Reinforced steel is one of the most important building materials in civil engineering and improving the intelligence of steel reinforcement engineering can greatly promote the intelligent development of the construction industry. This research addressed the problems of the slow speed and poor accuracy of manually extracting rebar processing information, which leads to a low degree of rebar processing intelligence. Firstly, based on digital image processing technology, image preprocessing methods such as binarization and grayscale were used to eliminate redundant information in a detail drawing of a rebar. An image segmentation method based on pixel statistics was proposed to store the geometric and non-geometric information of the detail drawing of the rebar separately. Next, the bending angle was extracted by line thinning and corner detection, and the bending direction of the steel bar was determined based on the mathematical characteristics of the vector product. Finally, the non-geometric information was extracted by combining the morphological algorithm and the Optical Character Recognition (OCR) engine. According to the characteristics of the information sequence, an information mapping method was proposed to realize the integration of geometric and non-geometric information. The applicability and accuracy of this method for extracting the steel bar’s information were tested by experiments, and it was shown that the method also provides a theoretical basis for realizing the intelligentization and informatization of steel bar processing.

To meet the requirements of specifications, intelligent optimization of steel bar blanking can improve resource utilization and promote the intelligent development of sustainable construction. As one of the most important building materials in construction engineering, reinforcing bars (rebar) account for more than 30% of the cost in civil engineering. A significant amount of cutting waste is generated during the construction phase. Excessive cutting waste increases construction costs and generates a considerable amount of CO 2 emission. This study aimed to develop an optimization algorithm for steel bar blanking that can be used in the intelligent optimization of steel bar engineering to realize sustainable construction. In the proposed algorithm, the integer linear programming algorithm was applied to solve the problem. It was combined with the statistical method, a greedy strategy was introduced, and a method for determining the dynamic critical threshold was developed to ensure the accuracy of large-scale data calculation. The proposed algorithm was verified through a case study; the results confirmed that the rebar loss rate of the proposed method was reduced by 9.124% compared with that of traditional distributed processing of steel bars, reducing CO 2 emissions and saving construction costs. As the scale of a project increases, the calculation quality of the optimization algorithm for steel bar blanking proposed also increases, while maintaining high calculation efficiency. When the results of this study are applied in practice, they can be used as a sustainable foundation for building informatization and intelligent development.

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Compliance checking is a very important step in engineering construction. With the development of information technology, automated compliance checking (ACC) has been paid more and more attention by researchers. One of the most important steps in automated compliance checking is the representation of the code information. However, the relationship constraint is often ignored in the code information and spatial geometric relationship is challenging to represent. The general code representation method does not have enough ability to identify the situation that does not meet the checking conditions because it is easy to cause semantic ambiguity in the checking results. This paper proposes a code representation method, and the building code information is represented in five parts. Relationships in the engineering domain and spatial relationships can be represented in constraint mode; different spatial relationship constraint-checking methods are also explicated. Constraint subject and constraint item can distinguish checking conditions and requirements, which supports semantic checking results. The mapping between the building information ontology and the code concepts is established, which can be used to automatically generate reasoning rules for compliance checking. Finally, the proposed method is verified by the representation of the China Metro Design Code and the application of the actual Metro model.