## 5 Theoretical Framework and Future Directions  
The overview of results serves as the foundation for the conceptual framework. It has been discovered that AI has a tremendous effect on construction activities such as computer vision, robotic construction, electronic augmentation, machine learning, digitalization, collaboration, and synchronization. While civil engineering includes several sub-fields such as structural engineering, construction engineering and management, transportation engineering, hydraulic engineering, geotechnical engineering, and environmental engineering, the role of AI in digitalization cannot be overstated. Machine learning techniques, in coordination with remote cameras and unmanned aerial vehicles (UAVs), provide promising non-contact solutions to civil infrastructure condition assessment in structural engineering. Furthermore, in the transportation field, computer vision-based condition management techniques are used on railway networks. These approaches may be used to obtain data from computers for the purpose of tracking rail conditions [140]. The use of AI in the modeling of semi-arid area infiltration is important in hydraulic engineering. Infiltration is vital to stream flow, groundwater recharge, subsurface flow, and the quality and quantity of surface and subsurface water. In addition, landslides and slope failures are serious issues because they can result in the loss of life and property [141]. As a result, slope stability analysis is one of the most critical geotechnical engineering issues. Slope failures, like most geotechnical problems, are complex. The slope stability research had evolved in tandem with advances in computational geotechnical engineering [142; 143]. Furthermore, rein \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sr No.** & **Applications of AI** & **Structure Engineering** & \begin{tabular}{c} **Construction Engineering and** \\ **Management** \\ \end{tabular} & \begin{tabular}{c} **Transportation Engineering** \\ **Engineering** \\ \end{tabular} & \
\begin{tabular}{c} **Hydraulic** \\ **Engineering** \\ \end{tabular} &
\begin{tabular}{c} **Geotechnical Engineering** \\ \end{tabular} \\ \hline \hline
1 & Developing model of steel structure using AI and & ✓ & & & & \\ \hline
2 & Nano-material with an artificial carbonate can be used in & & & & & \\  & nano-crystalline & ✓ & & & & \\ \hline
3 & Artificial neural network & & & & ✓ & \\ \hline
4 & To ensure irrigation and application of pesticides and & & & ✓ & \\  & hebicides are applied more effectively & & & ✓ & \\ \hline
5 & Slope stability & & & & ✓ \\ \hline
6 & Optimization of water demand forecasting & & & ✓ & \\ \hline
7 & AI focused on edge computing irrigation systems & & ✓ & & \\ \hline
8 & Deep neural assessment of friction angle clay & & & ✓ & \\ \hline
9 & Forecasting daily lake level & & & ✓ & \\ \hline
10 & Bio-inspired computational intelligence & & ✓ & & \\ \hline
11 & Innovation management and machine learning approach & ✓ & & & \\ \hline
12 & Forecasting to monthly discharge time series & & & ✓ & \\ \hline
13 & Pile foundation machine learning approach & & & ✓ & \\ \hline
14 & AI approaches for management risk assessment & ✓ & & & \\ \hline
15 & Multivariate transportation problem and its & & & ✓ & \\  & implementation & & & ✓ & \\ \hline
16 & Vehicle traffic load prediction & & & ✓ & \\ \hline
17 & Analysis and design of sustainable structures & ✓ & & & \\ \hline
18 & Neural network approaches for cost estimation & ✓ & & & \\ \hline
19 & Multi-agent system for traffic & & & ✓ & \\ \hline
20 & Structural health monitoring & ✓ & & & \\ \hline
21 & Investigating the soil properties & & & & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of findings of AI in civil engineering. forcement learning is a broad class of optimum control systems that increase performance in a highly dynamic, stochastic environment by estimating value functions obtained from experience, modeling, or search.