Resurfacing Research Articles

An Artificial Neural Network approach to assess road roughness using smartphone-based crowdsourcing data

Monitoring road surface conditions is a crucial task for road authorities to develop effective infrastructure maintenance programs. Despite smartphones have been introduced as cost-effective and real-time solution for this purpose, several challenges must be addressed before their real-world application. This study investigates the utilization of smartphone-based crowdsourcing data and Artificial Neural Networks (ANN) to enhance the precision of road surface condition estimation. Initially, data are collected from four different smartphone models mounted in various vehicles, including vertical acceleration, geographic location, and speed. The root mean square of the vertical acceleration data, along with vehicle speed, is then employed as input features for the ANN, while the true International Roughness Index (IRI) values serve as the corresponding output features. Comparative analysis between ANN and regression models based on statistical metrics such as Mean Squared Error (MSE) and Pearson correlation revealed that ANN outperforms regression models. The obtained MSE and Pearson correlation values for ANN (0.56 and 0.91) surpass those of regression models (0.72 and 0.88). Moreover, results indicated that utilizing crowdsourcing smartphone data yielded superior outcomes compared to using a single smartphone for this purpose.

Hubungan Penilaian Kerusakan Jalan Metode Bina Marga dan Light Weight Deflectometer (LWD)

The Tegal Siwalan-Banyuanyar Highway in Probolinggo Regency has been damaged at several points and has not received any maintenance for the last 7 years. This has a huge impact on road users because it can interfere with driving comfort and safety, so it is necessary to analyze the road damage that has occurred, including the type of damage and an assessment of road conditions. The Bina Marga method and a light-weight deflectometer (LWD) can be used to assess road deflection in order to assess road condition. The results of this study obtained priority order values for each segment that were in the range 5-11. The greater the value of the Order of Priority obtained, the better the road condition, whereas the smaller the value of the Order of Priority obtained, the worse the road condition. It was also found that the Bina Marga method and the Light Weight Deflectometer (LWD) tool had a poor correlation, so a comparison between the two could not be made for assessing the condition of the road surface layer.

Design of a Fiber Temperature and Strain Sensor Model Using a Fiber Bragg Grating to Monitor Road Surface Conditions

In this paper, the types and principles of operation of fiber sensors based on fiber Bragg gratings (FBGs) are investigated. The influence of strain and temperature on the characteristics of FBGs is considered, and a method for the simultaneous measurement of these parameters is presented. Laboratory studies were carried out in the temperature range from +18 °C to +135 °C with an incremental step of 5 °C, with the actual temperature not deviating by more than ±0.5 °C. From the data obtained, the Bragg wavelength–temperature relationships were plotted, which showed a linear increase in wavelength with increasing temperature. This study shows that the use of two FBGs with a different sensitivity to temperature and strain allowed for the simultaneous measurement of both parameters. Numerical models created in the MATLAB R2022b environment confirmed the high accuracy and precision of the measurements. The FBG-based sensors demonstrated a robust performance in harsh environments, withstanding temperatures of up to 160 °C and high humidity, making them applicable in various industries and sciences. This work confirms that FBGs are a promising tool for accurate temperature and strain measurements, providing reliable results in harsh environments.

Comprehensive Assessment of Context-Adaptive Street Lighting: Technical Aspects, Economic Insights, and Measurements from Large-Scale, Long-Term Implementations.

