基于磨损特性及矿物组成的粗集料耐磨性研究

doi:10.16552/j.cnki.issn1001-1625.2025.0979

摘要/Abstract

摘要：

为从细观层面揭示岩石集料的表面磨损机制，本文首先通过XRF及XRD分析了玄武岩、花岗岩、石灰岩、铝矾土四类粗集料的化学组成与矿物特征；其次，采用泰伯磨耗试验对各类粗集料进行了摩擦磨损测试，结合白光干涉三维重构，实现了磨损过程的量化表征；最终，以等效莫氏硬度H_EMH和集料硬度参数H_AHP作为矿物均质化后的集料宏观力学强度指标，分别建立了基于H_EMH、H_AHP的集料耐磨性预测模型。研究结果表明：磨损测试过程中玄武岩、花岗岩、铝矾土三类集料摩擦系数均呈先快速上升后趋于稳定的共性变化，石灰岩摩擦系数上升时段则略有滞后，最终稳定值由高到低依次为石灰岩、花岗岩、玄武岩和铝矾土；基于磨损区域的形貌特征，系统对比了断面最大磨损深度、磨损体积、磨损质量及磨损率四类集料耐磨性评价指标，提出磨损率是表征集料耐磨性的优选指标；仅通过H_EMH或H_AHP建立的集料耐磨性预测模型精度均在0.92以下，考虑到磨损过程中的黏着效应，引入摩擦系数对预测模型拟合结果进行了修正，修正后硬度指标与磨损率的拟合系数显著提升，均超过0.97。

关键词: 粗集料, 磨损机制, 摩擦磨损测试, 三维重构, 磨损率, 矿物组成, 预测模型

Abstract:

In order to reveal the surface abrasion mechanism of rock aggregate from the mesoscopic scale， the chemical composition and mineral characteristics of basalt， granite， limestone and bauxite were analysed by XRF and XRD. Subsequently， the Taribolab test was used to test the friction and abrasion of various types of coarse aggregates， and the quantitative characterisation of the abrasion process was realised by combining the three-dimensional reconstruction of white light interference. Ultimately， the equivalent Mohs hardness H_EMH and aggregate hardness parameter H_AHP were utilised as the macroscopic mechanical strength indexes of aggregate post-mineral homogenisation. In addition， prediction models of aggregate wear resistance were established based on H_EMH and H_AHP， respectively. The results demonstrate that the friction coefficient of basalt， granite and bauxite increases rapidly and then stabilises during the abrasion test. The increasing period of limestone friction coefficient is slightly delayed. Finally， the stable value from highest to lowest， is as follows： limestone， granite， basalt and bauxite. A systematic comparison was made of wear resistance evaluation indexes， including the maximum abrasion depth， abrasion volume， abrasion quality and abrasion rate of the four types of aggregates， based on the morphological characteristics of the abrasion area. It was proposed that the abrasion rate should be used as the preferred index to characterize the wear resistance of the aggregate. Concurrently， the prediction models reveal that the accuracy of the model established solely by H_EMH and H_AHP are below 0.92. In consideration of the adhesion effect in the wear process， the friction coefficient is introduced to rectify the fitting results of the prediction model. Following the implementation of the correction， there is a substantial enhancement in the fitting coefficient of the hardness index and the abrasion rate， which reaches a value greater than 0.97.

Key words: coarse aggregate, abrasion mechanism, friction and abrasion test, three-dimensional reconstruction, abrasion rate, mineral composition, prediction model

中图分类号:

U416

陈庚, 李晓龙, 侯建伟, 贾敬鹏. 基于磨损特性及矿物组成的粗集料耐磨性研究[J]. 硅酸盐通报, 2026, 45(4): 1471-1482.

CHEN Geng, LI Xiaolong, HOU Jianwei, JIA Jingpeng. Wear Resistance of Coarse Aggregate Based on Abrasion Characteristics and Mineral Composition[J]. BULLETIN OF THE CHINESE CERAMIC SOCIETY, 2026, 45(4): 1471-1482.

