# Prediction Model for Compaction Quality of Earth-Rock Dams Based on IFA-RF Model
## 1 Introduction  
In the construction quality management of earth-rock dams, compaction quality control is a critical factor in ensuring the stability and disaster resistance of the dam structure. However, traditional compaction quality control methods primarily rely on test pit experiments to determine the dry density. These methods are subject to limitations such as insufficient sampling points, being time-consuming, and retrospective evaluation, which hinder real-time monitoring and consequently compromise both the timeliness and accuracy of construction efficiency evaluations and quality assessments [1]. Therefore, developing a method that can evaluate the compaction quality of earth-rock dams accurately and comprehensively in real time holds significant engineering value. Extensive research has been conducted on this topic, focusing on the development of quality evaluation models based on real-time monitoring technology and the application of intelligent optimization algorithms. Domestically, a real-time monitoring system based on GPS, GPRS, and PDA technology was proposed by [PERSON] et al. [2] and [PERSON] et al. [3],successfully realizing full-process online monitoring of the compaction process, including parameters such as vehicle speed, number of passes, and compaction thickness. [PERSON] et al. [4] introduced the compaction value (CV) as a real-time indicator of compaction quality, verifying the significant correlation between CV and compaction degree. Other studies, such as those by [PERSON] et al. [5], applied multiple linear regression analysis to assess factors affecting the shear strength of earth-rock dams, while [PERSON] et al. [6] developed a nonlinear model for compaction degree using BP neural networks. [PERSON] et al. [7] and [PERSON] et al. [8] proposed compaction quality evaluation methods based on chaotic firefly algorithm (FA)-enhanced support vector regression (SVR) and adaptive differential evolution algorithm-improved extreme learning machine (ELM), respectively. [PERSON] et al. [9] established a real-time evaluation model for compaction quality using a kernel method combined with an adaptive chaotic bacterial foraging algorithm. [PERSON] et al. [10] developed a full-section compaction quality evaluation method for subgrades based on PSO-BPNN-AdaBoost (PBA). These studies have made significant progress in compaction quality evaluation. Internationally, [PERSON] et al. [11] built an intelligent feedback control system based on GPS for the real-time evaluation of roadbed compaction quality. [PERSON] et al. [12] developed a 3D vibration monitoring system for continuous compaction quality detection and feedback control. [PERSON] et al. [13] introduced a compaction monitoring system (CMS) for monitoring and documenting the compaction process of asphalt mixtures. [PERSON] et al. [14] proposed a practical method to maximize the benefits of the current intelligent compaction (IC) technology for quality control (QC). Although these studies have made notable advancements, limitations such as small sample sizes and data uncertainty in the parameters influencing compaction quality hinder the ability to fully reflect the spatial distribution of compaction quality. Currently, scholars widely employ linear and nonlinear regression models for compaction quality evaluation. However, traditional methods frequently neglect parameter uncertainty, and their fitting accuracy requires enhancement with compromised generalization ability. To overcome these limitations, intelligent algorithms have been increasingly applied in the analysis and prediction of compaction quality. Techniques such as artificial neural networks, genetic algorithms, and RF have demonstrated effectiveness. The RF algorithm [15], recognized for its strong nonlinear modeling capability and high noise resistance, is particularly suitable for small sample datasets and has demonstrated good performance in predicting uncertain engineering parameters, garnering attention from both academia and industry [16; 17; 18].