Search Results (8,264)

The integration of artificial intelligence (AI) and machine learning (ML) has revolutionised civil engineering, enhancing predictive accuracy, decision-making, and sustainability across domains such as structural health monitoring, geotechnical analysis, transportation systems, water management, and sustainable construction. This paper presents a detailed review of peer-reviewed publications from the past decade, employing bibliometric mapping and critical evaluation to analyse methodological advances, practical applications, and limitations. A novel taxonomy is introduced, classifying AI/ML approaches by civil engineering domain, learning paradigm, and adoption maturity to guide future development. Key applications include pavement condition assessment, slope stability prediction, traffic flow forecasting, smart water management, and flood forecasting, leveraging techniques such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Support Vector Machines (SVMs), and hybrid physics-informed neural networks (PINNs). The review highlights challenges, including limited high-quality datasets, absence of AI provisions in design codes, integration barriers with IoT-based infrastructure, and computational complexity. While explainable AI tools like SHAP and LIME improve interpretability, their practical feasibility in safety-critical contexts remains constrained. Ethical considerations, including bias in training datasets and regulatory compliance, are also addressed. Promising directions include federated learning for data privacy, transfer learning for data-scarce regions, digital twins, and adherence to FAIR data principles. This study underscores AI as a complementary tool, not a replacement, for traditional methods, fostering a data-driven, resilient, and sustainable built environment through interdisciplinary collaboration and transparent, explainable systems. Full article

Comprehension of spatio-temporal groundwater recharge (GWR) under climate change is imperative to enhance water resources availability and management. The main aim of this study is to examine climate change’s effects on spatio-temporal GWR. This study was done by ensembling five climate models and the physically-based WetSpass-M model to estimate GWR during baseline (1986 to 2015), mid-term (2031 to 2060), and long-term (2071 to 2100) periods for the Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. In comparison to the Identification of unit Hydrographs and Component flows from Rainfall, Evaporation, and Streamflow (IHACRES)’s baseflow and direct runoff with corresponding WetSpass-M model outputs, the statistical indices showed good performance in simulating water balance components. Projected future temperature and rainfall will likely increase dramatically compared to the baseline period for RCP4.5 and RCP8.5. In comparison to the baseline period, the annual GWR had been projected to increase by 4.28 mm for RCP4.5 for the mid-term (MidT4.5), 15.27 mm for the long-term (LongT4.5), 2.38 mm for the mid-term (MidT8.5), and 13.11 mm for the long-term for RCP8.5 (LongT8.5), respectively. The seasonal GWR findings showed an increasing pattern during winter and spring, whereas it declined in autumn and summer. The mean monthly GWR for MidT4.5, LongT4.5, MidT8.5, and LongT8.5 will increase by 0.34, 1.26, 0.18, and 1.07 mm, respectively. The watershed’s downstream areas were receiving the lowest amount of GWR, and prone to drought. Therefore, this study advocates and recommends that stakeholders participate intensively in developing and implementing climate change resilience initiatives and water resources management strategies to offset the detrimental effects in the downstream areas. Full article

Thorough investigation into Tesla valve (TV) design was conducted across a large design of experiments (DOE) consisting of four varying geometric parameters and six different Reynolds number regimes in order to develop an optimized pressure drop device utilizing machine learning (ML) methods. A non-standard TV design was geometrically parameterized, and an automation suite was created to cycle through numerous combinations of parameters. Data were collected from completed computational fluid dynamics (CFD) simulations. TV designs were tested in the restricted flow direction for overall differential pressure, and overall minimum pressure with consideration to the onset of cavitation. Qualitative observations were made on the effects of each geometric parameter on the overall valve performance, and particular parameters showed greater influence on the pressure drop compared to classically optimized parameters used in previous TV studies. The overall minimum pressure demonstrated required system pressure for a valve to be utilized such that onset to cavitation would not occur. Data were utilized to train an ML model, and an optimized geometry was selected for maximized pressure drop. Multiple optimization efforts were made to meet design pressure drop goals versus traditional diodicity metrics, and two geometries were selected to develop a final design tool for overall pressure drop component development. Future work includes experimental validation of the large dataset, as well as further validation of the design tool for use in industry. Full article

