Search Results (233)

Exit location can influence evacuation efficiency without changing the number of exits, yet its mechanism lacks quantitative characterization. Using a complex single-floor hospital outpatient department floor plan with 186 occupants as the case study, based on a direction-aware cellular automaton (CA) model, this study constructed two exit layout scenarios within the same complex building floor plan and independently repeated 50 simulations for each scenario under identical occupant population and model parameters. A mechanism-oriented analysis was conducted from the perspectives of evacuation efficiency, structural fairness, behavioral fairness, and structure–behavior deviation. The results showed that, in this case, exit relocation shortened the total evacuation time by approximately 20% ($p<0.001$) and significantly reduced the concentration of exit utilization, whereas the service area distribution changed only slightly, and local peak density did not increase significantly. This indicates that exit location improves evacuation efficiency by restructuring the crowd-splitting structure rather than by a simple balancing of structural service coverage. This study provides quantitative evidence for performance-based evacuation design and sustainable safety optimization in complex spaces. Full article

This study introduces a novel risk detection and control system to enhance social stability in major construction projects. Utilizing a heterogeneous cellular automaton model, the system simulates complex interactions among project stakeholders to identify and mitigate Social Stability Risks (SSR). Integrating the Ignorant–Latent–Malcontent–Recovered (ILMR) framework, the model applies principles from epidemiology to predict and manage the spread of social stability risks. Simulation results demonstrate the model’s effectiveness in reducing the number of malcontent and ignorant individuals while increasing the recovered category, stabilizing the social environment around large projects. This approach helps manage immediate risks and improves long-term social acceptance and sustainability of engineering projects. By bridging risk management with advanced simulation techniques, this research contributes to major construction projects by providing a robust framework for managing complex social dynamics, thereby enhancing project success and stakeholder satisfaction. The findings underscore the potential of integrating innovative technological tools with traditional risk management strategies to address the socio-technical challenges of large-scale engineering projects. Full article

Pitting corrosion is a prevalent and highly detrimental form of localized corrosion, which can severely compromise the local load-bearing capacity of metallic materials and, in extreme cases, trigger structural failure. In response to the pronounced susceptibility of Q235 galvanized steel plates to localized pitting under the extreme service conditions of the South China Sea—characterized by high temperature, high salinity, high humidity, and coupled chemical corrosive effects—this study conducts a systematic investigation combining experimental characterization and numerical simulation. First, a novel accelerated pitting corrosion apparatus was designed and developed, and chloride ion cyclic corrosion (CICC) tests were performed on Q235 galvanized steel plates. The morphology and temporal evolution of pitting damage were comprehensively characterized. Subsequently, based on a coupled Cellular Automata (CA) and Finite Element Analysis (FEA) framework, a corrosion evolution model termed CAFE (Cellular Automata-Finite Element) was established. This model elucidates the initiation, growth, and corrosion product evolution of pitting pits under varying temperature and salinity conditions and further quantifies the spatial distributions of stress and temperature fields in the vicinity of pitting sites. Finally, experimental results were employed to validate the rationality and effectiveness of the proposed electro-thermo-mechanical-chemical (ETMC) multi-field coupling model. The results demonstrate that temperature and salinity are the dominant environmental parameters governing the evolution of localized pitting corrosion rates. A strong agreement between numerical predictions and experimental observations is achieved in both qualitative trends and quantitative metrics. Notably, the model reveals that under elevated current-driving conditions, localized plastic deformation plays a critical role in promoting pit propagation and accelerating the pitting corrosion process. Full article

The accurate prediction of extreme dynamic amplification factor (DAF) values is significantly important to ensure a long-term safety assessment of bridges under stochastic vehicular loading. However, predicting extreme DAFs is challenging due to traffic randomness, road roughness variability, and nonlinear vehicle–bridge interaction (VBI) effects. This study presents an integrated framework for extreme DAF prediction for simply supported bridges by combining stochastic traffic–bridge interaction simulations with Bayesian updating and a Peaks-Over-Threshold–Generalized Pareto Distribution (POT–GPD) model. A coupled VBI model is developed, incorporating cellular automaton-based traffic flow, multi-axle nonlinear vehicle dynamics, finite-element bridge modeling, and stochastic road roughness profiles. A new DAF definition based on dynamic displacement difference is proposed to better represent dynamic effects. DAF samples obtained from VBI simulations under different road roughness levels are analyzed using the POT method, with GPD parameters estimated through maximum likelihood and Bayesian inference. Extreme DAFs corresponding to different return periods are then determined. The results indicate that extreme DAF values increase with worsening road roughness and longer return periods and that the Bayesian POT–GPD approach effectively captures tail behavior while providing reliable uncertainty quantification for extreme DAF prediction. Full article

