Kourosh Behzadian

This study analyses the free vibration and thermal buckling of size-dependent, axially functionally graded Euler–Bernoulli nanobeams using a nonlocal thermo-elastic Fredholm integral model based on Eringen’s nonlocal elasticity. The elastic modulus and mass density vary exponentially along the beam, so the thermal load, modeled as an equivalent axial force, follows the same exponential law. Four successive Fredholm integrations convert the governing equation into a weak-form integral equation that builds in the boundary conditions directly; this equation is then solved with a power series Galerkin method. The critical thermal buckling force is the axial force at which the fundamental frequency vanishes. Simple–simple (S–S), clamped–clamped (C–C), clamped–simple (C–S) and clamped–free beams are examined. Predicted frequencies agree with published solutions to within 0.11%. For a homogeneous beam, increasing the nonlocal parameter from zero to 0.2 lowers the fundamental frequency by 15.3% (S–S) and 18.3% (C–C). Shifting the thermal force from tension (−3) to compression (+3) lowers it by 26.9% (S–S) and 7.1% (C–C). Raising the nonlocal parameter from zero to 0.3 reduces the C–C buckling force by about 78% for a homogeneous beam and 94% for a gradient index of −2. Changing the gradient index from −2 to two lowers the first C–S frequency by 24.7% but the third by only 3.6%.

The growing reliance on early flood warning systems reflects broader advances in real-time flood forecasting, where increasing data availability, modelling complexity, and the integration of data-driven and AI-based approaches have been identified as critical enablers for effective urban flood risk management [1]. To investigate if flood warning distribution is affected by hydrological variables, socioeconomic vulnerabilities or physical exposure, this study evaluates the spatial and temporal trends of the Environment Agency (EA)’s flood warnings between 2006-2024 within London boroughs.By applying a comprehensive methodology incorporating GIS analysis, socioeconomic correlation analysis and time-series integration, Flood Alert (FA), Flood Warning (FW) and Severe Flood Warning (SFW) data were analysed using EA’s open-access data. Significant spatial variations were identified within the analysis. Riparian boroughs along the River Thames corridor experienced significantly higher FW rates (>293 alerts per borough approximately per year) compared to non-riparian boroughs (

Real-time flood forecasting and early warning systems (RTFF–EWS) have become central to contemporary flood risk management, particularly as climate change intensifies the frequency, magnitude, and spatial complexity of flood events. Recent advances demonstrate a clear shift from conventional physics-based forecasting towards integrated digital ecosystems that combine multi-source data acquisition, advanced analytics, artificial intelligence, and immersive decision-support interfaces. This study synthesises state-of-the-art tools and emerging research directions shaping next-generation RTFF–EWS.On the data side, dense Internet of Things (IoT) sensor networks, remote sensing (radar, LiDAR, satellites, drones), and crowd-sourced information now enable near–real-time monitoring of hydrological and hydraulic states across urban and catchment scales. These heterogeneous data streams are increasingly fused through machine learning (ML) and deep learning (DL) frameworks, including recurrent neural networks, long short-term memory models, and hybrid physics-informed approaches, to enhance forecast lead time, accuracy, and robustness under data scarcity and uncertainty. Natural language processing (NLP) and large language–based pipelines (LLP) further extend RTFF capabilities by extracting actionable intelligence from unstructured data such as social media, emergency reports, and textual observations, improving situational awareness during rapidly evolving flood events.Beyond forecasting, digital visualisation technologies are redefining how flood information is communicated and operationalised. Virtual reality (VR), augmented reality (AR), mixed reality (MR), and digital twins (DT) provide immersive and interactive representations of flood dynamics, impacts, and response options. These tools enable scenario testing, stakeholder engagement, and decision rehearsal across the full flood risk management cycle, from preparedness and response to recovery. Digital twins, in particular, are emerging as integrative platforms that couple real-time sensor data, predictive models, and visual interfaces into living representations of urban water systems. Despite these advances, key challenges remain, including data reliability, computational demands, interoperability across platforms, and the translation of complex model outputs into inclusive, actionable warnings for diverse stakeholders. This study also shows plausible deployment pathways for contemporary digital technologies, particularly NLP/LLP-enabled information extraction and AI-driven multi-agent frameworks, and demonstrates how their integration with RTFF–EWS can support adaptive, interpretable, and decision-centred flood risk management under real-world operational constraints.