Low-level wind shear (LLWS) is a critical issue in aviation meteorology, posing serious risks to flight safety—especially at plateau airports with high elevation and complex terrain. This study investigates a convective wind shear event at Xining Airport on 29 May 2021. Multi-source observations—including the Doppler Wind Lidar (DWL), the Doppler weather radar (DWR), reanalysis datasets, and automated weather observation systems (AWOS)—were integrated to examine the event’s fine-scale structure and temporal evolution. High-resolution simulations were conducted using the Large Eddy Simulation (LES) framework within the Weather Research and Forecasting (WRF) model. Results indicate that the formation of this wind shear was jointly triggered by convective downdrafts and the gust front. A northwesterly flow with peak wind speeds of 18 m/s intruded eastward across the runway, generating multiple radial velocity couplets on the eastern side, closely associated with mesoscale convergence and divergence. A vertical shear layer developed around 700 m above ground level, and the critical wind shear during aircraft go-around was linked to two convergence zones east of the runway. The event lasted about 30 min, producing abrupt changes in wind direction and vertical velocity, potentially causing flight path deviation and landing offset. Analysis of horizontal, vertical, and glide-path wind fields reveals the spatiotemporal evolution of the wind shear and its impact on aviation safety. The WRF-LES accurately captured key features such as wind shifts, speed surges, and vertical disturbances, with strong agreement to observations. The integration of multi-source observations with WRF-LES improves the accuracy and timeliness of wind shear detection and warning, providing valuable scientific support for enhancing safety at plateau airports. Full article

Thermal bridging through cold-formed steel (CFS) studs significantly reduces the thermal performance of light gauge steel frame (LSF) wall systems, particularly in climates demanding higher thermal resistance (R-value). While thermal breaks are commonly used, they increase material costs and construction complexity. According to NCC 2022, the minimum total R-value requirement for external walls ranges between 2.8 and 3.8 m²·K/W depending on the climate zone and building class. This study therefore evaluated web-perforated steel studs as a passive strategy to enhance thermal resistance of LSF walls, analysing 120 configurations with validated 3D finite element models in Abaqus and benchmarking in THERM. The results showed that web perforations consistently improved R-values by 14 to 20%, as isotherm contours and heat flux vectors demonstrated disruption of direct heat flow through the stud, thereby mitigating thermal bridging. Although the axial compression capacity of web-perforated CFS studs decreased by 29.5%, the use of 4 mm hole-edge stiffeners restored 96.8% of the original capacity. The modified NZS 4214:2006 and ASHRAE Modified Zone methods, incorporating steel area reduction and heat flux redistribution, closely matched Abaqus predictions, with coefficients of variation (COV) below 0.009, corresponding to less than 1% relative deviation between analytical and numerical R-values. Furthermore, application of web-perforated CFS studs in five external wall systems demonstrated improved thermal resistance, ensuring compliance with NCC 2022 R-value requirements across all Australian climate zones. Overall, the findings establish web-perforated studs as an effective solution for improving the energy performance of LSF building envelopes. Full article

The Niland Moving Mud Spring, located near the southeastern margin of the Salton Sea, represents a rare and evolving geotechnical hazard. Unlike the typically stationary mud pots of the Salton Trough, this spring is a CO₂-driven mud spring that has migrated southwestward since 2016, at times exceeding 3 m per month, posing threats to critical infrastructure including rail lines, highways, and pipelines. Emergency mitigation efforts initiated in 2018, including decompression wells, containment berms, and route realignments, have since slowed and recently almost halted its movement and growth. This study integrates hydrochemical, temperature, stable isotope, and tritium data to propose a refined conceptual model of the Moving Mud Spring’s origin and migration. Temperature data from the Moving Mud Spring (26.5 °C to 28.3 °C) and elevated but non-geothermal total dissolved solids (~18,000 mg/L) suggest a shallow, thermally buffered groundwater source influenced by interaction with saline lacustrine sediments. Stable water isotope data follow an evaporative trajectory consistent with imported Colorado River water, while tritium concentrations (~5 TU) confirm a modern recharge source. These findings rule out deep geothermal or residual floodwater origins from the great “1906 flood”, and instead implicate more recent irrigation seepage or canal leakage as the primary water source. A key external forcing may be the 4.1 m drop in Salton Sea water level between 2003 and 2025, which has modified regional groundwater hydraulic head gradients. This recession likely enhanced lateral groundwater flow from the Moving Mud Spring area, potentially facilitating the migration of upwelling geothermal gases and contributing to spring movement. No faults or structural features reportedly align with the spring’s trajectory, and most major fault systems trend perpendicular to its movement. The hydrologically driven model proposed in this paper, linked to Salton Sea water level decline and correlated with the direction, rate, and timing of the spring’s migration, offers a new empirical explanation for the observed movement of the Niland Moving Mud Spring. Full article