图/表 16

图1 粗集料的宏观形貌

Fig.1 Macroscopic morphology of coarse aggregates

图2 粗集料的微观形貌

Fig.2 Microtopography of coarse aggregates

表1 粗集料的物理性能指标

Table 1 Physical performance indexes of coarse aggregates

Testing item	Testing result				Testing method
Testing item	Basalt	Granite	Limestone	Bauxite	Testing method
Particle size/mm	4.75~9.50	4.75~9.50	4.75~9.50	4.75~9.50	T 0302—2024
Crush value/%	13.7	14.4	20.5	10.8	T 0316—2024
Firmness value/%	2.8	3.1	5.0	2.1	T 0314—2024
Apparent density/（g·cm^-3）	2.880	2.681	2.722	3.182	T 0304—2024
Los angeles abrasion value/%	15.8	17.2	22.4	9.5	T 0317—2024
Polishing value/BPN	52	46	43	60	T 0321—2024

表2 集料的化学成分分析结果

Table 2 Analysis results of chemical composition of aggregates

Aggregate type	SiO₂	Al₂O₃	CaO	MgO	Fe₂O₃	K₂O	Na₂O	TiO₂	MnO	P₂O₅	Misc.
Limstone	6.83	2.40	82.10	5.32	1.30	0.72	0.15	0.21	0.02	0.04	0.91
Granite	70.37	13.08	2.84	1.29	4.75	3.08	2.98	0.11	0.07	0.04	1.39
Basalt	47.92	15.77	9.80	1.52	12.80	1.41	3.26	3.28	0.17	0.06	4.01
Bauxite	9.52	87.50	0.17	0.15	0.55	0.40	0.01	1.25	0.01	0.20	0.24

图3 基于XRD谱的集料矿物成分分析结果

Fig.3 Analysis results of aggregate mineral composition based on XRD patterns

图4 Tribolab测试中各类集料摩擦系数变化曲线

Fig.4 Variation curves of friction coefficient for various types of aggregates in Tribolab test

图5 集料Tribolab测试后的二维形貌特征及断面轮廓曲线

Fig.5 Two-dimensional morphological characteristics and cross-section contour curves of aggregates after Tribolab test

图6 集料Tribolab测试后的三维形貌特征

Fig.6 Three-dimensional morphological characteristics of aggregates after Tribolab test

图7 集料Tribolab测试后Hmax（i）分布特征

Fig.7 Characterization of Hmax（i） distribution of aggregates after Tribolab test

表3 集料平均断面最大磨损深度

Table 3 Hmax（avg） of aggregates

Aggregate type	Basalt	Granite	Limestone	Bauxite
H_max（avg）/μm	22.525	26.072	68.026	15.292

表4 集料各类磨损指标计算结果

Table 4 Calculation results of various abrasion indexes of aggregates

Aggregate type	W_v /mm³	W_m /g	W_R/（mm³·N^-1·m^-1）
Basalt	0.126	2.390×10^-4	6.584×10^-4
Granite	0.166	3.430×10^-4	8.688×10^-4
Limestone	0.438	1.263×10^-3	2.284×10^-3
Bauxite	0.024	7.646×10^-5	1.252×10^-4

表5 集料中所涉矿物的莫氏硬度

Table 5 Mohs hardness of minerals involved in aggregates

Aggregate type	Mineral type	Proportion of mineral/%	Mohs hardness
Basalt	Feldspar	56.4	6.5
	Hornblende	27.6	5.5
	Pyroxene	15.8	5.5
Granite	Quartz	56.6	7.0
	Feldspar	37.0	6.5
	Mica	6.4	2.5
Limestone	Dolomite	7.6	3.5
Limestone	Calcite	92.4	3.0
Bauxite	Corundum	78.4	9.0
	Mullite	18.2	7.0
	Feldspar	3.4	6.5

表6 集料等效莫氏硬度与集料硬度参数计算结果

Table 6 Calculation results of equivalent Mohs hardness and hardness parameters of aggregates

Aggregate type	H_EMH	H_AHP
Basalt	6.1	8.1
Granite	6.5	11.5
Limestone	3.2	3.6
Bauxite	8.6	13.1

图8 集料硬度指标与WR的相关性分析结果

Fig.8 Results of correlation analysis between aggregate hardness index and WR

表7 集料摩擦系数测试结果

Table 7 Test results of friction coefficient of aggregates

Aggregate type	Basalt	Granite	Limestone	Bauxite
μ	0.56	0.60	0.76	0.48

表8 基于硬度指标及摩擦系数的WR预估模型

Table 8 WR prediction model based on hardness index and friction coefficient

Fitting parameter	Multiple linear regression model	R²
H_EMH， μ	W_R=-0.000 012 7H_EMH+0.001 02μ-0.005 04	0.971
H_AHP， μ	W_R=-0.000 042 5H_AH_P +0.008 57μ-0.003 63	0.976

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