# Urban Vulnerability Assessment of Sea Level Rise in Singapore through the World Avatar
## 3 Methodology
### Agents
#### 3.5.4 Cost Agent  
This agent estimates the construction cost of buildings based on the results generated by the OSM agent, the building floor agent, and the GFA agent. Construction costs are estimated using GFA of buildings. The construction cost per GFA used in this study is obtained from the industry [60]. Construction cost \(c\) of building \(b\), \(c_{b}\), is estimated as  
\[c_{b}=\sum_{u}c_{u}S_{u,b}\times\text{GFA}, \tag{2}\]  
where u indicates usage type (u \(\in\) obe:PropertyUsage), \(c_{u}\) is the cost per GFA for a given usage type obtained from [60], and \(S_{u,b}\) is the usage share for usage u within building \(b\). The summation acts over all usage types of a building. Table 4 shows a selection of average construction costs per GFA derived from AIS [60].  
To obtain the data required for Equation (2), the following query is made to the knowledge graph:  
_Give me the usages and GFA of buildings. (Query from Listing A4)_  
Table 5 demonstrates a set of mock results from Query from Listing A4 which returns four variables, i.e., 'building', 'usage', 'usageShare', and 'gfa'. The value of 'usage' can be any one of the sub-classes of obe:PropertyUsage in Figure 1, whereas 'usageShare' is the fraction of the corresponding usage share, and lastly 'gfa' is the gross floor area. This table shows the usages of two buildings, i.e., a building called 'Building1' that is used as an eating establishment with some office space and a building called 'Building2' that functions as an industrial facility. By considering obe:EatingEstablishment as the 'Commercial' category in Table 4, the construction cost of 'Building1' is calculated as  
\[(0.7\times 5690+0.3\times 5575)\times 110000=\text{SGD 622105}. \tag{3}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Cost per GFA (SGD/m2)** \\ \hline Office & 5575 \\ Commercial & 5690 \\ Industrial & 2090 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average construction costs derived from AIS [60].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Building** & **Floor** & **Area** \\ \hline Building1 & 29 & 1000 \\ Building2 & 28 & 3000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mock results of Query from Listing A3. The table header is based on the requested variables in the query.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Building** & **Usage** & **UsageShare** & **gfa** \\ \hline Building1 & obe:EatingEstablishment & 0.7 & 110,000 \\ Building1 & obe:Office & 0.3 & 110,000 \\ Building2 & obe:IndustrialFacility & 1 & 120,000 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mock results of Query from Listing A4. The table header is based on the requested variables in the query.  
This process is repeated for each building, with the resulting values being incorporated into the knowledge graph. While this approach may seem simplistic--given that factors such as location and raw material price fluctuations over the years can impact estimated construction costs--this method provides a baseline for demonstration purposes. As each agent in TWA is deployed as a Docker container, this flexibility allows the cost agent to be replaced should there be a more precise methodology for representing building value.