An automatic text generation algorithm of technical disclosure for catenary construction based on knowledge element model

Abstract

Preparing technical disclosure documents has always been a labor-intensive task in the site management practice of current OCS (Overhead Contact System) project construction, and the overall information level of construction management is not high. As a result, the technical disclosure documents are of poor quality, which significantly affects the effective play of technical documents in guiding construction activities. In view of the above limitations, this paper proposes a text-generation method for OCS engineering technical disclosure based on a knowledge element model. By investigating the characteristics of OCS engineering technical knowledge, a representation model is established to model the technical knowledge from multiple sources, such as case data, standard specifications, and design data. As OCS engineering is highly standardized, we propose a case-rule hybrid reasoning model for the reuse of OCS engineering case knowledge. The mining and utilization of earlier engineering technical knowledge are made possible by similar retrieval of precedent situations and adaptive rules modification. Finally, we suggest an automatic text-generating technique based on a configuration template for new building projects. The knowledge element model is converted into a technical disclosure document expressed in natural language using a two-level mapping process. The cantilever installation project is used as an illustration for empirical research, and relevant practitioners are invited to carry out a manual review by questionnaire from the perspectives of topic relevance, topic integrity, topic word implication, sentence smoothness, sentence continuity, and information volume. At the same time, standard assessment metrics such as BLEU and ROUGE are employed to compare with the neural network-based text generation approach. The outcomes demonstrate that the strategy suggested in this paper can generate technical disclosure text that performs well. Overall, its text integrity and readability may satisfy on-site management's demands and help on-site management lessen the workload of technical management staff.