养生条件对冷拌冷铺改性乳化沥青混合料强度增长规律的影响研究

Study on the Influence of Curing Conditions on the Splitting Strength Growth Law of Cold-Mixed and Cold-Laid Modified Emulsified Asphalt Mixture

为了分析养生条件对冷拌冷铺改性乳化沥青混合料强度形成的影响,采用与其路用性能相关性较好的劈裂强度作为评价指标,研究了不同养生条件下AC-13型改性乳化沥青混合料劈裂强度随养生时间的变化规律。首先根据冷拌冷铺改性乳化沥青混合料材料特性和北京市气候特点,确定了9种养生条件;然后采用室内恒温恒湿箱对成型后的试件进行养生,测试不同养生条件下混合料劈裂强度随养生时间的变化,并通过回归分析建立了在温度、相对湿度共同作用下,冷拌冷铺改性乳化沥青混合料的养生时间预测模型;最后通过室外自然养生试验对预测模型进行了验证。试验结果表明:养生条件的改变仅影响混合料的强度增长速率和养生时间,但对其强度增长趋势及最终力学强度基本无影响;室外自然养生试验确定的混合料养生时间与模型预测时间相对误差小于10%,说明该预测模型准确性较高。

In order to analyze the effect of curing conditions on the strength formation of cold-mixed coldlaid modified emulsified asphalt mixture,the splitting strength of AC-13 modified emulsified asphalt mixture under different curing conditions was studied by using the splitting strength which is closely related to its road performance as the evaluation index. First of all, according to the material characteristics of cold mixed cold laid modified emulsified asphalt mixture and the climatic characteristics of Beijing,nine kinds of curing conditions were determined( the curing temperature was 15℃,30℃ and45℃ respectively;the relative humidity was 30%,60% and 90% respectively);then the indoor constant temperature and humidity box was used to cure the formed specimen,and the change of the splitting strength of the mixture with the curing time under different curing conditions was tested. And the prediction model of curing time of cold mix cold paving modified emulsified asphalt mixture under the combined action of temperature and relative humidity was established by regression analysis. Finally,the prediction model was verified by outdoor natural curing experiment. The results show that the change of curing condition only affects the strength growth rate and curing time of the mixture,but has no effect on the strength growth trend and the final mechanical strength. The relative error between the mixture curing time and the model prediction time determined by the outdoor natural curing test is less than 10%,indicating that the prediction model is more accurate.