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Microwave heating, which is used for pre-treatment of concrete before it is comminuted, stands as a strong candidate for selective liberation of multiphase materials like concrete. This paper is concerned with the selective liberation of concrete's raw constituents (particularly aggregate) for recycling by considering the water content of concrete as a parameter of microwave heating for the first time. The deterioration law of the concrete's performance was characterized by the variation in the splitting tensile strength and relative dynamic modulus after heating by microwave at different water contents. Besides, tests were conducted to evaluate the performance of the interface transition zone (ITZ) between aggregate and mortar as well as to investigate the reasons for the stripping behavior of aggregate-mortar, which included the interface tensile strength test, temperature measurement, and porosity test. The deterioration law of splitting tensile strength and relative dynamic modulus revealed that the performance of concrete was subject to different degrees of damage depending on the water content. Furthermore, experimental results showed that interface bonding strength between aggregate and mortar was dramatically impaired, and a large temperature difference was generated between the aggregate and mortar during microwave heating. Meanwhile, the permeable pores increased considerably even when the specimens were dried. In the presence of water, the intactness of ITZ between aggregate and mortar was destroyed by microwave heating, and its performance was significantly lowered, which led to the occurrence of stripping behavior between aggregate and mortar. This was reaffirmed by the microstructure presented by scanning electron microscopy. Thus, the newly developed microwave pre-treatment improved by providing appropriate water contents for concrete corresponding to different strength grades is a promising method for recycling aggregate from waste concrete.

With the rapid advancement of Internet of Things (IoT) technology, its applications in road infrastructure have garnered attention. However, challenges persist when applying IoT to road infrastructure monitoring, including insufficient durability of front-end sensors, pavement damage due to sensor embedding, and the redundancy of a vast amount of real-time data, hindering the long-term real-time monitoring of pavements. To address these challenges, this study developed a self-powered distributed intelligent pavement monitoring system based on IoT, encompassing a sensor network, cloud platform, communication network, and power supply system. Considering the specific characteristics of slipform paving for cement concrete pavements, an integrated paving process was proposed, merging embedded sensors with pavement material structures. Through on-site engineering monitoring, the system actively collects and analyzes various data types such as system energy consumption, temperature and humidity, environmental noise, wind speed and direction, and pavement structural vibrations, providing data support for pavement design, maintenance, and vehicle-road synergy applications. Future efforts will continue to promote the application of IoT technology in digital road maintenance, traffic safety, and optimized pavement material structure design.

Road smoothness not only directly affects the safety and comfort of vehicle travel but also relates to the efficiency and cost-effectiveness of road maintenance. Traditional road smoothness detection methods usually require professional equipment and personnel, leading to high costs and cumbersome operations. Therefore, finding a low-cost, simple, and accurate method for detecting road smoothness is of great significance. This study uses vehicle-mounted acceleration sensors to detect road smoothness, establishing a correlation between driving vibration acceleration data and the international roughness index (IRI). For this research, a driving vibration acceleration data acquisition device was developed, and the driving acceleration data from the test sections were denoised and their feature values extracted. The pseudo-vibration velocity range was used as the characteristic index representing the road surface smoothness IRI value. Testing with different vehicle types showed that the method is applicable to both sedans and SUV models, yielding a relative error of 8.9% for the sedan smoothness test model and 6.7% for the SUV smoothness test model. This study contributes to conducting large-scale road smoothness detection at a low cost, improving the efficiency of road maintenance and operations.

The road is one of the most important civil infrastructures for serving society, where its service quality and life have direct impacts on the safety and comfort of users. Therefore, road construction, condition detection and monitoring, and timely maintenance are particularly important for engineers. Many engineering applications of industrial informatics approaches, like image processing technology, widely used computer-based algorithms, and advanced sensors, have been initially and gradually applied to roads. This state-of-the-art review first summarized the research on industrial applications of advanced information technologies in recent years, while analyzing and comparing the advantages and disadvantages of each technology. Especially, five topics were focused on road construction, road maintenance with decision strategy, road structure evaluation, smart sensing in the road, and cooperative vehicle infrastructure system. It is expected that advanced industrial informatics can help engineers promote the development of smart, safe, and sustainable roads.

