Rapid growth and the urban development of Chinese megacity in recent decades have led to land utilization challenges and the need for over-track buildings above metro depots that are part of the subway systems in these cities. Frequent subway operations into and out of the depots, which generate excessive vibration that transmits into the over-track buildings, can have adverse effects on the buildings’ occupants in terms of feelable vibration and noise. An efficient modeling procedure is presented for estimating vibration levels within the buildings before construction to determine whether vibration mitigation is required. Models were developed and validated based on vibration measurements in low-rise and high-rise buildings over a metro depot in Shenzhen, China. Impedance models were developed that characterize vibration transmission through the primary support columns or load-bearing walls into the building floors. Inputs to the model are vibration levels at the buildings’ foundation where the model estimates the transmission through individual support structures to the upper floors. Vibration estimates at different elevations within the building were compared with the measured levels during train pass-bys under the buildings with good agreement. Based on axial wave transmission impedances for column or load-bearing wall segments and simple impedance expressions for building floors, the closed-form analytical impedance model is an efficient and cost-effective tool for estimating floor vibration before construction.

Urban societies face the challenge of working and living in environments filled with vibration caused by transportation systems. This paper conducted field measurements to obtain the characteristics of vibration transmission from soil to building foundations and within building floors. Subsequently, a prediction method was developed to anticipate building vibrations by considering the soil and structure interaction. The rigid foundation model was simplified into a foundation–soil system connected via spring damping, and the building model is based on axial wave transmission within the columns and attached floors. Building vibrations were in response to measured input vibration levels at the ground and were validated through field measurements. The influence of different building heights on soil and structure vibration propagation was studied. The results showed that the predicted vibrations match well with the measured vibrations. The proposed prediction model can reasonably predict the building vibration caused by train operations. The closed-form method is an efficient tool for predicting floor vibrations prior to construction.

The escalating environmental noise pollution along urban rail transit corridors, exacerbated by rapid urbanization, necessitates innovative and efficient noise control measures. A comprehensive investigation was conducted that utilized field measurements of train passing-by noise to establish a statistical energy analysis model for evaluating the acoustic performance of both vertical (VB) and fully enclosed (FB) barrier configurations. The study incorporated Maa’s theory of micro-perforated plate (MPP) parameter optimization and developed a neural network surrogate model focused on insertion loss maximization for barrier geometric design. Key findings revealed significant barrier-induced near-track noise amplification, with peak effects observed at the point located 1 m from the barrier and 2 m above the rail. Frequency-dependent analysis demonstrated a characteristic rise-and-fall reflection pattern, showing maximum amplifications of 1.47 dB for VB and 4.13 dB for FB within the 400–2000 Hz range. The implementation of optimized MPPs was found to effectively eliminate the near-field noise amplification effects, achieving sound pressure level reductions of 4–8 dB at acoustically sensitive locations. Furthermore, the high-precision surrogate model (R² = 0.9094, MSE = 0.8711) facilitated optimal geometric design solutions. The synergistic combination of MPP absorption characteristics and geometric optimization resulted in substantially enhanced barrier performance, offering practical solutions for urban rail noise mitigation strategies.

This study presents a probabilistic prediction method for train-induced vibrations by combining a deep neural network (DNN) with the mixture density model in a cascade fashion, referred to as the DNN-RMDN model in this paper. A benchmark example is conducted to demonstrate and evaluate the prediction performance of the DNN-RMDN model. Subsequently, the model is applied to a case study to investigate and compare the uncertainties of train-induced vibrations in the throat area and testing line area of a metro depot. After training, the model is capable of accurately predicting the probability density function (PDF) of train-induced vibrations at different distances from the track and at different frequencies. Utilizing the predicted PDF, probabilistic assessments can be performed to ascertain the likelihood of surpassing predefined limits. By employing a mixture density model instead of a single Gaussian distribution, the DNN-RMDN model achieves more accurate prediction of the PDF for train-induced vibrations. The proposed probabilistic assessment framework can effectively assist in vibration screening during the planning phase and in selecting and designing vibration mitigation measures of appropriate levels.

Due to their proximity to track structures, over-track buildings in depot areas may experience adverse vibrations from train operations, which can negatively impact residential comfort and the performance of precision equipment. As the test line typically involves the highest train speeds within the depot and features distinct operating conditions such as constant-speed cruising, accelerating, and braking, this study aims to investigate the influence of different train operation modes on the vibration excitation of over-track high-rise buildings through field measurements at both the central and end sections of the test line. Three operational modes were examined, including cruising, accelerating, and braking. Vibration signals were systematically collected using sensors installed in the free field, on the ground above the building foundation, and at the bases of the shear walls. A time-varying frequency phenomenon was observed at measurement points on the ground above the foundation and at the shear wall bases during train acceleration and braking. Field data indicate that vertical vibrations in the free field at the end section of the test line were significantly greater than those at the middle section, whereas vertical vibrations at the shear wall bases were comparable between the two sections. Notable vibration transmission loss occurs as the vibration propagates into the building structure, with peak values reaching 20 dB at 80 Hz during cruising operations and 35 dB at 125 Hz during acceleration and braking. This study offers valuable insights for assessing train-induced vibration transmission into high-rise over-track buildings.