The rapid expansion of urban drainage pipe networks, driven by economic development, poses significant challenges for efficient monitoring and management. The complexity and scale of these networks make it difficult to effectively monitor and manage the discharge of urban domestic sewage, rainwater, and industrial effluents, leading to illegal discharges, leakage, environmental pollution, and economic losses. Efficient management relies on a rational layout of drainage pipe network monitoring points. However, existing research on optimal monitoring point layout is limited, primarily relying on manual analysis and fuzzy clustering methods, which are prone to human bias and ineffective monitoring data. To address these limitations, this study proposes a coupled model approach for the automatic optimization of monitoring point placement in drainage pipe networks. The proposed model integrates the information entropy index, Bayesian reasoning, the Monte Carlo method, and the stormwater management model (SWMM) to optimize monitoring point placement objectively and measurably. The information entropy algorithm is utilized to quantify the uncertainty and complexity of the drainage pipe network, facilitating the identification of optimal monitoring point locations. Bayesian reasoning is employed to update probabilities based on observed data, while the Monte Carlo method generates probabilistic distributions for uncertain parameters. The SWMM is utilized to simulate stormwater runoff and pollutant transport within the drainage pipe network. Results indicate that (1) the relative mean error of the parameter inversion simulation results of the pollution source tracking model is linearly fitted with the information entropy. The calculation shows that there is a good positive linear correlation between them, which verifies the feasibility of the information entropy algorithm in the field of monitoring node optimization; (2) the information entropy algorithm can be well applied to the optimal layout of a single monitoring node and multiple monitoring nodes, and it can correspond well to the inversion results of the tracking model parameters; (3) the constructed monitoring point optimization model can well realize the optimal layout of monitoring points of a drainage pipe network. Finally, the pollution source tracking model is used to verify the effectiveness of the optimal layout of monitoring points, and the whole process has less human participation and a high degree of automation. The automated monitoring point optimization layout model proposed in this study has been successfully applied in practical cases, significantly improving the efficiency of urban drainage network monitoring and reducing the degree of manual participation, which has important practical significance for improving the level of urban water environment management.

The Industry Foundation Classes (IFC) standard has been widely implemented as an open data standard in the architecture, engineering, and construction (AEC) industry. IFC enables robust information representation and facilitates cross-disciplinary collaboration, serving as a critical data foundation for future intelligent development in the engineering field. However, current IFC research topics remain relatively fragmented, and there are still several challenges in the practical implementation of IFC. Therefore, this paper provides a comprehensive review of IFC research over the past two decades. The research progress is systematically summarized in three key areas: IFC applications, interoperability, and data processing. Through this review, the limitations in IFC development have been discussed, and future research directions are proposed. This paper aims to provide a comprehensive perspective on addressing data-related challenges in the AEC industry and contributes to facilitating the deep integration of emerging technologies such as artificial intelligence within the AEC domain.

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.

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.

This paper proposes a hybrid steel plate–bolt dry and wet jointing method, where the dry jointing part is a steel plate–bolt connector joint and the wet jointing part is a cast-in-place concrete. The novel precast concrete shear wall (PCW) combines the advantages of both dry and wet connections. A steel plate–bolt dry–wet hybrid connection shear wall model was developed using the finite element method, and a low circumferential reciprocating load was applied to the PCW. By analyzing the force and deformation characteristics of the wall, the results showed that the failure mode of novel PCWs was bending-shear failure. Compared to the concrete wall (CW), the yield load, peak load, and ductile displacement coefficient were 6.55%, 7.56%, and 21.49% higher, respectively, demonstrating excellent seismic performance. By extending the wall parameters, it was found that the increased strength of the novel PCW concrete slightly improved the load-bearing capacity, and the ductility coefficient was greatly reduced. As the axial compression ratio increased from 0.3 to 0.4, the wall ductility decreased by 22.85%. Increasing the reinforcement rate of edge-concealed columns resulted in a severe reduction in ultimate displacement and ductility. By extending the connector parameters, it was found that there was an increased number of steel joints, a severe reduction in ductility, enlarged distribution spacing, weld hole plugging and bolt yielding, reduced anchorage performance, and weakening of the steel plate section, which reduced the load-bearing capacity and initial stiffness of the wall, with little effect on ductility.