As urbanization accelerates, the increasing number of vehicles and travel demands contribute to escalating road traffic noise pollution. Although passive noise control techniques such as noise barriers and green belts effectively mitigate noise, they occupy urban space, exacerbating the scarcity and high cost of already congested city areas. Emerging as a novel noise reduction strategy, active noise control (ANC) eliminates the need for physical isolation structures and addresses the noise within specific frequency ranges more effectively. This paper investigates the characteristics of urban road traffic noise and develops an ANC prototype. Utilizing the Least Mean Squares (LMS) algorithm, we conduct active noise control tests for various types of single- and dual-frequency noise within the prototype’s universal platform to validate its actual noise reduction capabilities. The study demonstrates that urban road traffic noise is mostly in the mid- to low-frequency range (below 2000 Hz). The developed ANC prototype significantly reduces single- or dual-frequency noise within this range, achieving a maximum noise reduction of nearly 30 dB(A). Future research should expand noise reduction tests across more frequency bands and assess the noise reduction effectiveness against real road traffic noise.

Pavement is an important part of transportation infrastructure. In order to maintain pavement before the damage and improve the service quality, it is necessary to develop an intelligent and durable pavement information monitoring system. However, the pavement dynamic response monitoring is highly costly, easily obsolete and statistically redundant. The emergence of the Internet of Things (IoT) technology promises to change that. In this paper, an architecture of a distributed road IoT monitoring system is proposed, which has an acquisition layer, a preprocessing layer, a processing layer, an interaction layer, an energy layer and a network layer. Then, a prototype wireless pavement vibration monitoring system based on the IoT is developed, which consists of a number of wireless sensing nodes, a gateway, a remote server and a browser. Finally, data preprocessing, wireless communication, time synchronization, data processing and visualization, which represent the key to an effective system, are tested and discussed. The prototype wireless pavement vibration monitoring system provides a viable scheme for upgrading the IoT system and its application in the road infrastructures. In the future, any smart road will have an IoT wireless monitoring system to monitor the traffic, environment, and pavement information, which help enable traffic guidance, signal control, danger warning, scientific maintenance decision-making.

Microbiologically induced concrete corrosion (MICC) is a major reason for sewer pipe replacement and rehabilitation. Predicting the depth of concrete corrosion can help make better decisions on the inspection and rehabilitation of pipes. Due to the limited data available, the models using deep learning methods for predicting MICC are prone to overfitting. This study develops an XGBoost-based MICC model with the benefits of hyperparametric auto-optimization. For this purpose, the factors affecting MICC were sorted out to be used as a guide for selecting attributes when building the database. 379 datasets relevant to the corrosion loss of the origin Portland cement-based materials in sewers were collected from literature to establish a database. It is then randomly partitioned into 8:2 training and test datasets. The hyperparameters were adjusted by performing Bayesian optimization for 200 iterations. On the training and test sets, the R2 scores of the trained model are 0.87 and 0.85, respectively. In addition, an ongoing field trail in a sewer well is introduced to test the generalization ability of the model. By deploying the model on the field inspection data, the predicted corrosion loss after 169 days is 1.6 mm, which ranges between 1.5 and 3.5 mm measured. The developed MICC model is anticipated to perform well when fed future inspection data that is richer and more abundant.

In modern transportation, pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians. Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users. Therefore, monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance, which in turn ensures public transportation safety. Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions. Advanced technologies can be employed for the collection and analysis of such data, including various intrusive sensing techniques, image processing techniques, and machine learning methods. This review summarizes the state-of-the-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.