This paper addresses the growing importance of efficient street lighting management, driven by rising electricity costs and the need for municipalities to implement cost-effective solutions. Central to this study is the UNI 11248 Italian regulation, which extends the European EN 13201-1 standard introduced in 2016. These standards provide guidelines for designing, installing, operating, and maintaining lighting systems in pedestrian and vehicular traffic areas. Specifically, the UNI 11248 standard introduces the possibility to dynamically adjust light intensity through two alternative operating modes: (a) Traffic Adaptive Installation (TAI), which dims the light based solely on real-time traffic flow measurements; and (b) Full Adaptive Installation (FAI), which, in addition to traffic measurements, also requires evaluating road surface luminance and meteorological conditions. In this paper, we first present the general architecture and operation of an FAI-enabled lighting infrastructure, which relies on environmental sensors and a heterogeneous wireless communication network to connect intelligent, remotely controlled streetlights. Subsequently, we examine large-scale, in-field FAI infrastructures deployed in Vietnam and Italy as case studies, providing substantial measurement data. The paper offers insights into the measured energy consumption of these infrastructures, comparing them to that of conventional light-control strategies used in traditional installations. The measurements demonstrate the superiority of FAI as the most efficient solution.

Road adhesion coefficient Estimation: Physics-informed deep learning method with vehicle dynamics model

Road adhesion coefficient (RAC) is not only a significant basis for trajectory planning and decision-making of autonomous vehicles but also a critical parameter to determine the control potential of autonomous vehicles under complex operating conditions. Accurate, timely, and reliable estimation of RAC facilitates many function units of autonomous vehicles and improves driving safety. To address the issues of low accuracy and transparency of RAC estimation method based on road surface image recognition, a vehicle dynamics model-informed deep learning (VDMIDL) method for RAC estimation is proposed in this work. The physics information related to vehicle dynamics is integrated into a CNN-based deep learning model via the fusion of vehicle physical features and high-dimensional road surface image features to realize accurate estimation of RAC. The results of the model training show that the proposed VDMIDL model for RAC estimation possesses higher prediction accuracy compared with the conventional deep learning (CDL) model. The vehicle test results indicate that the proposed VDMIDL model can provide more prospective and precise RAC for trajectory planning, decision-making, and control of the vehicle in response to sudden variations in road conditions. The VDMIDL model is applicable to a wide range of road conditions. RAC estimated by the VDMIDL model is accurate and stable under poor road condition in particular. The proposed fusion estimation model incorporates more physics information into deep learning model, endowing more interpretability and transparency to the black-box model.

Performance Characterization of the Field Aged Asphalt Pavement Materials at Mortar Scale: Testing Method Optimization and Rheology Analysis

Performance evaluation at the asphalt mortar scale offers increased sensitivity to aging and is less affected by extraction processes comparated to asphalt mixture. Despite these benefits, there is currently a lack of standardized methods for directly processing mortar samples obtained from field mixtures. This study aims to improve and validate a manufacturing procedure for aged mortar specimens derived from field aging cores, and to evaluate the field damage level of asphalt paving materials at the mortar scale. A novel approach was developed that incorporates warm sieving, compaction, coring, and cutting to manufacture mortar specimens suitable for Dynamic Shear Rheometer (DSR) tests. This study selected mortar specimens with different service ages and conducted rheological tests, including frequency sweep tests and modified Multiple Stress Creep Recovery (MSCR) tests, to assess their rheological performance. And the significance test of all the indcies were conducted to select suitable indcies.The results show the proposed aged mortar manufacture approach can prepare eligible mortar specimens for rheology tests. The rheology test results show that several indices can distinguish different mortar types and service ages, and the indices of surface layer mortar follow a linear relationship with service age. The linear viscoelastic zone (LVE), representative shear modulus (G0), and unrecovered creep compliance at 1.6kPa (Jnr1.6) and 3.2kPa (Jnr3.2) are identified as effective indices for evaluating the behavior of field aged mortar because of their effectiveness, lower variability, and better distinction.