The viscous rheological behavior of suspensions of mixtures of fumed silica nanoparticles (N20) and rod-shaped cellulose nanocrystals (NCC) were studied experimentally. The fumed silica concentration varied from 2 to 11.3 wt% and the NCC concentration varied from 0.99 to 6.73 wt%. The suspensions of pure fumed silica, pure NCC, and mixtures of N20 and NCC were non-Newtonian shear-thinning in nature. The viscosity versus shear rate data of all suspensions of pure and mixed additives could be described satisfactorily by a power-law model. The consistency and flow behavior indices of the suspensions were strongly dependent on the concentrations of both N20 and NCC. While the consistency index increased sharply with the increases in additive (N20 and NCC) concentrations, the flow behavior index generally decreased with the increases in N20 and NCC concentrations. Thus, the suspensions became more shear-thinning with the increases in N20 and NCC concentrations. The shear-thinning of suspensions was due to two different mechanisms: the orientation of rod-shaped cellulose nanocrystals in the flow direction with the increase in shear rate and the break-up of large agglomerates of fumed silica aggregates with the increase in shear rate. Full article

Flow field design in proton exchange membrane fuel cells (PEMFCs) is a critical issue, as it plays an important role in governing reactant transport dynamics and cell performance. In this work, numerical studies of a single Tesla-valve flow field were conducted. The influence of loop radius, channel angle, and channel height on the performance of PEMFCs were fully explored. Then, aiming to maximize the output current density, this study optimized the Tesla-valve flow field configuration through a framework that integrates Gaussian process modeling with a Genetic Algorithm (GA). The approach efficiently identifies the optimal geometric parameters, highlighting effective synergy between the surrogate model and intelligent evolutionary optimization for enhanced performance. Simulation results show that the current density at 0.4 V and the highest power density have been improved by more than 10% compared to the baseline design for both forward and reverse flow. The optimized Tesla valve design has been compared with conventional parallel and serpentine flow fields of the same flow area. Results show that, despite the larger pressure drop for the single channel case—which is due to the insufficient length of the serpentine channel—the Tesla-valve flow field demonstrated superior performance in other metrics, including current and power density, under both flow directions. Full article

As a core component of aero-engines, double-row ball bearings’ lubrication performance directly impacts the operational stability of the aircraft engine. However, existing under-race lubrication designs primarily rely on empirical knowledge, with insufficient understanding of the complex oil–air two-phase flow mechanisms, leading to bottlenecks in optimizing lubrication efficiency. Therefore, based on the computational fluid dynamics (CFD) method, a two-phase flow model for double-row ball bearings was established to systematically analyze the influence patterns of key parameters—including rotational speed, oil supply rate, number of under-race holes, diameter of under-race holes, and oil properties (viscosity, density)—on the distribution of the oil–air two-phase flow. The findings reveal that (1) the oil in the circumferential direction of the bearing cavity exhibits periodic distribution characteristics correlated with the number of under-race holes; (2) the self-rotation effect of balls hinders the migration of oil toward the outer raceway region, resulting in a significant reduction in the oil volume fraction within the bearing cavity; (3) compared with the single-sided oil supply configuration, the double-sided oil supply structure demonstrates superior lubrication performance. These research results provide theoretical support and reference data for the optimal design of under-race lubrication systems for double-row ball bearings. Full article