Pavement vibration monitoring under vehicle loads can be used to acquire traffic information and assess the health of pavement structures, which contributes to smart road construction. However, the effectiveness of monitoring is closely related to sensor performance. In order to select the suitable acceleration sensor for pavement vibration monitoring, a printed circuit board (PCB) with three MEMS (micro-electromechanical) accelerometer chips (VS1002, MS9001, and ADXL355) is developed in this paper, and the circuit design and software development of the PCB are completed. The experimental design and comparative testing of the sensing performance of the three MEMS accelerometer chips, in terms of sensitivity, linearity, noise, resolution, frequency response, and temperature drift, were conducted. The results show that the dynamic and static calibration methods of the sensitivity test had similar results. The influence of gravitational acceleration should be considered when selecting the range of the accelerometer to avoid the phenomenon of over-range. The VS1002 has the highest sensitivity and resolution under 3.3 V standard voltage supply, as well as the best overall performance. The ADXL355 is virtually temperature-independent in the temperature range from −20 °C to 60 °C, while the voltage reference values output by the VS1002 and MS9001 vary linearly with temperature. This research contributes to the development of acceleration sensors with high precision and long life for pavement vibration monitoring.

External load is one of the important reasons for structural damage and failure of reinforced concrete sewage pipelines, causing pipe leaks, pipe explosions, and even road collapses. In this paper, three-point loading experiments on full-size reinforced concrete pipes were carried out, and the damage state of the pipes was monitored by acoustic emission technology; the evolution trend of the mechanical properties and acoustic emission monitoring indexes under load was investigated. The experimental results showed that: (1) According to the change of acoustic emission energy and accumulated energy during loading, the mechanical response of the pipeline can be divided into an elastic compression phase, a plastic damage phase, and a residual strength phase; (2) The accumulated acoustic emission energy (∑E) and the maximum value of a single acoustic emission energy (Emax) can effectively characterize the different damage states of the loaded pipe; (3) A “double-peak” was observed in AF/RA data within the loading process. The appearance of the two peaks corresponds to the change of the loading phase of the pipeline and the occurrence of the major damage. Thus, the AF/RA index can effectively characterize the loading state and the damage degree of the pipeline. This study provides a valuable reference for pipeline health monitoring by using AE technology.

Rollpave pavement, as a rollable prefabricated asphalt pavement technology, can effectively reduce the overall road closure time required for pavement construction and maintenance. Sensors can be integrated into Rollpave pavement, thereby avoiding sensor damage that may otherwise result from high temperatures and compactive forces during the rolling process, as well as pavement structural damage resulting from cutting and drilling. However, the embedment of sensors into Rollpave pavement still presents certain challenges, namely poor interfacial synergy between the embedded sensor and the asphalt mixture. To solve this problem, three-point bending tests and dynamic response FEM simulations were used to optimize the embedded sensor’s packaging. The influence of sensor embedment on Rollpave pavement under different working conditions was analyzed. Results of these analyses show that low temperature and the epoxy resin negatively affect the bending performance of specimens, and that packaging with cylindrical shape, flat design, and consisting of a material with modulus similar to that of the asphalt mixture should be preferred. This study is conducive to improve the intellectual level and service life of road infrastructure.

The wave velocity of acoustic emission (AE) can reflect the properties of materials, the types of AE sources and the propagation characteristics of AE in materials. At the same time, the wave velocity of AE is also an important parameter in source location calculation by the time-difference method. In this paper, a new AE wave velocity measurement method, the arbitrary wave (AW) method, is proposed and designed to measure the AE wave velocity of an asphalt mixture. This method is compared with the pencil lead break (PLB) method and the automatic sensor test (AST) method. Through comparison and analysis, as a new wave velocity measurement method of AE, the AW method shows the following advantages: A continuous AE signal with small attenuation, no crosstalk and a fixed waveform can be obtained by the AW method, which is more advantageous to distinguish the first arrival time of the acoustic wave and calculate the wave velocity of AE more accurately; the AE signal measured by the AW method has the characteristics of a high frequency and large amplitude, which is easy to distinguish from the noise signal with the characteristics of a low frequency and small amplitude; and the dispersion of the AE wave velocity measured by the AW method is smaller, which is more suitable for the measurement of the AE wave velocity of an asphalt mixture.

