Rubberized concrete is one of the solutions to the disposal problem caused by large amounts of untreated waste rubber. To assess the performance of existing concrete structures, non-destructive-testing techniques offer a direct, quick, safe and reliable means of assessing the performance of concrete structures. Several researchers have proposed relationships, often of an exponential form, between the Ultrasonic Pulse Velocity (UPV) and compressive strength of rubberized concrete. This paper aims to propose a regression model and a genetic algorithm based backpropagation neural network (GA-BPNN) model that can be used to estimate compressive strength of rubberized concrete with UPV for different applications and accuracy requirements. Regression model in comparisons with GA-BPNN, firstly requires less computation work and will be easier for site measurement or environments without computers or certain softwares, secondly it has no barriers for users who are not familiar with machine learning models to approximately estimate the strength. The regression model comprises an Adjusted Regression Model which is a multi-variable non-linear model adjusted based on the ordinary exponential model incorporating other principal parameters, hence representing an improvement on the existing exponential model and two types of Stepwise Regression Model (pure linear and pure quadratic) will be employed. To achieve this, a database containing 158 pairs of data collected from previous literature is compiled. Results indicate that both three types of regression models and GA-BPNN are capable of effectively predicting the compressive strength of rubberized concrete with reasonable values of statistical indexes. More specifically, among three types of regression model, the pure quadratic stepwise regression model has relatively better performance with higher R and lower root-mean-square error values. Results also support that GA-BPNN has the highest accuracy compared to regression models and is proven to be reasonable for more precise estimations.

橡胶混凝土是解决大量未经处理的废橡胶造成的处置问题的解决方案之一。为了评估现有混凝土结构的性能，无损检测技术提供了一种直接、快速、安全和可靠的评估混凝土结构性能的方法。一些研究人员提出了超声波脉冲速度 (UPV) 与橡胶混凝土抗压强度之间的关系，通常呈指数形式。本文旨在提出一种回归模型和基于遗传算法的反向传播神经网络 (GA-BPNN) 模型，可用于估计具有 UPV 的橡胶混凝土的抗压强度，以满足不同的应用和精度要求。回归模型与 GA-BPNN 的比较，一是计算量少，在没有计算机或某些软件的现场测量或环境中更容易，二是对于不熟悉机器学习模型的用户来说，近似估计强度没有障碍。回归模型包括调整回归模型，它是在普通指数模型基础上，结合其他主要参数进行调整的多变量非线性模型，是对现有指数模型的改进和两种逐步回归模型（纯线性和纯二次）将被采用。为此，编译了一个包含 158 对从以前的文献中收集的数据的数据库。结果表明，三种回归模型和GA-BPNN均能有效预测橡胶混凝土的抗压强度，统计指标取值合理。更具体地说，在三种类型的回归模型中，纯二次逐步回归模型具有相对较好的性能，具有较高的 R 和较低的均方根误差值。结果还支持 GA-BPNN 与回归模型相比具有最高的准确度，并且被证明对于更精确的估计是合理的。