To study the rheological and aging properties of vegetable oil–based polyurethane (V-PU)–modified asphalt, V-PU terminated with an –NCO group was synthesized from renewable castor oil, and liquefied MDI-100LL and 10–40 wt% V-PU modified asphalts were prepared. Temperature classification, multiple stress creep recovery (MSCR), and linear amplitude scanning (LAS) tests were carried out. The results showed that the modulus, the creep recovery rate (R), and the yield stress and yield strain of the V-PU modified asphalts significantly increased in the order: 0 wt% < 10 wt% < 20 wt% < 40 wt% < 30 wt%, while the phase angle and the unrecoverable creep compliance (Jnr) changed in the opposite order, and the high temperature grade of 30 wt% V-PU modified asphalt was 4 grades higher than that of the base asphalt, which indicated that the addition of V-PU enhanced the fatigue, permanent deformation, and recovery deformation resistance. The 30 wt% sample-exhibited phase inversion had the best performance. Comprehensive FTIR, GPC, and fluorescence microscopy analyses showed that the molecular weight significantly increased and the V-PU molecules agglomerated after aging. The excess–NCO groups of V-PU prepolymer react with water in the air and the active hydrogen in the asphalt system and finally form a cross-linked three-dimensional network structure with the asphalt to improve performance. The mechanism of intramolecular cementation reaction and the aging process of V-PU-modified asphalt was creatively derived.

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Integrated, timely data about pavement structures, materials and performance information are crucial for the continuous improvement and optimization of pavement design by the engineering research community. However, at present, pavement structures, materials and performance information in China are relatively isolated and cannot be integrated and managed. This results in a waste of a large amount of effective information. One of the significant development trends of pavement engineering is to collect, analyze, and manage the knowledge assets of pavement information to realize intelligent decision-making. To address these challenges, a knowledge graph (KG) is adopted, which is a novel and effective knowledge management technology and provides an ideal technical method to realize the integration of information in pavement engineering. First, a neural network model is used based on the principle of deep learning to obtain knowledge. On this basis, the relationship between knowledge is built from siloed databases, data in textual format and networks, and the knowledge base. Second, KG-Pavement is presented, which is a flexible framework that can integrate and ingest heterogeneous pavement engineering data to generate knowledge graphs. Furthermore, the index and unique constraints on attributes for knowledge entities are proposed in KG-Pavement, which can improve the efficiency of internal retrieval in the system. Finally, a pavement information search engine based on a knowledge graph is constructed to realize information interaction and target information matching between a webpage server and graph database. This is the first successful application of knowledge graphs in pavement engineering. This will greatly promote knowledge integration and intelligent decision-making in the domain of pavement engineering.