Natural vibration characteristics serve as one of the crucial references for bridge monitoring. However, temperature-induced changes in the natural vibration characteristics of bridge structures may exceed the impact of structural damage, thus causing some interference in damage identification. This study analyzed the influence of temperature on the natural vibration characteristics of simply supported beams, which is the most widely used bridge structure. The theoretical formula for the variation of the natural frequency of simply supported beams with temperature was proposed. The elastic modulus of simply supported beams in the range of −40 °C to 60 °C was acquired by means of the falling ball test and the theoretical formula and was compared with the elastic modulus obtained by the three-point bending test at room temperature (20 °C). In addition, the Midas/Civil finite-element simulation was carried out for the natural frequency of simply supported beams at different temperatures. The results showed that temperature was the main factor causing the variation of the natural frequency of simply supported beams. The linear negative correlation between the natural frequency of simply supported beams and their temperature were observed. The natural frequency of simply supported beams decreased by 0.148% for every 1 °C increase. This research contributed to the further understanding of the natural vibration characteristics of simply supported beams under the influence of temperature so as to provide references for natural frequency monitoring and damage identification of beam bridges.

Road power generation technology is of significance for constructing smart roads. With a high electromechanical conversion rate and high bearing capacity, the stack piezoelectric transducer is one of the most used structures in road energy harvesting to convert mechanical energy into electrical energy. To further improve the energy generation efficiency of this type of piezoelectric energy harvester (PEH), this study theoretically and experimentally investigated the influences of connection mode, number of stack layers, ratio of height to cross-sectional area and number of units on the power generation performance. Two types of PEHs were designed and verified using a laboratory accelerated pavement testing system. The findings of this study can guide the structural optimization of PEHs to meet different purposes of sensing or energy harvesting.

Traditional road-embedded monitoring systems for traffic monitoring have the disadvantages of a short life, high energy consumption and data redundancy, resulting in insufficient durability and high cost. In order to improve the durability and efficiency of the road-embedded monitoring system, a pavement vibration monitoring system is developed based on the Internet of things (IoT). The system includes multi-acceleration sensing nodes, a gateway, and a cloud platform. The key design principles and technologies of each part of the system are proposed, which provides valuable experience for the application of IoT monitoring technology in road infrastructures. Characterized by low power consumption, distributed computing, and high extensibility properties, the pavement vibration IoT monitoring system can realize the monitoring, transmission, and analysis of pavement vibration signal, and acquires the real-time traffic information. This road-embedded system improves the intellectual capacity of road infrastructure and is conducive to the construction of a new generation of smart roads.

A traditional penetration test only measures the total penetration within 5 s. The penetration process is not monitored, and therefore, a large amount of information on the deformation properties of asphalt is not used. This paper documents a study to use a high-speed camera to quantify the entire penetration process and use the Finite Element Method (FEM) to interpret the penetration process using a viscoelastic model. The penetration–time relationships of several asphalt binders (70#, 90#, a rubber modified binder, and a styrene-butadiene-styrene (SBS) modified binder) have been acquired using the new method, and the FEM modeling of the penetration processes is performed. The results show that both stress relaxation and creep appear during the penetration process. The results indicate that the improved test method and its data interpretation procedure may better characterize the properties of asphalt binder, which may extend the applications of the traditional penetration test.

Dynamic response of pavement provides service condition information and helps with damage prediction, while limited research is available with the simulation of pavement vibration response for evaluating roadway service condition. This paper presents a numerical model for the analysis of the pavement vibration due to the dynamic load created by a passing vehicle. A quarter vehicle model was used for the determination of the vehicle moving load. Both random and spatial characteristics of the load were considered. The random nonuniform moving load was then introduced in a 3D finite element model for the determination of the traffic-induced pavement vibration. The validated numerical model was used to assess the effects of dynamic load, material properties, and pavement structures on pavement vibration response. Numerical analyses showed that the vibration modes changed considerably for the different roadway service conditions. The vibration signals reflect the level of road roughness, the stiffness of the pavement materials, and the integrity of pavement structure. The acceleration extrema, the time-domain signal waveform, the frequency distribution, and the sum of squares of Fourier amplitude can be potential indexes for evaluating roadway service condition. This provides recommendations for the application of pavement vibration response in early-warning and timely maintenance of road.