Modeling and visualizing zones within the spread of toxic clouds from fires and explosions during accidents at industrial facilities located near residential areas is of high practical value. This tool is critical for the rapid planning of population evacuation measures and emergency response. Of particular importance is the development of computer software that can quickly model the hazard zone of toxic cloud spread and superimpose it on a terrain map to determine the potential impact on residential areas. This software should be based on a mathematical model that can accurately predict the parameters of the hazard zone both near the industrial facility and beyond it, at a distance of more than 1 km. The objective of this study is to create algorithms for modeling the hazard zone during a fire or explosion at an industrial facility using a cellular automaton method and to develop a software tool for its visualization. The software must display the hazard zone for the population of a nearby residential area on a map in real time, which is necessary for assessing potential harm to residents’ health and in planning their rapid evacuation. To achieve this objective, this article presents a model for determining the boundaries and main parameters of a hazard zone based on the cellular automaton method (frontal and probabilistic). The proposed model takes into account both constants (properties of chemical substances, building parameters, population size, etc.) and variables (the mass of the substance at each explosion and fire, wind speed and direction, air temperature, etc.). The FireSoft III software, developed by the authors and based on the cellular automaton model, provides more rapid calculation of the parameters and delineation of the hazard zone boundaries compared to similar software, which was tested in cases of an ammonia tank explosion and a prolonged fire in a warehouse containing polyvinyl chloride at an enterprise. This makes FireSoft III promising for use in a fire and explosion response at enterprises. Full article

As an interposer, Through-Glass Vias (TGVs) play a critical role in advanced packaging such as Co-packaged optics (CPO). Currently, due to the complex influence of laser wet-etching process parameters, the precise bidirectional prediction of TGV parameters and the etching morphology still remains a challenge. In this paper, a bidirectional design method for TGVs is proposed, which is based on the cross-modal learning method. By integrating a Cellular Automaton Etch-Diffusion (CAED) physical model with a Stable Diffusion (SD) architecture, accurate forward prediction from laser parameters to TGV morphology is realized successfully. In addition, the Contrastive Language–Image Pre-training (CLIP) model is also applied to achieve an efficient inverse design of TGVs. Furthermore, the generalization ability is examined in this paper, demonstrating significant robustness and stability of the generative model. The results provide an efficient method for enhancing TGV quality within a deep learning framework. Full article

Modeling elementary diffusion processes in nanostructured materials is essential for developing platforms capable of interacting with high-speed physical signals. In this work, the photothermal response of a nano-hydroxyapatite/carbon nanotube (nHAp/CNT) composite was experimentally characterized and modeled through a cellular automaton (CA) framework designed to capture the thermal propagation of the hybrid system. Synthesizing nHAp/CNT composites enables the combination of the biocompatible and piezoelectric nature of nHAp with the enhanced photothermal response introduced by CNTs. UV–Vis reflectance measurements confirmed that CNT incorporation increases the optical absorption of the ceramic matrix, resulting in more efficient photothermal conversion. The composite was irradiated with a nanosecond pulsed laser, and the resulting thermal transients were compared with CA simulations based on a D2Q9 lattice configuration. The model accurately reproduces experiments, achieving R² > 0.991 and NRMSE below 2.4% for all tested laser powers. This strong correspondence validates the CA approach for predicting spatiotemporal heat diffusion in heterogeneous nanostructured composites. Furthermore, the model revealed a sensitive thermal coupling when two heat sources were considered, indicating synergistic enhancement of local temperature fields. These findings demonstrate both the effective integration of CNTs within the nHAp matrix and the capability of CA-based modeling to describe their photothermal behavior. Overall, this study establishes a computational–experimental basis for designing controlled thermal-wave propagation and guiding future multi-frequency or multi-source photothermal mixing experiments. Full article