Real-time flood risk assessment requires integrated frameworks that not only forecast flood dynamics accurately [1] but also capture how people respond to rapidly changing hazard conditions [2,3]. This study develops a novel real-time flood impact assessment framework that couples AI-driven flood forecasting (Long Short-Term Memory) with an agent-based model (ABM) to evaluate human mobility disruption and behavioural adaptation during flood events.The outputs of the flood forecasting model are dynamically transferred to an ABM that represents urban populations, daily activity schedules, and transport networks. Agent (i.e., road users with different demographic attributes) decision-making is governed by a novel risk priority index (RPI), which integrates direct flood exposure, official communication, and demographic vulnerability of agents. Watts-Strogatz small-world network was used to consider the interaction of agents and realistically represent information diffusion, allowing the dissemination of risk awareness.Results reveal substantial real-time impacts on urban mobility, with significant increases in travel times, particularly during peak hours. Moreover, incorporating behavioural adaptation through the RPI in agent-based modelling highlights the critical role of flood-risk information sharing among. A balanced combination of personal flood experience (Individual RPI) and socially shared information (average RPI of neighbouring agents) leads to faster and more effective formation of risk awareness across the population. The proposed AI-driven, agent-based framework enables real-time evaluation of flood impacts on population groups and transport systems, offering a powerful tool for emergency response planning and operational flood risk mitigation by local authorities.References[1] Piadeh F., Bakhtiari, V., Piadeh, F. (2026). Automated novel real-time framework for rainfall data imputation in flood early warning systems, Engineering Applications of Artificial Intelligence, 1164(B), p.113348. https://doi.org/10.1016/j.engappai.2025.113348[2] Qin, H., Liang, Q., Chen, H., De Silva, V. (2024). A Coupled Human and Natural Systems (CHANS) framework integrated with reinforcement learning for urban flood mitigation. Journal of Hydrology, 643, 131918. https://doi.org/10.1016/j.jhydrol.2024.131918[3] Bakhtiari, V., Piadeh, F., Chen, A.S., Behzadian, K. (2024). Stakeholder analysis in the application of cutting-edge digital visualisation technologies for urban flood risk management: A critical review. Expert Systems with Applications, 236, 121426. https://doi.org/10.1016/j.eswa.2023.12142

Reliable early warning is crucial for the operational stability of urban wastewater treatment infrastructure in response to emergencies that might threaten the environment and human health. In sewage treatment works (STWs), process anomalies can be driven by extreme influent loads, episodic operational interventions, or sensor faults. Therefore, under these multivariate, non-stationary conditions, it is challenging to ensure timely and robust practical response based on manual supervision alone. This study presents an AI-driven analytical framework for real-time early warning of process anomalies using multi-source sensor monitoring data. The framework offers a pipeline to transform continuous, real-time monitored sensor data into fixed-length time-series windows suitable for deep learning and real-time inference. A deep unsupervised learning model is innovatively introduced to learn multivariate dynamics and cross-variable dependencies of normal operation modes, and then to generate window-level scoring and map it to early warning alerts. To improve the interpretability, window-level alerts are aligned with timestamped, manually recorded event management logs to distinguish between sensor malfunctions and process disturbances. The framework is demonstrated on a multi-year real-world urban STW’s dataset, and evaluated based on detection timeliness, false alarm behaviour, and consistency with logged operational events. Results indicated that the proposed framework is a feasible approach to integrate contextual evidence with AI-driven early warning alarms. It also offers a promising powerful tool to support real-time anomaly diagnosis and decision-making for STWs’ operators.

