## 1 Introduction  
With rapid economic development and the increase in traffic volumes in China, many early-built highways now require renovation and expansion due to deteriorating pavement conditions [1, 2]. Highway reconstruction aims to improve transportation efficiency, stimulate economic growth, and enhance quality of life for residents. However, current standards for assessing asphalt pavement performance in China, i.e., the Highway Performance Assessment Standards (JTG 5210-2018), are insufficient to guide reconstruction works [3]. Therefore, developing a performance assessment indicator system and evaluation models for existing asphalt pavements is imperative in highway renovation and expansion projects. Numerous studies have explored indicator systems and models for assessing existing asphalt pavement in highway renovation and expansion projects in China. [PERSON] et al. [4] employed non-destructive testing (NDT) systems to establish a decision-making framework for pavement maintenance in renovation and expansion projects. [PERSON] et al. [5] utilized artificial field surveys, ground-penetrating radar (GPR), and a falling-weight deflectometer (FWD) to inspect existing asphalt pavements in highway reconstruction and expansion projects, subsequently developing a corresponding decision-making framework. While these are pioneering detection methods, the main challenge lies in comprehensive analysis and utilization of inspection results. The rapid progress of computer technology has offered a plethora of valuable tools for conducting comprehensive pavement performance evaluations. Notably, fuzzy mathematics [6], grey theory [7], neural networks [8], entropy theory [9; 10], and support vector machines [11], among others, were initially applied for the comprehensive evaluation of pavement performance. However, there are still several key issues that require further discussion concerning the proper application of these tools in road surface evaluation [12; 13; 14; 15]. [PERSON], an academician from the Chinese Academy of Engineering, proposed the cloud model theory based on fuzzy mathematics and statistical mathematics to realize the mapping and conversion between uncertain linguistic values and accurate values, better describing the randomness, fuzziness, and relevance of variables [16]. Since their inception, cloud models have achieved success across diverse fields through natural language processing, data mining, decision analysis, and intelligent control [17; 18; 19]. [PERSON] [17] and [PERSON] et al. [20] reviewed the generality and flexibility of cloud models in dealing with complex problems and multi-indicator system problems. Cloud models have been preliminarily applied in pavement performance assessment. [PERSON] and [PERSON] [21] analyzed the fuzziness, randomness, and discreteness in the process of pavement condition evaluation, established a pavement condition evaluation model based on the comprehensive weight and cloud model, and verified the model's correctness through engineering applications. [PERSON] et al. [22] established an entropy weight-cloud model for evaluating pavement performance and compared the calculated results of the model with those of the matter element model, confirming the feasibility of the model. [PERSON] et al. [23] combined the gray level co-occurrence matrix (GLCM) algorithm and the cloud model theory to construct a damage identification and evaluation model for pavements. However, applications of cloud models in evaluating pavement performance remain limited. Moreover, existing studies neglect reconstruction project particulars by not discussing indicator systems tailored for such work. This study established an evaluation model based on a comprehensive weighting method and the normal cloud model theory for assessing the performance of existing asphalt pavement in highway renovation and expansion projects, taking into account the fuzziness and randomness inherent in the assessment.