With the acceleration of global urbanization, airport noise pollution has emerged as a significant environmental concern that demands attention. Traditional airport noise monitoring systems are fraught with limitations, including restricted spatial coverage, inadequate real-time data acquisition capabilities, poor data correlation, and suboptimal cost-effectiveness. To address these challenges, this paper proposes an innovative airport noise perception and monitoring approach leveraging Internet of Things (IoT) technology. This method integrates multiple data streams, encompassing noise, meteorological, and ADS–B data, to achieve precise noise event tracing and deep multi-source data fusion. Furthermore, this study employs Kriging interpolation and Inverse Distance Weighting (IDW) techniques to perform spatial interpolation on data from sparse monitoring sites, thereby constructing a spatial distribution model of airport noise. The results of the practical application demonstrate that the proposed airport noise monitoring method can accurately reflect the spatiotemporal distribution patterns of airport noise and effectively correlate noise events, thereby providing robust data support for the development of airport noise control policies.

For the construction process of striking and rolling to treat the foundation, the traditional compaction test must conduct core sampling and damage detection, which are time-consuming and strenuous. Furthermore, it is difficult to determine the actual overall compaction. Based on acceleration sensor and satellite positioning measurement technology, this paper designs and develops an acceleration information acquisition device for impact construction, which can auto monitor impact construction in large-scale pavement construction. This paper also proposes methods for analyzing the degree of impact acceleration and compaction, including: (1) comprehensive analysis of the functional requirements and structural levels of a device for the acquisition of information about impact acceleration; (2) development of an acquisition system for collecting information about impact acceleration installed on the roller; (3) proposal of a method for the processing and analysis of compaction information based on the collected acceleration information; and (4) proposal of an experimental scheme of to test impact acceleration and application of the whole set of developed equipment to practical engineering.

To address the issue of safe, orderly, and efficient operation for unmanned vehicles within the apron area in the future, a hardware framework of aircraft–vehicle–airfield collaboration and a trajectory planning method for unmanned vehicles on the apron were proposed. As for the vehicle–airfield perspective, a collaboration mechanism between flight support tasks and unmanned vehicle departure movement was constructed. As for the latter, a control mechanism was established for the right-of-way control of the apron. With the goal of reducing waiting time downstream of the pre-selected path, a multi-agent reinforcement learning model with a collaborative graph was created to accomplish path selection among various origin–destination pairs. Then, we took Apron NO.2 in Ezhou Huahu Airport as an example for simulation verification. The results show that, compared with traditional methods, the proposed method improves the average vehicle speed and reduces average vehicle queue time by 11.60% and 32.34%, respectively. The right-of-way signal-switching actions are associated with the path selection behavior of the corresponding agent, fitting the created aircraft–vehicle collaboration. After 10 episodes of training, the Q-values can steadily converge, with the deviation rate decreasing from 40% to below 0.22%, making the balance between sociality and competitiveness. A single trajectory can be planned in just 0.78 s, and for each second of training, 7.54 s of future movement of vehicles can be planned in the simulation world. Future research could focus on online rolling trajectory planning for UGSVs in the apron area, and realistic verification under multi-sensor networks can further advance the application of unmanned vehicles in apron operations.

The high-pressure rotary jet pile construction method has been widely used in airport projects, particularly in regions with soft soil foundations, such as coastal areas. The method’s primary aim is to establish a sound foundation bearing capacity. However, the conventional machinery used in constructing high-pressure rotary jet piles lacks automation. To achieve real-time quality control in high-pressure rotary jet pile construction, the development of high-pressure rotary jet pile construction data acquisition and monitoring equipment is made possible by integrating key technologies such as the global navigation satellite system (GNSS), the Internet of Things and massive data processing. The acquisition and storage of crucial construction parameters, such as the drilling position, drill rod verticality, drilled hole depth, grouting pressure, grouting lifting speed and grouting volume for high-pressure rotary jet piles is successfully implemented in this paper. A large amount of actual construction data is collected through an on-site application, and the construction quality of the high-pressure rotary jet pile machine is monitored and analyzed using geographic information system (GIS) technology. Actual applications demonstrate that the proposed digital monitoring model and method for rotary jet pile construction quality control is viable, improves the on-site management level and efficiency and has significant future value.