Water retention and heat storage characteristics of phase change hydrogel in cooling pavement

Phase change hydrogel (PCH) combines the heat storage characteristics of phase change material with the water retention capacity of hydrogel, showing great potential in cooling pavement applications. This study aims to investigate the water retention and heat storage properties of PCH in cooling pavement, providing new insights for mitigating the urban heat island effect. Based on the principle of osmotic pressure, polyethylene glycol (PEG) solution was actively absorbed by superabsorbent polymers (SAP) to form stable PCH. By infusing the asphalt mixture skeleton with PCH-containing phase change grouting material (PCGM), a temperature-regulating phase change grouting pavement (PCGP) can be obtained. Morphological characterization showed that PCH was uniformly distributed in PCGM and effectively locked PEG during high temperature and water absorption/release processes without leakage. PCH imparted excellent water retention properties to PCGM, with a saturation water absorption rate of 44.1 %, which is 6.2 times higher than the control group, and a water retention rate of 34.31 % after heating at 60 °C for 12 hours. Furthermore, PCGM exhibited considerable heat storage performance, with a melting enthalpy of 15.74 J/g and a crystallization enthalpy of 12.07 J/g. Based on these, PCGP demonstrated outstanding temperature-regulating ability, reducing surface temperatures by 9.3 °C and 11.6 °C under dry and saturated conditions, respectively, compared to control pavement. In conclusion, this study provides a theoretical basis for the development of novel cooling pavement materials and shows great potential for its wide application in urban infrastructure construction.

Investigating the evolution of road surface descriptors according to observation angles using a database of the reflection properties of urban materials

For road lighting design, road surface descriptors like the degree of lightness Q0 and specular factor S1 are derived from reflection properties (or Bidirectional Reflectance Distribution Function (BRDF)) of road surfaces. However, these BRDF are based on outdated road surface characteristics and are also partial (they are called r-tables) because only the motorist’s 1° observation angle is considered. Thus, the need to have full BRDF measurements of current road surfaces available. This paper proposes to use a recently released open source database of measured whole BRDF by means of our in-lab gonioreflectometer to investigate the performance of Q0 and S1 according to observation angles. We first compare descriptors obtained from measured and interpolated r-tables and find a good accuracy, especially for Q0. Then we propose a model for these descriptors function of the observation angle, and find quite an accurate fitting. Finally, we suggest a methodology for the classification of road surfaces which equally considers Q0, S1 and the observation angles, and propose a tentative classification of road surfaces based, not in terms of their materials, but rather in terms of their usage-driven performance.

Research on Electric Heating Green Snow Melting and Anti-icing Technology for Tunnel, Bridge, and Culvert Entrances

This paper addresses the issue of electric heating snow melting at tunnel, bridge, and culvert entrances. It explores a novel carpet-style electric heating snow melting and anti-icing system for these areas. The study analyzes the shortcomings of existing electric heating snow melting methods and proposes effective measures for improvement. To accurately assess the characteristics of current snow melting techniques, the research considers the damage caused by traditional snow melting methods to road structures and subsequent maintenance issues, while emphasizing energy-saving and environmentally friendly technologies. The snow melting process was simulated using Star-CCM+. The results show that the new carpet-style electric heating elements significantly improve snow melting efficiency. Preheating the road surface before snow accumulation greatly reduces snow retention time, enabling timely snow and ice removal. Typically, electric heating elements in snow melting systems are installed beneath the road surface at tunnel entrances. After installation, to protect the elements and ensure smoothness, a layer of cement or another suitable material is poured over them. However, these heating elements require regular inspection or replacement, which necessitates breaking up the road surface, increasing maintenance costs, and disrupting normal traffic operations. This study overcomes these challenges by providing significant convenience for installation and maintenance work, reducing costs, and minimizing the impact on traffic operations.

