## 5 Conclusions and Future Directions  
The complexities of the earthquake source process, heterogeneities in 3D Earth structure through which seismic waves travel, and intricate local site conditions govern the response of structures subjected to earthquake shaking. In a PBEE framework, different characteristics of earthquake ground motions, quantified in terms of parameters related to wave amplitude, spectral content, and energy, are represented by a variety of intensity measures (IMs). Several intensity measures are reviewed in this paper, as well as the thought processes behind their development. Following this, we discuss ground motion selection approaches from the basic level to the state of the art with a special focus on IM-based approaches. Next, based on more than 3500 near-field strong motion recordings (magnitude between \(M_{w}\) 5-8) from the NGA West 2 PEER database and structural analysis of a set of different 2D reinforced concrete frames, we discuss the correlations between ten simple IMs and building response (inter-story drift ratio). The natural period of the structural frames varies from 0.1 to 1.55 sec, and the corresponding building response is extracted by using non-linear time history analysis. We analyze the effect of structural (\(T_{f}\)) and seismological parameters (\(M_{w}\), \(R_{api}\), \(V\)s30) on IM-EDP correlations. Finally, we provide recommendations on how to obtain efficient and sufficient EDPs from these IMs. Seismological and structural uncertainties pose both challenges and opportunities for future research. Despite the improvements in ground motion sensors and the increasing importance of rotational motion on structures in recent studies [103; 104], rotational motion is rarely considered in IMs. Researchers have proposed IMs for rocking structures [105; 106] \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**IM(s)**} & \multirow{2}{*}{**Efficiency**} & \multicolumn{3}{c}{\(p\) **values**} \\ \cline{3-5}  & & \(p_{M}\) & \(p_{R}\) & \(p_{Vs30}\) \\ \hline \(PGV\) & 0.478 & \(\mathbf{8.47\times 10^{-1}}\) & \(3.61\times 10^{-16}\) & \(3.56\times 10^{-2}\) \\ _Sat(T)_ & 0.486 & \(\mathbf{1.85\times 10^{-1}}\) & \(1.09\times 10^{-20}\) & \(\mathbf{7.69\times 10^{-1}}\) \\ _VSI_ & 0.466 & \(\mathbf{1.10\times 10^{-1}}\) & \(1.10\times 10^{-10}\) & \(\mathbf{5.22\times 10^{-2}}\) \\ _PGV, Sa(T)_ & 0.447 & \(\mathbf{3.85\times 10^{-1}}\) & \(9.52\times 10^{-14}\) & \(\mathbf{6.33\times 10^{-2}}\) \\ _PGV, VSI_ & 0.455 & \(\mathbf{2.75\times 10^{-1}}\) & \(8.24\times 10^{-11}\) & \(1.91\times 10^{-2}\) \\ _PGV, Sa(T), VSI_ & 0.446 & \(\mathbf{2.93\times 10^{-1}}\) & \(2.90\times 10^{-12}\) & \(\mathbf{5.45\times 10^{-2}}\) \\ _PGV, Sa(T)_ & \(A_{RMS}\) & 0.443 & \(\mathbf{2.50\times 10^{-1}}\) & \(4.17\times 10^{-12}\) & \(\mathbf{1.08\times 10^{-1}}\) \\ _PGV, Sa(T)_ & 0.445 & \(\mathbf{2.78\times 10^{-1}}\) & \(2.47\times 10^{-11}\) & \(\mathbf{5.29\times 10^{-2}}\) \\ _PGV, VSI_, \(A_{RMS}\) & 0.452 & \(\mathbf{1.61\times 10^{-1}}\) & \(3.54\times 10^{-9}\) & \(3.54\times 10^{-2}\) \\ _PGV, VSI_, \(I_{A}\) & 0.454 & \(\mathbf{1.98\times 10^{-1}}\) & \(6.94\times 10^{-9}\) & \(\mathbf{1.55\times 10^{-2}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Efficiency and \(p\) values for frames > 0.5 s.  
that are more susceptible to rotational motion damage, but a broader research effort is needed to develop further IMs. Recent advances in machine learning have led to the development of innovative approaches for simulating synthetic ground motions [107; 108] and developing new ground motion models [109; 110; 111; 112; 113]. It may be interesting to apply these novel techniques to evaluating intensity measures and choosing ground motion records. A few studies have used these strategies for determining efficient IMs [114; 115] and for proposing new metrics to evaluate IMs [116]. To the best of our knowledge, there are no stand-alone machine learning algorithms for selecting ground motions.