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The construction sector is undergoing a rapid transition toward more resilient, sustainable, and digitally connected systems, creating increasing demand for materials capable of providing functions beyond conventional structural performance. In this context, smart materials have emerged as promising solutions due to their ability to respond to mechanical, thermal, chemical, or electromagnetic stimuli through adaptive behaviors such as self-healing, structural sensing, energy regulation, vibration control, and reversible deformation. Despite growing scientific interest, available knowledge remains fragmented across specific material families and isolated application domains. Therefore, this study presents a PRISMA-based systematic review of smart materials in construction using peer-reviewed journal literature indexed in Scopus during the 2021–2026 period. The review examines the principal smart material families currently applied in construction, including self-healing concretes, self-sensing cementitious systems, Shape Memory Alloys (SMA), piezoelectric materials, phase change materials, adaptive coatings, conductive nanocomposites, and multifunctional geopolymers. Their engineering functions, structural and architectural applications, reported performance characteristics, sustainability contributions, digital integration potential, and implementation barriers are comparatively discussed and qualitatively synthesized based on the reviewed literature. The findings indicate that smart materials can improve durability, structural health monitoring, seismic resilience, thermal efficiency, lifecycle performance, and carbon reduction when properly integrated into buildings and infrastructure. However, large-scale adoption remains constrained by high initial costs, manufacturing scalability, regulatory uncertainty, long-term durability validation, and limited market confidence. The review further shows that the greatest future potential lies in combining material intelligence with IoT platforms, artificial intelligence, BIM environments, and digital twins. Overall, smart materials are positioned as strategic enablers of next-generation low-carbon, adaptive, and intelligent construction systems. Full article

Smart Mobility plays a key role in Smart Cities, given its ability to support the rollout of intelligent transport systems, allowing for more sustainable urban transportation and greater interoperability across diverse mobility modes. Furthermore, Smart Mobility is essential to maximize the quality of life for the community while advancing principles of sustainability, economic development, technological innovation, and collaborative governance. Real-Time Traffic Management (RTTM) emerges as a vital technology for optimizing traffic management in Smart Mobility. Using the PRISMA framework, the proposed systematic literature review examines 165 peer-reviewed publications related to RTTM research work published between 2019 and 2025. This review identified eleven application domains, with Urban Traffic Management Systems (36.97%) and Artificial Intelligence (AI) and Predictive Analytics (12.73%) representing the most prominent areas. A retrospective analysis of the literature on control architecture used in closed-loop feedback systems indicates that most studies (89%) have adopted a more dynamic control model, while 7.8% adopted a Digital Twin (DT)-based approach. However, several implementation barriers persist, including limited integration of online optimization and learning loops into RTTM systems, gaps in performance comparisons between simulation and reality, scalability issues due to heterogeneous environments, inconsistent data quality caused by various sensor types, and difficulties integrating sensors into a control system. In addition, this paper proposes a taxonomy of RTTM applications and control architectures, while outlining key practical barriers to implementation and charting future research directions for advancing Smart Mobility through robust RTTM. Full article

Greenhouse microclimate regulation is challenging due to nonlinear coupling among temperature, humidity, soil moisture, and light intensity, which limits the effectiveness of conventional threshold-based and PID control strategies under time-varying environmental disturbances. This paper presents a forecast-augmented ensemble control framework that combines Random Forest, Gradient Boosting, and Support Vector Machine classifiers with one-hour-ahead weather forecasts for closed-loop greenhouse microclimate regulation. The proposed system was deployed and validated in a working greenhouse cultivating cucumber (cv. ‘Madora F1’) over 28 consecutive days. Sensor measurements and forecast inputs were processed through a unified preprocessing pipeline, while control actions were generated through majority voting and executed on Raspberry Pi 4B edge hardware with a worst-case inference latency below 18 ms. The proposed framework achieved a temperature RMSE of 0.83 °C during field deployment. For reference, RMSE values of 3.21 °C and 1.94 °C were obtained for the threshold-based and PID baseline controllers, respectively, under the adopted disturbance-consistent evaluation protocol. Compliance rates reached 96.4% for temperature, 94.1% for relative humidity, and 97.2% for soil moisture across 40,320 resampled observation intervals (60 s analysis grid) derived from the original 10 s acquisition stream. Integration of short-term weather forecasts enabled anticipatory irrigation management, reducing irrigation pump operation by 18% without compromising soil-moisture compliance and yielding an estimated annual energy saving of 158 kWh per greenhouse zone. Unlike prediction-oriented greenhouse artificial-intelligence studies, the proposed approach implements a deployable forecast-augmented closed-loop control architecture validated under continuous real-world greenhouse operation. Full article