Deep learning in driverless: Research results, issues, challenges

As computing and communication technologies continue to evolve, leading technology companies are investing significant resources in self-driving car techniques. With the use of deep learning in driverless driving, driverless cars are enabling them to sense, make decisions and control in complex environments, leading to truly autonomous driving. Deep learning is used for environment perception, which enables driverless cars to understand their surroundings by recognizing information such as road surfaces, pedestrians and vehicles, and also used for decision formulation, by predicting the behavior of other vehicles, pedestrians and identifying the environment around the road conditions to control the driverless car to make the optimal driving decision. But in order to achieve fully autonomous driving, there are still some technical aspects. Therefore, we deep learning in the existing driverless technology is reviewed, mainly discussing the application in the direction of driverless implementation and safety inspection. Through reading a large amount of literature, we discuss the problems that hinder the development of the driverless field, and at the same time, how to solve the problems in the future to promote the breakthrough of the driverless field. The application of deep learning in driverless not only enhances the intelligence level of driverless cars, but also prepares for the commercial application of driverless cars in the future. It enables driverless cars to better identify and adapt to complex traffic environments. Finally, realizing truly autonomous driving.

Vehicle Stability Control Based on Vehicle Motion State and Tire Force Estimation

The direct yaw moment control system is able to greatly enhance the vehicle stability when driving on challenging road surfaces. This paper introduces a direct control approach for managing the vehicle yaw moment, utilizing terminal sliding mode control and a Square Root Cubature Kalman Filter (SRCKF). Initially, the longitudinal and lateral forces acting on the vehicle's tires are estimated through a sliding mode observer. Subsequently, the SRCKF algorithm and the four-wheel tire force are employed to accurately estimate the yaw rate and sideslip angle. Based on these estimations, additional yaw moments are determined using the terminal sliding mode control algorithm, and a combined control of yaw rate and sideslip angle is achieved through a threshold method. A rule-based braking force distribution strategy is then implemented to ensure vehicle stability control. Simulation results demonstrate that the tire force estimations from the sliding mode observer and the vehicle yaw rate and sideslip angle estimations from the SRCKF algorithm closely align with the output results from Carsim, with an error margin of less than 15%. The braking force distribution strategy, based on the direct yaw moment control using the terminal sliding mode algorithm, effectively tracks the desired yaw rate and sideslip angle.

Evaluation of the attenuation mechanism of the noise reduction performance of polyurethane porous elastic road surface

The polyurethane porous elastic road surface (PERS) demonstrates superior noise reduction performance, primarily due to its porous structure that facilitates effective sound absorption and its high elasticity that contributes to substantial vibration attenuation. However, it is susceptible to void clogging and long-term aging during service, leading to a gradual decline in noise reduction performance. To investigate the attenuation characteristics of noise reduction performance in PERS mixtures under void clogging and long-term aging conditions, this study investigated PERS mixtures subjected to various clogging cycles and aging times. Concurrently, the tire vertical drop test and standing wave tube absorption coefficient test were employed to analyze the variation rule in the noise sound pressure level (SPL) and sound absorption coefficient frequency curves under the above conditions. The results show that the noise reduction performance of the PERS mixture exhibits a variation rule of first decreasing and then increasing with the increase of the clogging cycles, and the required clogging amount to reach the void saturation state is 30–40 g. Compared to before clogging, the PERS mixture after clogging has an enhanced ability to absorb low-frequency noise and a weakened ability to absorb high-frequency noise; the sizes of the cloggings have a higher degree of attenuation in low-frequency sound absorption than in high-frequency. As long-term aging progresses, the noise reduction performance of the PERS mixture first increases and then decreases. Notably, long-term aging predominantly impacts the high-frequency noise reduction capabilities of the PERS mixture. The impact of void clogging on the noise reduction performance of PERS mixtures is found to be more pronounced than that of long-term aging.

