The attempt to integrate the applications of conventional structural deformation reconstruction strategies and vibration-based damage identification methods is made in this study, where, more specifically, the inverse finite element method (iFEM) and pseudo-excitation approach (PE) are combined for the first time, to give rise to a novel structural health monitoring (SHM) framework showing various advantages, particularly in aspects of enhanced adaptability and robustness. As the key component of the method, the inverse finite element method (iFEM) enables precise reconstruction of vibration displacements based on measured dynamic strains, which, as compared to displacement measurement, is much more adaptable to existing on-board SHM systems in engineering practice. The PE, on the other hand, is applied subsequently, relying on the reconstructed displacements for the identification of structural damage. Delamination zones in a carbon fibre reinforced plastic (CFRP) laminate are identified using the developed method. As demonstrated by the damage detection results, the iFEM-PE method possesses apparently improved accuracy and significantly enhanced noise immunity compared to the original PE approach depending on displacement measurement. Extensive parametric study is conducted to discuss the influence of a variety of factors on the effectiveness and accuracy of damage identification, including the influence of damage size and position, measurement density, sensor layout, vibration frequency and noise level. It is found that different factors are highly correlated and thus should be considered comprehensively to achieve optimal detection results. The application of the iFEM-PE method is extended to better adapt to the structural operational state, where multiple groups of vibration responses within a wide frequency band are used. Hybrid data fusion is applied to process the damage index (DI) constructed based on the multiple responses, leading to detection results capable of indicating delamination positions precisely.

Under low-load cycles (LLCs), the issue of high NOx emissions from diesel engines is attracting widespread attention. Through a combination of experimental and simulation approaches, the NOx emission behavior under LLC conditions was investigated. Furthermore, the optimization strategies for reducing NOx emissions was studied based on a dual selective catalytic reduction (SCR) after-treatment system. The results indicate that emissions at load rates below 30% during LLCs account for more than 67.5% of the total cycle emissions, particularly under idling and start-stop conditions. Moreover, it was found that NOx emissions decrease significantly by using a pre-positioned dual SCR after-treatment system. And, the closer the SCR is to the engine, the higher the NOx conversion efficiency becomes. As the SCR’s position is adjusted, the rate of NOx removal stabilizes, achieving a maximum reduction in NOx concentration of up to 60.1%.

Off-road machinery is one of the significant contributors to air pollution due to its large quantity. In this study, a deep learning model was developed to predict the transient engine emissions of CO, NO, NO₂, and NO_x, which are the main pollutants emitted by off-road machinery. A portable emission measurement system (PEMS) was used to measure the exhaust emission features of four types of construction machinery. The raw PEMS data were preprocessed using data compensation, local linear regression, and normalization to ensure that the data could handle transient conditions. The proposed model utilizes the preprocessing PEMS data to estimate the CO, NO, NO₂, and NO_x emissions from off-road machinery using a recurrent neural network (RNN) based on a long short-term memory (LSTM) model. The experimental results show that the proposed method can effectively predict the emissions from off-road construction machinery under transient conditions and can be applied to controlling the emissions from off-road construction machinery.

Global warming is directly related to heavy-duty vehicle fuel consumption and greenhouse gas (CO₂ mainly) emissions, which, in China, are certified on the vehicle chassis dynamometer. Currently, vast amounts of vehicle real-road data from the portable emission measurement system (PEMS) and remote monitoring are being collected worldwide. In this study, a binning-reconstruction calculation model is proposed, to predict the chassis dynamometer fuel consumption and CO₂ emissions with real-road data, regardless of operating conditions. The model is validated against chassis dynamometer and PEMS test results, and remote monitoring data. Furthermore, based on the proposed model, the fuel consumption levels of 1408 heavy-duty vehicles in China are analyzed, to evaluate the challenge to meet the upcoming China fourth stage fuel consumption limits. For accumulated fuel consumption based on the on-board diagnostic (OBD) data stream, a predictive relative error less than 5% is expected for the present model. For bag sampling results, the proposed model’s accuracy is expected to be within 10%. The average relative errors between the average fuel consumption and the China fourth stage limits are about 3%, 8%, and 0.7%, for current trucks, tractors, and dump trucks, respectively. The urban operating condition, with lower vehicle speeds, is the main challenge for fuel consumption optimization.

At present, most of the damage detection techniques based on global vibration and local guided wave have obvious limitations, which brings difficulties to the safety assessment of structures. To solve this problem, a damage location identification method based on micro-dynamic balance and inverse finite element is proposed. Firstly, the vibration frequency, measuring point density and damage magnitude were determined by using finite simulation composite plate structure. Secondly, a fiber grating sensor is attached to the surface of the laminate to receive the strain response signal of the structure under steady-state vibration, and the global displacement of the structure is constructed by inverse finite element method. Finally, the inverse finite element listing is introduced into the theoretical framework of the dynamic response of the micro-element, and the structural damage identification experiment based on the measured strain data is realized. The experimental results show that this method can effectively identify the structural damage area and has a special sensitivity to structural damage, and the identification accuracy and efficiency are high.