Water resources are a crucial foundation, and their importance increases in dry and semi-arid environments. Given the constraints of water resources, increasing population needs, and the processes of evaporation and infiltration, it is imperative to explore strategies to optimise rainfall, noted for its abruptness and quick accessibility. Constructing small dams is one of the most effective methods for harvesting rainwater in the Iraqi Western Desert. This will conserve water throughout the arid season. The study’s goal was to assess and enhance rainwater harvesting (RWH) performance across diverse design and management scenarios, utilising a novel water-harvesting model (WHCatch) for testing at the sub-catchment level. Rainfall data from two dams in Wadi Horan from 1990 to 2019 were included in the model. This study emphasises the advantages of modelling long-term water balances at the sub-catchment level and proposes strategies for optimising rainwater harvesting to enhance understanding of the hydrological processes inside the rainwater harvesting system. Substantial enhancements in RWH performance were attained by modifying the heights of the spillway (2 m) and the flow directions, yielding 90% and 85% increased storage for the Horan/2 dam and the Horan/3 dam, respectively. In practice, this provides guidelines for creating and implementing low-cost, minor dam modifications as well as for establishing seasonal release schedules that satisfy downstream and storage requirements. The findings are consistent with policy-level support for sustainable development goals in arid regions. Full article

Recent advances in computational tools, particularly machine learning (ML), deep learning (DL), and structure-based modeling, are transforming aptamer research by accelerating discovery and enhancing biosensor development. This review synthesizes progress in predictive algorithms that model aptamer–target interactions, guide in silico sequence optimization, and streamline design workflows for both laboratory and point-of-care diagnostic platforms. We examine how these approaches improve key aspects of aptasensor development, such as aptamer selection, sensing surface immobilization, signal transduction, and molecular architecture, which contribute to greater sensitivity, specificity, and real-time diagnostic capabilities. Particular attention is given to illuminating the technological and experimental advances in structure-switching aptamers, dual-aptamer systems, and applications in electrochemical, optical, and lateral flow platforms. We also discuss current challenges such as the standardization of datasets and interpretability of ML models and highlight future directions that will support the translation of aptamer-based biosensors into scalable, point-of-care and clinically deployable diagnostic solutions. Full article

Background: Hemiplegic cerebral palsy (HCP) is a motor dysfunction disorder resulting from perinatal developmental brain injury, predominantly impairing upper limb function in children. Nonetheless, there has been insufficient research on the brain activation patterns and inter-brain coordination mechanisms of HCP children when performing motor control tasks, especially in contrast to children with typical development(CD). Objective: This cross-sectional study employed functional near-infrared spectroscopy (fNIRS) to systematically compare the cerebral blood flow dynamics and brain network characteristics of HCP children and CD children while performing upper-limb mirror training tasks. Methods: The study ultimately included 14 HCP children and 28 CD children. fNIRS technology was utilized to record changes in oxygenated hemoglobin (HbO) signals in the bilateral prefrontal cortex (LPFC/RPFC) and motor cortex (LMC/RMC) of the subjects while they performed mirror training tasks. Generalized linear model (GLM) analysis was used to compare differences in activation intensity between HCP children and CD children in the prefrontal cortex and motor cortex. Finally, conditional Granger causality (GC) analysis was applied to construct a directed brain network model, enabling directional analysis of causal interactions between different brain regions. Results: Brain activation: HCP children showed weaker LPFC activation than CD children in the NMR task (t = −2.032, p = 0.049); enhanced LMC activation in the NML task (t = 2.202, p = 0.033); and reduced RMC activation in the MR task (t = −2.234, p = 0.031). Intragroup comparisons revealed significant differences in LMC activation between the NMR and NML tasks (M = −1.128 ± 2.764, t = −1.527, p = 0.025) and increased separation in RMC activation between the MR and ML tasks (M = −1.674 ± 2.584, t = −2.425, p = 0.031). Cortical effective connectivity: HCP group RPFC → RMC connectivity was weaker than that in CD children in the NMR/NML tasks (NMR: t = −2.491, p = 0.018; NML: t = −2.386, p = 0.023); RMC → LMC connectivity was weakened in the NMR task (t = −2.395, p = 0.022). Conclusions: This study reveals that children with HCP exhibit distinct abnormal characteristics in both cortical activation patterns and effective brain network connectivity during upper limb mirror training tasks, compared to children with CD. These characteristic alterations may reflect the neural mechanisms underlying motor control deficits in HCP children, involving deficits in prefrontal regulatory function and compensatory reorganization of the motor cortex. The identified fNIRS indicators provide new insights into understanding brain dysfunction in HCP and may offer objective evidence for research into personalized, precision-based neurorehabilitation intervention strategies. Full article