Characterisation of the Rigid Body Vibration Spectra of Road Transport Vehicles

ABSTRACTFor decades, numerous attempts at re‐creating realistic (vertical) vibrations that purport to represent typical conditions during road transport have been proposed and published. This has and continues to result in a significant number of target power density spectra (PDS) for use in laboratory simulation of vibrations despite the fact that the underlying parameters that influence the vibrations (road surface unevenness and vehicle dynamics) remain largely unchanged. This paper seeks to address this situation by analysing published and measured PDS (129 in total) from a variety of vehicles and road types with the aim of establishing typical PDS. Through the analysis of the frequency of the first natural mode of these PDS, it was found that there exist five typical response PDS (three for steel leaf suspension and two for air ride suspension) that can be considered as representative vibration response PDS for road transport in general. These representative PDS were matched with a two degree‐of‐freedom quarter‐car model representing the heave response of the rigid body motion of the vehicles. The analysis suggests that the higher frequency content of the measured data is not related to rigid body motion but emanates from drivetrain and, in some cases, structural vibrations. These usually comprise numerous harmonics of fixed and varying frequencies as well as transients. As these vibrations are impossible to classify based on vehicle type, it is recommended that the five quarter‐car representative spectral models proposed in this paper—namely, for steel leaf spring suspended vehicles with heavy, moderate and light loads and air ride type vehicles with heavy and light loads—be used for laboratory simulation for the purpose of evaluating the vibration resistance of products and packaged systems. It is suggested that, for most packaged product, this is sufficient to test their ability to survive road transport vibrations. These will yield more realistic vibrations than those endorsed by standards organisations and should have a positive impact on the optimisation of packaging designs and a corresponding reduction in packaging waste. Finally, the need for further work aimed at developing methods to identify and extract fixed and varying discrete frequency vibration as well as transient vibrations was identified.

A dual-driven fusion model of evaluating the performance and carbon emissions for recycled waste pavement

Solid waste recycling is an essential approach for the sustainable transition of transportation infrastructure development. In this study, a holistic model for recycled waste pavement was developed, achieving a breakthrough in eco-efficiency-based pavement material design. Using this model, we can not only individually assess the technical feasibility of the pavement material and its carbon emissions, but also realize the unified dimensional quantification of multidimensional parameters based on decision-making expectations using a metrics fusion system, thus achieving high-level sustainability decisions for pavement schemes. The proposed model was validated by evaluating the comprehensive properties of steel slag pavements to determine a suitable and durable pavement solution. This study provides a decarbonization strategy for the transportation sector considering waste-recycled pavement design, which may promote the development of more resilient transportation infrastructure and significantly contribute to achieving carbon neutrality and mitigating climate change.•A model for solid waste pavement was established based on synthesized assessing feasibility and carbon emissions.•The field performance and climate system impacts of pavements with solid waste materials were assessed.•It was demonstrated that recycling steel slag for pavement construction can reduce carbon emissions by >50 %.•Our results contribute to achieving sustainable transportation infrastructure systems to mitigate climate issues.•Our methodology can be used not only for road construction but also in the civil engineering field.

Innovative Pavement Materials: Utilizing Corn Stover and Fly Ash in Geopolymers

The development of each nation is evaluated by its infrastructure, and each nation is competing with the others in infrastructure advancement, especially in the construction of roadways, since they play a vital role in the economic and social development of the nation. The conventional materials used for road construction are concrete and asphalt, which pose significant environmental challenges. This research gives insight into the potential of fly ash (FA) and corn stover (CS) in synthesizing geopolymer, as an alternative material for the construction of roads. This study examines the impact of three FA and CS mixture percentages and the particle size of CS on the compressive strength and porosity of geopolymer. The results indicate that incorporating larger amounts of CS in fly ash-based geopolymer may decrease the compressive strength of the geopolymer. Smaller CS particle sizes also tend to lead to lower compressive strength. Porosity of the geopolymer tended to increase with the incorporation of higher percentages of CS, particularly for smaller corn stover sizes. As a fine aggregate replacement for geopolymer, CS incorporation has the potential to reduce mined aggregate obtained from a process that harms the environment.