Green and sustainable power supply for sensors in pavement monitoring system has attracted attentions of civil engineers recently. In this paper, the piezoelectric energy harvesting technology is used to provide the power for the acceleration sensor and Radio Frequency (RF) communication. The developed piezoelectric bimorph cantilever beam is used for collecting the vibrational energy. The energy collection circuit is used to charge the battery, where the power can achieve 1.68 mW and can meet the power need of acceleration sensor for data collection and transmission in one operation cycle, that is, 32.8 seconds. Based on the piezoelectric-cantilever-beam powered sensor, the preliminary study on the IoT-based pavement monitoring platform is suggested, which provides a new applicable approach for civil infrastructure health monitoring.

In this paper, an acceleration sensing node for pavement vibration was developed to monitor traffic information, including vehicle speed, vehicle types, and traffic flow, where a hardware design with low energy consumption and node encapsulation could be accomplished. The service performance of the sensing node was evaluated, by methods including waterproof test, compression test, sensing performance analysis, and comparison test. The results demonstrate that the sensing node is low in energy consumption, high in strength, IPX8 waterproof, and high in sensitivity and resolution. These characteristics can be applied to practical road environments. Two sensing nodes were spaced apart in the direction of travelling. In the experiment, three types of vehicles passed by the monitoring points at several different speeds and values of d (the distance between the sensor and the nearest tire center line). Based on cross-correlation with kernel pre-smoothing, a calculation method was applied to process the raw data. New algorithms for traffic flow, speed, and axle length were proposed. Finally, the effects of vehicle speed, vehicle weight, and d value on acceleration amplitude were statistically evaluated. It was found that the acceleration sensing node can be used for traffic flow, vehicle speed, and other types of monitoring.

In the previous studies on the dynamic responses of pavement, most researchers paid attention to the stress and strain responses of pavement. With the passings of vehicles, road structures will inevitably produce vibration accelerations. Since acceleration is closely related to many physical quantities according to the definitions in physics, the acceleration of pavement can reflect pavement conditions from the side, and the relevant studies on the acceleration field of pavement under moving load are worth conducting. In this study, with the help of ABAQUS, the numerical analysis on the distribution and response of the acceleration field of pavement under moving load was carried out, and the influencing degrees of vehicle load, vehicle speed and the elastic modulus of upper layer on the acceleration response of intact pavement were also compared based on the designed orthogonal test. It was found that the three-directional accelerations of pavement present symmetrical distributions around the loaded area, and the vertical acceleration is the largest but decays rapidly with the form of quartic polynomials as far away from the loaded area. Meanwhile, the mechanical state of pavement can be evaluated by analyzing pavement accelerations. Besides, the acceleration response of the intact pavement in this study is the most sensitive to the influencing factor of vehicle speed, followed by vehicle load, and the brought effect of the elastic modulus of upper layer is the lowest in general. [ABSTRACT FROM AUTHOR]

Dynamic response analysis of pavement helps with structure and material optimization. The vehicle loading has a significant effect on the pavement dynamic response, which is usually assumed to be concentrated load, uniform load, or harmonic load without considering the space-time characteristics. In this study, a validated quarter vehicle model and finite element road model were developed to obtain the random non-uniform load. The random non-uniform load integrates the randomness characteristics due to the vehicle-road interaction and spatial characteristics caused by the tire pattern. The impacts of the vehicle weight, speed, and road roughness on the vehicle dynamic load were analyzed. The effect of random non-uniform load on pavement dynamic response was investigated by comparing the pavement responses under constant non-uniform load and constant uniform load. Numerical analyses show that the stress extremum along the transverse and longitudinal sections can reflect the time-space characteristics of the loads. The damage of the surface layer is underestimated when the vehicle load is simplified to be a constant uniform load without considering the random and spatial characteristics. [ABSTRACT FROM AUTHOR]