Not only can waste rubber enhance the properties of concrete (e.g., its dynamic damping and abrasion resistance capacity), its rational utilisation can also dramatically reduce environmental pollution and carbon footprint globally. This study is the world’s first to develop a novel machine learning-aided design and prediction of environmentally friendly concrete using waste rubber, which can drive sustainable development of infrastructure systems towards net-zero emission, which saves time and cost. In this study, artificial neuron networks (ANN) have been established to determine the design relationship between various concrete mix composites and their multiple mechanical properties simultaneously. Interestingly, it is found that almost all previous studies on the ANNs could only predict one kind of mechanical property. To enable multiple mechanical property predictions, ANN models with various architectural algorithms, hidden neurons and layers are built and tailored for benchmarking in this study. Comprehensively, all three hundred and fifty-three experimental data sets of rubberised concrete available in the open literature have been collected. In this study, the mechanical properties in focus consist of the compressive strength at day 7 (CS7), the compressive strength at day 28 (CS28), the flexural strength (FS), the tensile strength (TS) and the elastic modulus (EM). The optimal ANN architecture has been identified by customising and benchmarking the algorithms (Levenberg–Marquardt (LM), Bayesian Regularisation (BR) and Scaled Conjugate Gradient (SCG)), hidden layers (1–2) and hidden neurons (1–30). The performance of the optimal ANN architecture has been assessed by employing the mean squared error (MSE) and the coefficient of determination (R2). In addition, the prediction accuracy of the optimal ANN model has ben compared with that of the multiple linear regression (MLR).
In order to improve the theoretical analysis of surrounding rock stability of shallow buried tunnel. The strength reduction shortest path theory is applied to the stability analysis of shallow buried tunnel surrounding rock, combined with the ultimate equilibrium strength reduction theory. We discussed the influence of the depth and span ratio of tunnel, cohesion, and internal friction angle on the shortest path of the strength reduction, and studied the effects of various factors on shallow buried tunnel safety relationship by using strength reduction factor of safety and the shortest path of the shallow buried tunnel surrounding rock and the grey relational analysis theory. The results show that: In the analysis of shallow buried tunnel in strength reduction, the approximate distribution obeys parabolic between reduction path length and the reduction ratio. When the strength reduction of cohesion is the shortest path the reduction rate is greater than the internal friction angle. The internal friction angle and cohesive force have a great influence on the stability of shallow tunnel under the method of shortest path of strength reduction. Finally, the comprehensive safety factor of shallow buried tunnel calculated by the finite element strength reduction shortest path method is greater than that calculated by the limit equilibrium method.
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