Laboratorial investigation on optical, thermal and pavement performance of biomimetic dark reflective coatings with composite structure for pavement cooling

The surface temperature of asphalt pavement in summer can reach up to 70 °C, which leads to poor outdoor thermal comfort and exacerbates the urban heat island effect. Reflective coatings can effectively reduce pavement temperature, but their intrinsic light color with high reflectance causes safety issues such as glaring. In this study, based on the biomimetic idea of chameleon skin structure, composite pavement coatings for cooling and anti-glare were developed. Their optical properties, thermal behavior, adhesion and skid-resistance performances were investigated. The results showed that near-infrared reflectance of black and grey double-layer reflective coatings could reach 0.66 and 0.70, respectively (58 % higher than single-layer coatings). The optimal mass fraction of perylene black in the upper black/grey coating was 2.94 % and 0.74 %, respectively. The double-layer dark coatings could decrease the temperature by 14 °C at the depth of 3 cm below road surface, which achieved cooling effect close to that of white single-layer reflective coatings. The adhesion between coating and stone was generally higher than the cohesion of asphalt, therefore, opening traffic for a period of time or carrying out micro-milling before coating could improve the abrasion resistance. Due to increase in the thickness of the double-layer coating, the microscopic texture of road surface was reduced, thereby reducing skid-resistance. However, it still met the standard requirements. This study intends to provide a reference for developing pavement coatings that balance cooling and visual safety to mitigate the heat island effect, and improve the sustainability of transportation infrastructure.

Development of a Superhydrophobic Protection Mechanism and Coating Materials for Cement Concrete Surfaces.

In order to further enhance the erosion resistance of cement concrete pavement materials, this study constructed an apparent rough hydrophobic structure layer by spraying a micro-nano substrate coating on the surface layer of the cement concrete pavement. This was followed by a secondary spray of a hydroxy-silicone oil-modified epoxy resin and a low surface energy-modified substance paste, which combine to form a superhydrophobic coating. The hydrophobic mechanism of the coating was then analysed. Firstly, the effects of different types and ratios of micro-nano substrates on the apparent morphology and hydrophobic performance of the rough structure layer were explored through contact angle testing and scanning electron microscopy (SEM). Subsequently, Fourier transform infrared spectroscopy and permeation gel chromatography were employed to ascertain the optimal modification ratio, temperature, and reaction mechanism of hydroxy-silicone oil with E51 type epoxy resin. Additionally, the mechanical properties of the modified epoxy resin-low surface energy-modified substance paste were evaluated through tensile tests. Finally, the erosion resistance of the superhydrophobic coating was tested under a range of conditions, including acidic, alkaline, de-icer, UV ageing, freeze-thaw cycles and wet wheel wear. The results demonstrate that relying solely on the rough structure of the concrete surface makes it challenging to achieve superhydrophobic performance. A rough structure layer constructed with diamond micropowder and hydrophobic nano-silica is less prone to cracking and can form more "air chamber" structures on the surface, with better wear resistance and hydrophobic performance. The ring-opening reaction products that occur during the preparation of modified epoxy resin will severely affect its mechanical strength after curing. Controlling the reaction temperature and reactant ratio can effectively push the modification reaction of epoxy resin through dehydration condensation, which produces more grafted polymer. It is noteworthy that the grafted polymer content is positively correlated with the hydrophobicity of the modified epoxy resin. The superhydrophobic coating exhibited enhanced erosion resistance (based on hydrochloric acid), UV ageing resistance, abrasion resistance, and freeze-thaw damage resistance to de-icers by 19.41%, 18.36%, 43.17% and 87.47%, respectively, in comparison to the conventional silane-based surface treatment.

