## 4 IFC Data Model for Supporting Safety Risk Identification in Deep Excavation
### Prototype System for Safety Risk Identification Based on the IFC Data Model  
The safety risk identification of deep excavation according to the workflow above is realized by establishing a CRIS prototype based on the IFC data model [28]. The CRIS mainly contains three function modules: knowledge base management, information retrieval, and risk identification.  
1. Knowledge base management module  
The core function of this function module is to act as a risk identification knowledge base. Its main functions include fact, rule, and case base management. They are designed mainly to collect, store, and represent safety risk identification knowledge (e.g., risk knowledge facts, risk identification rules, and construction risk cases). Risk knowledge facts are the knowledge base for conducting risk identification. According to our previous work [36], eight essential parts of risk knowledge are involved in the fact base. Risk knowledge, such as'safety risk', 'risk factor', and 'construction parameter', which are closely related to the risk reasoning and identification results, are mainly managed based on the information requirement model. Table 4 shows related risk knowledge aiming at the risk of'surrounding structure cracking', which is described as an example.  
2. Information retrieval module  
Based on the IFC data model, the information retrieval module retrieves and extracts construction parameters required for safety risk identification from IFC files [21]. Its main functions include information depth management, IFC retrieval, and construction information management.  
3. Risk reasoning module  
The risk reasoning module reasons safety risks involved in the construction process. It is operated in conjunction with the rule-based reasoning method, and its main functions include reasoning algorithm management, reasoning, and reasoning result output. The risk reasoning rules are stored in the rule base of the CRIS. The reasoning method for identifying potential safety risks is briefly illustrated as follows:  
\[\text{CF}(\epsilon)=\sum_{i=1}^{n}\varpi_{i}\times\text{CF}(e_{i}). \tag{1}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Risk Knowledge Part** & **Description** \\ \hline
1. Construction method & The construction methods adopted in deep excavation projects \\
2. Supporting system & The supporting system adopted in deep excavation projects \\
3. Safety risk & Potential safety risks of certain construction methods using different supporting systems \\
4. Risk factor & Risk factors leading to potential safety risks \\
5. Construction parameters & Construction parameters leading to risk factors \\
6. Risk grade & Severity of a risk \\
7. Risk consequence & Expression of the outcomes of a risk \\
8. Risk prevention measurement & Expression of prevention measurements to certain risks \\ \hline \end{tabular}
\end{table}
Table 4: Main risk knowledge parts in the fact base \[\text{CF}(h)=\text{CF}(\text{h, e})\times\text{CF}(e),\ \ when,\ \ \text{CF}(e)\geq\lambda \tag{2}\]  
where \(\text{CF}(e_{i})\) represents the certainty factor of certain value of a rule premise, \(\omega_{i}\) is the weight factor of a rule premise to a certain risk, \(\text{CF}(e)\) is the certainty factor of rule premises of a certain risk, \(\lambda\) is the threshold of the rule, \(\text{CF}(h,e)\) is the rule certainty factor, and \(\text{CF}(h)\) is the conclusion certainty factor.