Driven by the increasing global population and rapid urbanization, aircraft noise pollution has emerged as a significant environmental challenge, impeding the sustainable development of the aviation industry. Traditional noise prediction methods are limited by incomplete datasets, insufficient spatiotemporal consistency, and poor adaptability to complex meteorological conditions, making it difficult to achieve precise noise management. To address these limitations, this study proposes a novel noise prediction framework based on a hybrid Convolutional Neural Network–Bidirectional Long Short-Term Memory–Attention (CNN–BiLSTM–Attention) model. By integrating multi-source data, including meteorological parameters (e.g., temperature, humidity, wind speed) and aircraft trajectory data (e.g., altitude, longitude, latitude), the framework achieves high-precision prediction of aircraft noise. The Haversine formula and inverse distance weighting (IDW) interpolation are employed to effectively supplement missing data, while spatiotemporal alignment techniques ensure data consistency. The CNN–BiLSTM–Attention model leverages the spatial feature extraction capabilities of CNNs, the bidirectional temporal sequence processing capabilities of BiLSTMs, and the context-enhancing properties of the attention mechanism to capture the spatiotemporal characteristics of noise. The experimental results indicate that the model’s predicted mean value of 68.66 closely approximates the actual value of 68.16, with a minimal difference of 0.5 and a mean absolute error of 0.89%. Notably, the error remained below 2% in 91.4% of the prediction rounds. Furthermore, ablation studies revealed that the complete CNN–BiLSTM–AM model significantly outperformed single-structure models. The incorporation of the attention mechanism was found to markedly enhance both the accuracy and generalization capability of the model. These findings highlight the model’s robust performance and reliability in predicting aviation noise. This study provides a scientific basis for effective aviation noise management and offers an innovative solution for addressing noise prediction problems under data-scarce conditions.

With the continuous elevation of aviation safety standards, accurate monitoring of ground support vehicles in aircraft stand areas has become a critical task for enhancing overall aircraft stand operational safety. Given the limitations of existing surface movement radar and multi-camera surveillance systems in terms of cost, deployment complexity, and coverage, this paper proposes a lightweight vision-based framework for vehicle state perception and spatiotemporal behavior analysis oriented toward aircraft stand safety. Leveraging existing fixed monocular monitoring resources in the stand area, the framework first establishes a precise mapping from image pixel coordinates to the physical plane through self-calibration and homography transformation utilizing scene line features, thereby achieving unified spatial measurement of vehicle targets. Subsequently, it integrates an improved lightweight YOLO detector (incorporating Ghost modules and CBAM for noise suppression) with the ByteTrack tracking algorithm to enable stable extraction of vehicle trajectories under complex occlusion conditions. Finally, by combining functional zone division within the stand, a semantic map is constructed, and a behavior analysis method based on a spatiotemporal finite state machine is proposed. This method performs joint reasoning by fusing multi-dimensional constraints including position, zone, and time, enabling automatic detection of abnormal behaviors such as “intrusion into restricted areas” and “abnormal stop.” Quantitative evaluations demonstrate the framework’s efficacy: it achieves an average physical localization error (RMSE) of 0.32 m, and the improved detection model reaches an accuracy (mAP@50) of 90.4% for ground support vehicles. In tests simulating typical violation scenarios, the system achieved high recall (96.0%) and precision (95.8%) rates in detecting ‘area intrusion’ and ‘abnormal stop’ violations, respectively. These results, achieved using only existing surveillance cameras, validate its potential as a cost-effective and easily deployable tool to augment existing safety monitoring systems for airport ground operations.