Agent-based modelling (ABM) is increasingly recognised as an essential tool for urban flood risk assessment to analyse the response of people and transport systems under dynamically evolving flood conditions [1,2]. However, the dynamic evolution of risk awareness and the exchange of risk-related information during flood events remain inadequately represented in many existing modelling approaches [3, 4]. This study presents an integrated flood-impact assessment framework that couples a hydrodynamic flood model with a risk-based ABM to evaluate the impact of flooding on travelling population groups and road network performance.Flood modelling is simulated using a hydrodynamic model calibrated through the sequential uncertainty-fitting algorithm, providing reliable spatio-temporal flood characteristics. These hydraulic outputs are dynamically linked to an ABM representing urban populations with realistic daily activity. People’s behavioural adaptation is governed by a novel risk priority index, which evolves based on direct flood exposure, institutional communication, and risk-information exchange. People are assumed to interact together through Watts-Strogatz small-world network, enabling realistic diffusion of risk awareness across the population.Results show that flood-induced road closures trigger sharp increases in travel times. Agent-based analysis revealed that, among population groups, adults experienced the highest total flood exposure, followed by seniors and children. Moreover, travel mode strongly influences vulnerability, with cycling users experiencing the highest exposure levels, followed by public transit, walking, and driving users. The proposed framework provides a robust decision-support tool for evaluating how risk awareness and social interaction through an agent-based model influence road users and road network performance.References[1] Bakhtiari, V., Piadeh, F., Chen, A. S., & Behzadian, K. (2024). Stakeholder analysis in the application of cutting-edge digital visualisation technologies for urban flood risk management: A critical review. Expert Systems with Applications, 236, 121426. https://doi.org/10.1016/j.eswa.2023.121426[2] Najjar Ghabel, S., Zarghami, M., Akhbari, M., & Nadiri A.A. (2019). Groundwater Management in Ardabil Plain Using Agent-Based Modeling, Iran-Water Resources Research 15, 1–16.[3] Kunreuther, H., & Pauly, M. (2006). Rules rather than discretion: Lessons from Hurricane Katrina. Journal of Risk and Uncertainty, 33(1–2), 101–116. https://doi.org/10.1007/s11166-006-0173-x[4] Lo, A. Y. (2013). The role of social norms in climate adaptation: Mediating risk perception and flood insurance purchase. Global Environmental Change, 23(5), 1249–1257. https://doi.org/10.1016/j.gloenvcha.2013.07.019

Heavy metal contamination in wastewater is a major global problem that requires effective, automated, and sustainable treatment systems capable of linking laboratory-scale adsorption processes to intelligent large-scale operation. This work introduces a novel Expert System ( ES ) that combines a Genetic Algorithm ( GA ), Artificial Neural Networks ( ANN ), and Monte Carlo ( MC ) analysis for the automated removal of heavy metals using adsorbents obtained from Low-Density Concrete waste. This research is significant because adsorption systems are complex to automate due to their nonlinear behaviour and sensitivity to fluctuating operating conditions. By facilitating intelligent process control and optimisation, the system overcomes the main drawback of adsorption technology, which is its lack of automation. Experimental datasets for Pb ²⁺ , Co ²⁺ and Mn ²⁺ removal were used to train the ANN , which predicts removal from inputs (pH, adsorbent mass, contact time, initial concentration). GA then used this trained ANN to optimise these same factors. According to the sensitivity analysis, the most important control variables in heavy metal adsorption were heavy metal type, pH, and adsorbent mass. The ANN model demonstrated strong predictive performance ( R ² = 0.908, RMSE = 7.43 %), enabling GA to optimise process conditions and achieve removal efficiencies of 100 % (Pb ²⁺ ), 87.8 % (Co ²⁺ ), and 71.62 % (Mn ²⁺ ) under identified optimal operating parameters. Operational reliability measured by MC simulations showed Co ²⁺ had a higher variability ( CV : Coefficient of Variation = 5.39 %) than Mn ²⁺ ( CV = 3.29 %) and Pb ²⁺ ( CV = 2.13 %), which had minimal uncertainty. Using resources from the circular economy, the ES provides a framework for a digital twin of an Industry 4.0-compliant water treatment system that enables translation of lab-based procedures into practical applications.

Basalt fiber-reinforced composites are increasingly utilized in sustainable construction due to their high strength, environmental benefits, and durability. However, the long-term tensile performance of these composites in alkaline environments remains a critical concern. This study investigates the degradation performance of basalt fibers exposed to different alkaline solutions (NaOH, KOH, and Ca(OH)₂) with varying concentrations (5 g/L, 15 g/L, and 30 g/L) over various exposure periods (7, 14, and 28 days). The performance assessment is carried out by mechanical properties, including tensile strength and modulus of elasticity, using experimental techniques and Response Surface Methodology (RSM) to find influential factors on tensile performance. The findings indicate that tensile strength degradation is highly dependent on alkali type and concentration, with Ca(OH)₂-treated fibers exhibiting superior mechanical retention (max tensile strength: 938.94 MPa) compared to NaOH-treated samples, which showed the highest degradation rate. Five machine learning (ML) models, including Tree Random Forest (TRF), Function Multilayer Perceptron (FMP), Lazy IBK, Meta Bagging, and Function SMOreg (FSMOreg), were also implemented to predict tensile strength based on exposure parameters. FSMOreg demonstrated the highest prediction accuracy with a correlation coefficient of 0.928 and the lowest error metrics (RMSE 181.94). The analysis boosts basalt fiber durability evaluations in cement-based composites.