Collar structures are widely used to protect monopile foundations from scour, but their geometric obstruction hinders direct observation of the surrounding flow in physical experiments. To overcome this limitation, this study employs large-eddy simulation (LES) to investigate the flow characteristics around a monopile with collar protection. The LES model was validated against well-documented experimental data of pile-induced flow, confirming its reliability. Simulations under flat-bed and equilibrium scour conditions were conducted to evaluate the effects of the collar on time-averaged velocity, vortex dynamics, and turbulence intensity. The results show that the collar substantially weakens the upstream accelerated flow, suppresses horseshoe vortex formation, and reduces both the strength and extent of sidewall currents. Under flatbed conditions, the side-flow intensity decreases by 24.3% and the accelerated flow area is reduced by 93.3%. A counter-rotating vortex beneath the collar dissipates kinetic energy and simplifies the near-bed vortex system, thereby mitigating scour. However, the protective effect diminishes with increasing inflow velocity, with turbulence intensity rising by 159% for a 14% velocity increase. Overall, this study provides deeper insights into the protective mechanisms of collar structures, advancing the understanding of their effectiveness and limitations in monopile scour protection. Full article

Integrating Unmanned Aerial Vehicles (UAVs) into urban airspace requires a risk-aware approach to strategic flight planning and trajectory optimization, particularly for beyond-visual-line-of-sight operations. Existing regulatory frameworks impose strict restrictions and lack dynamic, trajectory-based risk assessments. This study presents a methodology to compute efficient UAV flight paths that comply with a predefined Target Level of Safety (TLS) for ground risk. An A* algorithm with an adaptive, risk-weighted cost function optimizes trajectories by balancing flight efficiency and ground risk exposure. The risk model incorporates key urban factors, including population exposure, road-traffic density and flow, sheltering effects, UAV-specific parameters, and wind conditions. The approach is validated through a large-scale simulation study using synthetic urban environments, systematically analyzing TLS compliance and the impact of UAV parameters on optimal trajectories. In a real-world case study using open urban GIS data, the method achieved a 72.2% reduction in induced ground risk compared to the direct path, while increasing the detour factor only to $1.06$ and maintaining full TLS compliance, demonstrating its practical relevance for strategic, risk-aware UAV flight planning. Full article

It is well known that temperature strongly affects the photovoltaic (PV) performance. Raising the working temperature leads to a significant decrease in PV output of the power capacity, and it also lowers power conversion efficiency. This issue is highly important for the PV systems operating in tropical climate areas such as southern Viet Nam. Developing the cooling methods applied for reducing the PV module temperature might be the solution to this problem and has attracted many researchers and industrial sectors. However, the existing research might not sufficiently address the comparative evaluation of multiple active water-based cooling methods on power conservation efficiency, power output, and cost implications under high-temperature conditions in tropical areas. This study is a case study that aims at conducting some experimental investigations for active water-based cooling methods applied to PV modules in Ho Chi Minh City, South Viet Nam. There are four active water-based cooling methods, including the spraying liquid method (SL), the dripping droplet method (DD), tube heat exchanger method (TE), and the liquid flowing on the PV surface method (LF), that have been developed and experimentally investigated. The voltage, current, temperature, and humidity of the PV cells have been automatically recorded in every one-minute interval via sensors and electronic devices. The experimental results indicate that the surface temperature, the power conversion efficiency, and the output power of PV module are developed toward the useful and positive direction with four cooling methods. In detail, the SL is the best one, in which it leads the PV temperature to reduce from 52 °C to 34–35 °C, the output power increases up to 6.3%, its power conversion efficiency improves up to 2%, while the water flow rate is at its lowest with 0.65 L/min. Similarly, LF also creates results that are similar to SL, but it needs a higher amount of cooling water, which is up to 3.27 L/min. The other methods, like DD and TE, have less power conversion efficiency compared to the SL; it improves only around 1 to 1.3%. These results might be useful for improving the benefits of PV power generation in some tropical regions and contributing to the green energy development in the world. Full article