The safety operation of dams after extreme load is an important frontier research topic in the field of dam engineering. The dam health monitoring provides a reliable data basis for a safety evaluation after extreme loads. This study proposes a novel data-driven fusion model for a dam safety evaluation after extreme load based on monitoring data derived by sensors. First, the relationship between dam environmental quantity and effect quantity is deeply excavated based on bidirectional long short-term memory (BiLSTM) network, which is a deeply improved LSTM model. Aiming at the parameter optimization problem of BiLSTM model, sparrow search algorithm (SSA), which is an advanced optimization algorithm, is integrated. Second, conducting the constructed SSA-BiLSTM model to estimate the change law of dam effect quantity after the extreme load. Finally, the Mann–Whitney U-test theory is introduced to establish the evaluation criterion of the dam safety state. Project case shows that the multiple quantitative prediction accuracy evaluation indicators of the proposed method are significantly superior to the comparison method, with mean absolute percentage error (MAPE) and mean absolute error (MAE) values decreasing by 30.5% and 27.8%, respectively, on average. The proposed model can accurately diagnose the dam safety state after the extreme load compared with on-site inspection results of the engineering department, which provides a new method for dam safety evaluation.

The analysis of common properties of growth for crops is the basis for further understanding crop growth in different regions. We used four typical crops of China, winter wheat, summer maize, rice, and cotton, to build an integrated model suitable for simulating the growth of different crops. The rates and characteristics of crop growth were systematically analysed based on semirelative and fully relative logistic models of crop growth, and a comprehensive, fully relative logistic model for the four crops was established. The spatial distributions of the maximum leaf area index (LAI_max) and maximum dry-matter accumulation (DMA_max) for the four crops were analysed. The semirelative and fully relative growth models exhibited different characteristics of crop growth. The essential characteristics of growth and the characteristics of the crops at each stage of growth were better represented by the fully relative logistic growth model than by the semirelative model. The comprehensive, fully relative logistic model fitted the growth of all four crops well. LAI_max and DMA_max varied greatly amongst the four crops and were strongly regionally distributed. These indicators for the same crop were differentially spatially variable, and the two indicators were not significantly correlated, except for rice. LAI_max and DMA_max in different regions could be obtained using a binary quadratic equation of water consumption and growing degree days for the crops. This study provides a novel method for quantitatively judging the status of crop growth, predicting crop yields, and planning for regional agricultural planting.

Code compliance checking is a very important step in engineering construction, but most of code compliance checking relies on manual review at present. With the development of semantic web technology, ontology can be used to represent code information and check the code automatically. However, code ontology is established manually by researchers who have sufficient domain knowledge, in which it is easy to cause poor hierarchical structure of classes. It is also possible for code ontology not being suitable for compliance check. This paper proposes a semiautomatic construction method of railway code ontology based on ifcOWL. The railway code ontology is developed by converting ifcOWL which extends semantic information of railway code. This method can ensure the completeness of the hierarchical relationship of the classes in code ontology with good scalability, which makes use of taxonomy in ifcOWL. The establishment of ontology is divided into two processes with low coupling, namely, extension and conversion, which reduces the domain knowledge requirements of the researchers. Finally, a practical specification is selected to generate a code ontology that achieves some clauses checking.

Experiments were performed on four specimens of steel frames with infilled recycled aggregate concrete shear walls (SFIRACSWs), one specimen of infilled ordinary concrete wall, and one pure-steel frame were conducted under horizontal low cyclic loading. The influence of the composite forms of steel frames and RACSWs (namely, infilled cast-in-place and infilled prefabricated) on the failure modes, transfer mechanisms of lateral force, bearing capacity, and ductility of SFIRACSWs is discussed, and the concrete type and connecting stiffness of beam–column joints (BCJs) are also considered. Test results showed that infilled RACSWs can increase the bearing capacity and lateral stiffness of SFIRACSWs. The connecting stiffness of BCJs slightly influences the seismic behavior of SFIRACSWs. In the infilled cast-in-place RACSWs, the wall cracks mainly extended along the diagonal direction. The bearing capacity was 2.4 times higher than in the pure steel frame, the initial stiffness was 4.3 times higher, and the displacement ductility factors were 2.44–2.69 times higher. In the infilled prefabricated RACSWs, the wall cracks mainly extended along the connection between the embedded T-shape connectors and walls before finally connecting along the horizontal direction. Moreover, shear failure occurred in the specimens. The bearing capacity was 1.44 times higher than that of the pure steel frame, the initial stiffness was 2.8 times higher, and the displacement ductility factors were 3.32–3.40 times higher. The degradation coefficients of the bearing capacity were more than 0.85, indicating that the specimens demonstrated a high safety reserve.

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.