Al-Ghazali's Eco-Sufism for Environmental Preservation: Living Sufism at Pesantren Al-Anwar 3 of Central Java

Natural destruction is a severe problem faced by human beings up to the present. Many factors are identified, including less awareness of its preservation, no matter how much research is exposed. This study investigates how Al-Ghazali’s ecosufism theory is implemented at Pesantren Al-Anwar 3 of Central Java. This study employs a qualitative method with field research, which involves an anthropological approach to observe and analyze the implementation of al-Ghazali’s ecosufism concept regarding environmental ethics and waste management at Pesantren Al-Anwar 3 in Central Java. The research findings reveal al-Ghazali’s concept in dealing with environmental issues, including shukr (thankfulness), zuhd (asceticism), ‘uzla (seclusion) and maḥabba (love). They are practiced by Pesantren Al-Anwar 3 by creating innovative waste management systems, such as burning without emitting smoke, optimizing organic waste as worm feed, and utilizing the remaining combustion as material for paving. This shows how Al-Ghazali’s ecosufism theory is implemented in waste management and can effectively reduce the impact of natural damage.Contribution: This research contributes to overcoming natural damage by applying al-Ghazali’s ecosufism theory and combining it with ecological principles. It can be one of the effective ways to offer a sustainable ecological management model that can be widely adopted to overcome environmental problems that modern humans face today.

Compressive strength, durability, and resilient modulus of cement-treated magnetite and hematite iron ore tailings as pavement material

The extraction and processing of iron ore produce significant amounts of mine tailings, causing environmental problems that require storage in reservoirs or dams. Using these materials in construction helps minimize their adverse impacts. This study analyzed the geotechnical properties of Magnetite and Hematite iron ore tailings (MIOT, HIOT) from the Golgohar mine in Sirjan, Iran. Two IOTs were compacted using the Standard Proctor technique after being treated with 5, 7, and 9 % Portland cement. Following curing time, treated samples were tested at different stress levels for resilient modulus. Based on the results, to meet strength and durability requirements, cement-treated MIOT needs 9 % cement. Contrastingly, only 5 % of the cement for cement-treated HIOT met the criterion. The resilient moduli of untreated MIOT and HIOT materials heavily rely on the confining pressure, resulting in a minimal decrease in modulus by increasing deviatoric stress. The effect of cement on resilient modulus is more pronounced in high confining stresses than in low confining stresses in MIOT and HIOT materials. A comparison of different non-linear models showed that ‘Universal’ model is the best fit for laboratory results of cement-treated MIOT and HIOT materials, as it accounts for hardening and softening behavior.

Research on Ride Comfort Control of Air Suspension Based on Genetic Algorithm Optimized Fuzzy PID

The air suspension system’s superior variable stiffness, low vibration frequency, and resistance to road impacts significantly elevate both the comfort of vehicle occupants and the overall ride quality. By effectively controlling the air suspension system, its superior characteristics can be fully exploited to enhance the overall performance of vehicles. However, the parameter tuning process of the fuzzy PID controller for air suspension involves subjectivity and blindness, which affects the performance of the suspension system. To overcome these shortcomings, a control strategy combining genetic algorithms with fuzzy PID control is proposed. This strategy involves a genetic algorithm-optimized fuzzy PID air suspension control approach specifically targeting the fuzzy PID controller for air suspension. A 1/4 two-degree-of-freedom air suspension fuzzy PID controller is designed in MATLAB 2019a, utilizing genetic algorithms to optimize the PID parameter tuning process. The ride comfort of the fuzzy PID air suspension after tuning is then investigated. In the study of ride comfort on Class B road surfaces, the simulation and experimental results were consistent. Using a genetic algorithm to optimize a fuzzy PID-controlled air suspension resulted in reductions of the root mean square values for vertical body acceleration, suspension deflection, and wheel dynamic load by 30%, 26%, and 9%, respectively, compared to passive suspension. These reductions are further improvements over the corresponding indices controlled by the fuzzy PID alone, which decreased by 23%, 18%, and 6%, respectively. Thus, the control effect of the genetic algorithm-optimized fuzzy PID is superior to that of the fuzzy PID control. This demonstrates that the fuzzy PID control of air suspension optimized by genetic algorithms can further improve the comfort of vehicle occupants and the ride comfort of driving, providing a reference for active control of air suspension systems.