Understanding the underlying processes associated with the viscoelasticity performance of ethylene-propylene-diene monomer (EPDM) during its service life is essential for assessing and predicting its waterproofing performance in underground infrastructure. The viscoelasticity of the polymer is closely related to its free volume, and both of these properties depend on multiple factors, such as temperature, stress magnitude, and strain level. To explore the fundamental viscoelastic behavior of EPDM using free volume as a proxy for viscoelasticity, this article investigates the influence of temperature, stress magnitude, and strain level, as well as their combined effect, on the free volume through molecular dynamics (MD) simulations. An EPDM cross-linked molecular model was built and verified by comparing the simulation values of glass transition temperature, mechanical properties, and gas diffusivity with the experimental results reported in the literature. Then, the dependence of EPDM’s fractional free volume on temperature, strain, and their combined effect was investigated via MD simulations, on the basis of which the applicability of various superposition principles was also evaluated.

The cross-linking structure of the Ethylene-propylene-diene monomer (EPDM) is made up of a number of cross-linking types, including carbon atoms from the main chain or monomer and ether crosslinks formed during degradation. Through molecular dynamic simulations, the contribution of each type of cross-linked structure to the dynamics and mechanical properties of EPDM, the study’s focus, were investigated. Cross-linking between the tertiary carbons of two main chains, cross-linking at the monomer’s unsaturated position, ether cross-linking after oxidation, and other combinations of target cross-linked carbon atoms from different positions, totaling eight types of cross-linked types, were mixed with EPDM free chains in a 1:1 ratio to form eight types of cross-linked EPDMs. These varieties of cross-linked EPDMs were then compared to an uncross-linked EPDM in terms of density, radius of gyration, free volume, mean square displacement, and uniaxial tensile stress-strain curves. It was found that the cross-linking was always proven to have a favorable influence on mechanical characteristics; however, the relaxation inhibition effect varied. The cross-linking between the diene monomer at the C9 position resulted in a more flexible molecular shape and was more than double the free volume of the uncross-linked EPDM, resulting in an improved diffusion ability. The ether cross-linking produced by the oxidation of the side chain cross-linking improved the positive contribution to stiffness and enhanced the inhibitory impact on diffusion properties, whereas the main chain cross-linking had the opposite effect. The research presented in this study leads to a better knowledge of the microscopic aspects underlying EPDM performance.

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 (E_max) 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.

Building a deep foundation pit in urban centers frequently confronts issues such as closeness to structures, high excavation depths, and extended exposure durations, making monitoring and prediction of the settlement and deformation of neighboring buildings critical. Machine learning and deep learning models are more popular than physical models because they can handle dynamic process data. However, these models frequently fail to establish an appropriate balance between accuracy and generalization capacity when dealing with multi-objective prediction. This work proposes a multi-objective prediction model based on the XGBoost algorithm and introduces the Random Forest Bayesian Optimization method for hyperparameter self-optimization and self-adaptation in the prediction process. This model was trained with monitoring data from a deep foundation pit at Luomashi Station of Chengdu Metro Line 18, which are characterized by a sand and pebble stratum, cut-and-cover construction, and a depth of 45.5 m. Input data of the model included excavation rate, excavation depth, construction time, shutdown time, and dewatering; output data included settlement, ground settlement, and pit deformation at an operating metro station only 5.7 m adjacent to the ongoing pits. The training effectiveness of the model was validated through its high R² scores in both training and test sets, and its generalization ability and transferability were evaluated through the R² calculated by deploying it on adjacent monitoring data (new data). The multi-objective prediction model proposed in this paper will be promising for monitoring the data processing and prediction of settlement of surrounding buildings for ultra-deep foundation pit engineering.