In embodied-intelligence Industrial Internet of Things (IIoT), multi-AGV intelligent warehousing requires continuous processing of latency-sensitive tasks, such as environmental perception, inventory monitoring, and anomaly detection. Due to limited onboard computing capability and energy capacity, purely local execution can hardly satisfy real-time requirements, whereas fully cloud-based processing may incur excessive transmission delay and backhaul overhead. To address this issue, this paper investigates the joint optimization of AGV service-point migration and task offloading under a cloud-edge-end collaborative architecture. Considering the impact of service-point selection on wireless access, MEC resources, movement delay, and energy consumption, as well as the effect of offloading decisions on transmission, computation, and AGV-side energy cost, a dual-time-scale optimization model is formulated to minimize the long-term accumulated system delay while satisfying task latency and AGV energy constraints. To solve the resulting mixed discrete problem, a DPSO-MAPPO algorithm is proposed, where DPSO searches service-point plans satisfying movement and conflict constraints at the slow time scale, and MAPPO learns coordinated multi-AGV offloading policies at the fast time scale. The delay and energy feedback further enables coordination between the two types of decisions. Simulation results show that the proposed algorithm converges stably, reduces system delay by 13.55% compared with benchmark algorithms, and improves total energy consumption and energy-violation control. Full article

Full-duplex acoustic telemetry is important for real-time bidirectional measurement and control in intelligent coiled-tubing operations, but its reliability in deep horizontal wells is limited by long-range dispersion, asymmetric flow-induced noise, and severe near-end self-interference. This study proposes an orthogonal frequency-band planning and synergistic interference suppression method for full-duplex acoustic communication in coiled tubing. A dispersion model and an asymmetric attenuation model were first established for a fluid-filled coiled-tubing cylindrical-shell waveguide to characterize the physical transmission constraints. A multiphysics multi-objective cost function was then formulated by considering dispersion flatness, channel attenuation, asymmetric noise adaptability, and spectral isolation, and an improved simulated annealing algorithm was used to optimize the uplink and downlink frequency bands. In addition, a three-stage suppression architecture integrating mechanical decoupling, physical-layer frequency isolation, and CEEMDAN–wavelet denoising was developed to reduce self-interference and residual nonstationary noise. Full-scale experiments on a 457.2 m coiled-tubing surface circulation system showed that the proposed method improved the output signal-to-interference-plus-noise ratio from −15 dB to 18.5 dB and maintained a bit error rate below 1.2 × 10⁻⁴ at 400 L/min. These results indicate that the proposed approach can enhance the robustness of full-duplex acoustic telemetry under strong flow-induced noise. Full article

This study presents an intelligent framework for the analysis of multidimensional geomechanical states in underground mining systems based on numerical simulation and machine learning methods. A three-dimensional geomechanical model of the Zholymbet deposit was developed in the RS3 environment using the generalized Hoek–Brown failure criterion. Numerical simulations were performed for representative mining scenarios characterized by complex excavation interaction and stress redistribution. The modelling results were transformed into a multidimensional geomechanical dataset containing stress, deformation, displacement, and yielding parameters. Principal component analysis (PCA) was applied to investigate the internal structure of the geomechanical state space and identify dominant patterns controlling the rock mass behavior. Clustering analysis revealed several geomechanical regimes corresponding to stable, transitional, and instability-prone conditions. Isolation Forest anomaly detection demonstrated that atypical geomechanical states are not randomly distributed but spatially localized near excavation systems and mining horizons. The obtained results indicate that hazardous geomechanical conditions are governed by complex interactions between stress concentration, deformation intensity, yielding processes, and excavation geometry. The proposed approach provides a basis for intelligent interpretation of large-scale numerical modelling results and may support geomechanical risk assessment in underground mining operations. Full article