The Random Domino Automaton—a stochastic cellular automaton forest-fire model—is formulated for the Bethe lattice geometry. The equations describing the stationary state of the system are derived using combinatorial analysis. The special choice of parameters that define the dynamics of the system leads to a solvable reduction in the set of equations. Analysis of the equations shows that by changing the parameter responsible for cluster removal, the size distribution of clusters smoothly transitions from (near) exponential to inverse power, beyond which the system is unstable. The analysis shows the crucial role of combining more than two clusters in elongating the tail of the size distribution generated by the system and, thus, in increasing the range of validity of the inverse power law. We also point out an interesting connection of the proposed model with Catalan-like integer sequences. Full article

Traditional pedestrian evacuation models struggle to balance global exit guidance with local, individual decision making under hazards. We address this by decomposing long-term objectives into Particle Swarm Optimization (PSO)-based micro-goals and proposing a hybrid Cellular Automaton (CA) and PSO model. The hybrid design reduces the decoupling between spatial discretization and individual choices and more tightly couples hazard and density fields with movement decisions. Two contributions are central. First, we develop an autonomous following pathfinding mechanism (AFPM) that linearly blends the direction toward a PSO micro-goal with a herd following direction and adds a small reward for directional consistency. This mitigates path chaos from purely autonomous moves and congestion aggregation from purely herding moves. Second, we build a multi-dimensional interpretability and robustness framework that combines the empirical Cumulative Distribution Function (CDF) and a kernel-smoothed Probability Density Function (PDF) of key evacuation times (T_clear, T_95%_alive) together with vulnerability curves, to analyze the data and assess robustness. It combines Shapley Sobol analysis to quantify parameter effects on clearance time T_clear and the 95% survival evacuation time T_95%_alive, with CDF/PDF summaries and vulnerability curves to assess anti-interference performance. Experiments use a simulated underground shopping mall. In a 60 pedestrian case, a geometry-only baseline yields T_clear $\approx 33 \mathrm{s}$; hazard- and density-aware strategies produce slightly longer T_clear but reduce peak bottleneck congestion by 20–30%. When one exit is closed, the exceedance probability at $\tau =70 \mathrm{s}$ drops from 0.44 to 0.36, reducing long tail risk. Compared with geometry-based Dijkstra, the proposed model slightly increases clearance time while lowering peak congestion by 20–30%, achieving a balance between efficiency and safety. The model and evaluation protocol provide technical support for evacuation policy, facility layout, and emergency system design in large complex buildings. Full article

The simulation of solidification microstructures of cast alloys is crucial to the integrated “process–microstructure–property” numerical simulation. In order to verify the accuracy of the solidification microstructure simulation results, the solidification microstructures of WE54 alloy under both metal mold casting (MMC) and sand mold casting (SMC) conditions were simulated using the CAFE (Cellular Automaton–Finite Element) method, and the simulation results were validated experimentally. First, the effects of microstructure simulation parameters on the results were investigated, including nucleation density (n), nucleation undercooling (ΔT), and dendrite tip growth kinetics parameters (a₂, a₃). The results showed that, with the maximum surface nucleation undercooling (ΔT_s,max) kept constant, increasing the maximum volume nucleation undercooling (ΔT_v,max) significantly increases the proportion of columnar grains in the ingot structure. Moreover, when nucleation parameters remain constant, increasing a₂ and a₃ leads to expansion of the columnar grain zone. Secondly, numerical simulations of the solidification microstructure of WE54 alloy under different solidification conditions were carried out. The results indicated that as the cooling rate increases, the grain structure of the ingot becomes significantly refined, and the proportion of columnar grains decreases notably. Based on these findings, the simulation parameters suitable for simulating the solidification process and microstructure of MMC and SMC WE54 alloy were determined. Simulations of the temperature field and solidification microstructure were performed and compared with experimental results. Full article

