1% and 99.6% for the acceleration-only and combined datasets, respectively. These findings underscore the efficacy of DL approaches for real-time human activity recognition in construction settings and demonstrate the potential for improving workforce management and site productivity.

This study aims to predict the outcomes of construction disputes before they proceed to litigation and to foster a constructive environment between parties. Within the scope of the study, a total of 24 legal factors; 14 legal factors were identified through extensive literature review and 10 legal factors were identified through content analysis. These legal factors were used in three stages: Pre-Litigation (A, B) and Post-Litigation. Legal factors with significant relationships were tested with 24 different machine learning algorithms. NB Tree, Logit Boost and LMT algorithms achieved 63.79%, 63.66% and 86.90% accuracy for models A, B and C, respectively.