Industrial quality control is a critical component of modern manufacturing, as defects can lead to significant economic losses and safety risks. Traditional inspection methods, largely reliant on human operators or rule-based systems, often suffer from inconsistency, limited scalability, and reduced accuracy in complex environments. Recent advances in artificial intelligence (AI), particularly in deep learning and computer vision, have enabled automated defect detection and classification with unprecedented performance. This paper provides a comprehensive review of AI-based image processing techniques for industrial quality control, covering classification, detection, and segmentation approaches. Key applications across manufacturing sectors are discussed, alongside current challenges such as data scarcity, real-time implementation, and model generalisation. Furthermore, this paper explores emerging trends toward autonomous inspection systems, integrating real-time analytics, edge computing, and intelligent decision making. The insights presented aim to guide future research toward robust, scalable, and fully automated quality control solutions in smart manufacturing environments. Full article

To address the critical safety challenges of flooding induced by ship–ice collisions in Arctic shipping routes, this study proposes an Observe–Orient–Predict–Decide–Act (OODA-P)-enhanced closed-loop intelligent damage control decision-support framework integrated with predictive simulation. To address the limitations of existing systems—namely, weak polar adaptability and the absence of a decision feedback loop—this research presents three core findings: (1) A fast time-domain floating condition model was developed by coupling topside icing with progressive flooding. Numerical simulations indicate that neglecting ice accretion leads to an underestimation of the long-term heel angle and transverse stability by 4.4% and 4.5%, respectively, validating the necessity of incorporating coupled ice loads. (2) A serial dual-channel prediction and evaluation mechanism, integrating “situation evolution prediction” and “decision efficacy evaluation,” was designed. This mechanism can proactively forecast long-term deterioration trends in the floating condition within 0.3147 s of acquiring damage information, capable of identifying and flagging potentially high-risk emergency plans before their execution, thus preventing adverse outcomes. (3) The proposed framework was validated through typical polar scenarios and 111 damage control training sessions across three batches, with the full-loop logic flow completing in under 3 s. Compared with the traditional OODA loop, the average emergency response time was reduced from 26.9 to 22.7 min (a 15.5% reduction), while the initial response success rate improved from 74.7% to 97.3% in a simulated training environment. By enabling “virtual trial-and-error” prior to execution, this framework demonstrates the potential to augment traditional experience-based damage control with proactive, simulation-driven decision support, marking a step towards more intelligent interventions. Through the explicit coupling of topside icing and progressive flooding into real-time predictions, this work provides a foundation for further development of polar-adaptable intelligent damage control systems. Full article

As the core actuation component of intelligent precision spraying systems, the variable-rate nozzle is essential for achieving on-demand agricultural spraying; improving the use efficiency of water, fertilizers and pesticides; and reducing environmental pollution. This paper systematically reviews the development of agricultural variable-rate nozzles, from early mechanical profiling structures to modern intelligent control technologies based on Pulse Width Modulation (PWM). First, the existing variable-rate nozzles are classified into three major categories: electromagnetic-integrated type, centrifugal type, and variable-diameter type. A comparative analysis is conducted from three dimensions of working principle, performance characteristics and application scenarios, to delineate the respective advantages and limitations of each nozzle category. Second, the paper examines key technological advances in three areas: high-frequency solenoid valves, PWM control, and pressure and flow stabilization. It identifies the nonlinear response of solenoid valves, flow distortion under low duty cycles, and water hammer pressure fluctuation induced by high-speed switching as the three core technical bottlenecks at the current stage. Subsequently, the latest achievements and typical methodologies of variable-rate nozzles in structural design, simulation and experimental analysis are systematically reviewed, and their application performance in scenarios including field crops, orchards, protected agriculture and beyond are summarized. Finally, the remaining open issues in this field are put forward. It is suggested that future research should focus on key breakthroughs in the development of corrosion and wear-resistant high-frequency solenoid valves, the formation mechanism and suppression methods of pressure fluctuation, as well as adaptive algorithms based on machine learning or Model Predictive Control (MPC), to promote the leapfrog development of agricultural variable-rate nozzle technology from single variable control to multi-factor coupling optimization. All references cited in this paper are from articles published after the year 2000. Among them, the literature published in the last decade accounts for 86.6%, and literature published in the last five years accounts for 58.9%. Full article