Safe and efficient building evacuation for heterogeneous populations, particularly individuals with disabilities, remains a critical challenge in emergency management. This study proposes a hybrid evacuation framework that integrates Floor Field Cellular Automaton (FFCA) with reinforcement learning, specifically a Deep Q-Network (DQN), to enhance adaptive decision-making in dynamic and complex environments. The model incorporates velocity heterogeneity, friction-based conflict resolution, and real-time path planning to capture diverse mobility capabilities and interactions among evacuees. Simulation experiments were conducted under varying population densities, walking speeds, and exit configurations, considering four types of occupant groups: able-bodied individuals, wheelchair users, and people with visual or hearing impairments. The results demonstrate that the DQN-enhanced model consistently outperforms the conventional SFF + DFF approach, achieving significant reductions in evacuation time, particularly under high-density and reduced-speed scenarios. Notably, the DQN dynamically adapts evacuation paths to mitigate congestion, thereby improving both system efficiency and the safety of vulnerable groups. These findings highlight the potential of combining CA-based environmental modeling with reinforcement learning to develop adaptive and inclusive evacuation strategies. The proposed framework provides practical insights for designing evacuation protocols and intelligent navigation systems in public buildings. Future work will extend the proposed FFCA + DQN framework to more complex and realistic environments, including multi-exit and multi-level buildings, and further integrate multi-agent reinforcement learning (MARL) architectures to enable decentralized adaptation among heterogeneous evacuees. Furthermore, lightweight DQN variants and distributed training schemes will be explored to enhance computational scalability, while empirical data from evacuation drills and real-world case studies will be used for model calibration and validation, thereby improving predictive accuracy and generalizability. Full article

Modern lightweight pedestrian bridges exhibit heightened susceptibility to human-induced vibration due to low natural frequencies and high flexibility. This study integrates behavioral science to explore pedestrian–structure coupling, developing a novel bidirectional biomechanical model capturing vertical/lateral movements. Body dynamics were solved iteratively. Concurrently, an agent-based cellular automata model embedded pedestrian social attributes and mutual exclusion to simulate crowd flow. Coupling these with finite element bridge analysis simulated vibration responses. Experimental validation confirms the model’s validity. This work advances a behavioral science perspective for mechanistically understanding pedestrian-induced vibration in flexible bridges, thereby contributing to strategies for mitigating vibration-induced disasters like structural damage or crowd panic. Full article

This study investigates eutectic morphology transitions in Ni-Sn alloys using Bridgman directional solidification with a transition variable speed coupled with cellular automaton (CA) simulations. Steady-state solidification (0.1–2000 μm/s) produced only regular lamellar/rod-like eutectics, while velocity jumps triggered anomalous eutectic formation. As the drawing speed increased, the lamellar spacing decreased from ~3 μm to 0.4 μm, while the microhardness increased from ~426 HV to 500 HV. The experiments on Ni-Sn alloys revealed that anomalous eutectic morphologies form specifically at velocity transition interfaces (0.1–1000 μm/s), consistent with CA simulations showing destabilization of the lamellae, epitaxial growth of the Ni₃Sn phase, and decoupled nucleation of the α-Ni phase for the formation. The work defines a processing window for anomalous eutectic formation and provides mechanistic insights bridging undercooling and directional solidification regimes. Full article

Understanding how complex biological forms emerge and evolve remains a central question in evolutionary and developmental biology. To explore this complexity, we introduce a minimal two-dimensional, cellular automaton (CA)-based model that captures key features of biological development—such as spatial growth, self-organization, and differentiation—while remaining computationally tractable and evolvable. Unlike most abstract genotype–phenotype mapping models, our approach generates emergent morphological complexity through spatially explicit rule-based interactions governed by a simple genetic vector, resulting in self-organized patterns reminiscent of biological morphogenesis. Using simulations, we show that, as observed in empirical studies, the resulting phenotypic distribution is highly skewed: simple forms are common, while complex ones are rare. The model exhibits a strongly non-linear genotype-to-phenotype mapping in such a way that small genetic changes can lead to disproportionately large morphological shifts. Notably, transitions toward complexity are less frequent than regressions to simplicity, reflecting evolutionary asymmetries observed in natural systems. We further demonstrate that, by allowing for mutations in the generative rules, our model is capable of adaptive evolution and even reproducing generic features of tumoral growth. These findings suggest that even minimal developmental rules can give rise to rich, hierarchical patterns and complex evolutionary dynamics, positioning our CA-based model as a powerful tool for investigating how developmental constraints and biases shape morphological evolution. Full article

Thin liquid film corrosion is a critical failure mechanism for the atmospheric environment and industrial infrastructure. This review systematically examines relevant methods and recent advances in characterizing and simulating this phenomenon. Various measurement methods for liquid film thickness, composition, and conductivity are investigated, with particular focus on the advantages of non-contact optical technology and X-ray fluorescence (XRF) in in situ monitoring and analysis. For corrosion simulation, the finite element method (FEM), cellular automaton (CA), and molecular dynamics (MD) are widely used. Their combination has synergistic potential in revealing essential corrosion mechanisms and establishing prediction models across scales. Full article