Short-term traffic forecasting is a critical component of intelligent transportation systems. While deep learning architectures for this task have proliferated rapidly, the sensor-level data characteristics—zero-value prevalence, distributional heterogeneity, and cross-sensor correlation structure—that drive architecture-specific failure modes remain insufficiently understood, and their implications for evidence-based model selection in real deployments have not been systematically addressed. This study addresses that question through a sensor-network diagnostic framework applied to the METR-LA dataset (Metropolitan Los Angeles; 207 inductive loop detectors, 5-min resolution). The framework integrates systematic characterization of sensor data properties, a controlled benchmark of four representative architectures—Transformer, Spatio-Temporal Graph Convolutional Network (STGCN), Diffusion Convolutional Recurrent Neural Network (DCRNN), and Gated Temporal Convolutional Network (Gated TCN)—under a unified 12→3 prediction setting, and a novel per-sensor regression analysis that quantitatively links zero-value ratios to model-specific prediction errors across all 207 sensors. Building on these findings, this study further proposes Graph-Enhanced Transformer (GETFormer), a lightweight hybrid architecture that augments the Transformer with a single-hop Graph Convolutional Network (GCN) layer and a gated residual fusion module. The diagnostic findings and condition-dependent model-selection guidelines provide an empirically grounded foundation for principled hybrid architecture development in urban traffic sensing. Full article

As tunnel engineering continues to advance toward deeper, longer, and more complex projects, the risks encountered during the construction phase have evolved into a combination of various disaster types and the accumulation of multiple contributing factors. Traditional empirical and semi-empirical risk management methods are increasingly revealing shortcomings in terms of timeliness, accuracy, and the ability to process multi-source data. In recent years, driven by advancements in computing power and sensor technology, artificial intelligence algorithms (AI algorithms) such as machine learning and deep learning have been rapidly adopted in tunnel construction risk management. This paper retrieved relevant literature from the Web of Science database covering the period from 2010 to 2025. After rigorous screening, 96 highly relevant papers were selected for bibliometric analysis. This paper systematically reviews research progress from two perspectives: algorithmic models and engineering applications. The review indicates that, in terms of algorithmic models, traditional machine learning, convolutional neural network, recurrent neural network, generative adversarial network, Transformer, and graph neural network constitute a multi-level technical framework encompassing feature representation, risk perception, and intelligent decision-making. In terms of applications, AI algorithms have been widely integrated into typical scenarios such as geological hazard identification and prediction, surrounding rock stability and deformation prediction, rock burst assessment and early warning, lining defect detection and structural safety assessment, construction-induced ground settlement prediction, and tunnel gas and fire hazard prediction, significantly enhancing risk identification and early warning capabilities. However, several challenges remain, including the scarcity of high-quality datasets, the prevalence of noisy, incomplete, and heterogeneous monitoring data, insufficient coupling between model interpretability and engineering mechanisms, limited cross-project transferability, and the lack of integrated management systems for multi-hazard lifecycle control. Based on this, this paper proposes future research directions in areas such as data infrastructure development, integration of mechanism constraints, and multi-hazard collaborative modeling, aiming to provide guidance for the further development of intelligent risk management in tunnel construction. Full article

Autonomous ship maneuvering in harbor environments is increasingly supported by advances in model predictive control, reinforcement learning, digital twins, multi-sensor fusion, berth allocation, and multi-agent coordination. However, these developments are often studied as separate technological domains, while real harbor autonomy requires coordinated operation across vessels, port infrastructure, regulatory systems, cybersecurity mechanisms, and human supervisory processes. This study presents an architecture-oriented critical review of autonomous ship and harbor maneuvering research published between 2015 and May 2026. The review synthesizes literature from control engineering, maritime artificial intelligence, sensor fusion, digital twins, port logistics, cyber-physical systems, regulation, cybersecurity, and human–AI supervision. The analysis introduces two conceptual contributions: a layered cyber-physical taxonomy and an integration maturity model. The taxonomy organizes autonomous harbor maneuvering into seven interdependent layers: physical dynamics, perception and sensor fusion, prediction and state estimation, control, decision and coordination, digital twin federation, and regulatory–supervisory governance. The maturity model distinguishes isolated vessel autonomy, assisted coordination, shared digital synchronization, agent-based coordination, and fully federated maritime cyber-physical autonomy. The reviewed evidence shows substantial progress in individual layers, especially control, perception, digital twins, and berth allocation. However, major gaps remain in cross-layer synchronization, semantic interoperability, regulation-aware decision-making, cybersecurity integration, and validated ship–shore federation. To address these gaps, this study proposes a Federated Maritime Cyber-Physical Architecture for autonomous harbor maneuvering. The architecture integrates vessel autonomy cores, port intelligence cores, semantic federation middleware, agent-based negotiation, regulatory verification, cybersecurity safeguards, and human supervisory interfaces. This review argues that future progress in autonomous harbor operations depends not only on stronger algorithms, but on interoperable, explainable, regulation-aware, and cyber-resilient ship–shore ecosystems. Full article

Robotic belt grinding is an effective and widely adopted finishing method for superalloys, offering notable advantages such as high material removal capability, low heat input, and reduced workpiece damage. In addition, robots can readily integrate multiple sensors—such as infrared radiation cameras, force sensors, and high-speed cameras—which facilitate real-time monitoring of the grinding process and thereby enhance grinding quality control. With the establishment and continuous advancement of large-scale artificial intelligence (AI) data models, new breakthroughs have emerged in the optimization of robotic grinding processes. Owing to its dexterous workspace and advantages in high flexibility and cost-effectiveness, robotic belt grinding has become a critical process for the precision forming of complex curved components such as aero-engine blades and blisks. However, factors such as the limited absolute accuracy of industrial robots, time-varying grinding contact states, and significant transient boundary effects make it difficult for the current constant-parameter open-loop machining mode to simultaneously meet the demands for high material removal efficiency and high surface integrity on complex profiles. This paper systematically reviews the technologies for precision control and process optimization of robotic belt grinding aimed at pointwise precise material removal. First, the structural composition of the robotic belt grinding system and the material removal mechanism are analyzed. Then, centered on the compensation concept, a hierarchical progressive technical framework is outlined, covering geometric calibration compensation, force/position hybrid online compensation, transient entry boundary compensation, and system-level comprehensive compensation of multi-source errors, with a comparison of the applicable scenarios and the effects on shape and property control at each level. Furthermore, under the support of effective compensation, the collaborative optimization methods of material removal modeling, multi-objective optimization of process parameters, force-constrained trajectory planning, and intelligent adaptive processes are elaborated. Finally, current technical bottlenecks are summarized, and future trends in next-generation adaptive grinding technology driven by digital twins and embodied intelligence are envisioned. This review aims to provide a systematic theoretical reference for the high-precision and intelligent upgrading of robotic precision grinding systems. Full article

Heterogeneous hydrogels capable of complex, programmable deformation are highly desirable for soft actuators, yet general strategies that simultaneously impart structural anisotropy, rapid responsiveness, and mechanical robustness remain limited. Here, a gradient anisotropic natural rubber-poly(N-isopropylacrylamide) (NR-PNIPAM) composite hydrogel is developed through a simple one-pot polymerization strategy by coupling pH-regulated colloidal stability with gravity-directed redistribution of natural rubber latex particles. Under an optimized pH window, NR nanoparticles gradually migrate during gelation and are fixed as a continuous gradient within the PNIPAM network, generating built-in structural asymmetry for nonuniform deformation. Meanwhile, NR nanoparticles act as soft reinforcing domains to improve mechanical strength, while water-soluble graphene nanosheets provide efficient photothermal conversion for remotely-controlled near-infrared (NIR)-responsive actuation. Benefiting from this synergistic design, the hydrogel exhibits programmable bending and localized folding with high actuation rates of 129° s⁻¹ and 46° s⁻¹, respectively, along with a tensile strength of 0.32 MPa and an active lifting capability exceeding 70 times its own weight. The material further enables biomimetic gripping and lifting under NIR stimulation. This work establishes a general route to robust gradient hydrogels by integrating colloidal regulation, structural anisotropy, and photothermal actuation, offering a versatile platform for high-performance soft intelligent systems. Full article

Chitosan, produced through deacetylation of chitin from crustacean byproducts and, increasingly, fungal biomass and insects, is attracting food-sector interest because it combines antimicrobial activity, antioxidant capacity, biodegradability, and film-forming behavior in a single polymer. This review discusses how source, molecular weight (MW), degree of deacetylation, solubility, and charge density shape its performance in food systems. The paper then follows the main technological routes now tested or used: edible films and coatings, hydrogels, cryogels, nanoparticles, microcapsules, and hybrid matrices. These formats can protect fresh produce, meat, poultry, fish, seafood, and dairy foods, while also supporting beverage clarification, emulsion control, release of natural antimicrobials or antioxidants, and freshness monitoring in active or intelligent packaging. The evidence indicates strong promise, especially where microbial growth, lipid oxidation, moisture transfer, and short shelf life remain limiting factors. Yet, wider industrial use is still slowed by water sensitivity, sensory effects, raw-material variation, cost, process scale-up, and regulatory alignment. Future work should move beyond laboratory efficacy and address reproducible production, food-specific validation, and consumer acceptance. Full article