# An Adapted Model of Cognitive Digital Twins for Building Lifecycle Management
## 6 Conclusions  
Construction projects and their data from the first stage to the last day of AEC projects are becoming huge and more complex to gather and manage. It is becoming exceedingly difficult, if not impossible, to identify and collect the right data sets and put them in the proper context to enable the optimization of the system. However, with help from a DT that can sense, reason, and act, such intelligent systems will help projects' stakeholders make the right decisions or autonomously trigger the right actions in the digital or physical world. Increasing complexity in terms of the DT becomes apparent when looking at the various application streams and their need for precise and real-time data. The DTs' highest level is the CDT connected with the top-level cognitive engineering maturity, including AI and ML. This article introduced the newly adapted CDT paradigm, including BLM's capabilities in cognition, analytics, and optimization for construction 4.0. The most significant advantage of cognition is the ability to solve problems preventively unknown. The CDT provides a toolkit for optimizing based on its cognitive components that enables CDT to carry out optimization tasks and delivers valuable results that other CDTs or process actors consume. Industry practitioners have examined the technical framework of CDTsBLM \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{4}{c}{**Building Lifecycle Management**} \\ \hline  & **Process** & **Opportunities** & **Challenges** \\ \hline \multirow{4}{*}{Cognition} & Sensing complex and & & \\  & unpredicted behavior, and & & \\  & reasoning and insights from & & Creating cognitive artificial & IoT network in terms of \\  & real-time processing, where & intelligence from raw data & scalability, security, data loss, \\  & cases, knowledge, and & and maximizing monitoring & competent human resources, lack \\  & experience interoperate to & accuracy. & of enabling technologies \\  & facilitate to comprehend and & & \\  & control the progress & & \\ \hline \multirow{4}{*}{Analytics} & Monitoring, refining, and & Applying cognitive analytics & Lack of fully automated DT \\  & utilizing the flow of incoming & through data-enriched & platform, various types of \\  & real-time data from various & simulations enhanced by & captured data, experienced staff, \\  & sources (the physical & cognitive computing insights & IT infrastructure, trust with \\  & counterparts and sensors) & and predictive analytics & respect to data, privacy and \\ \hline \multirow{4}{*}{Self-Learning} & Extracting knowledge from & Applying intelligent and & \\  & aggregated data, & self-learning planning and & Integration of transfer learning \\  & automatically learning from & control to improve the & algorithms, lack of comprehensive \\  & data, identifying patterns, and & accuracy of monitoring & modeling language, data \\  & making decisions. & through iterative updating. & availability, validation of data \\ \hline \multirow{4}{*}{Optimization} & For schedule design, task & Combining reasoning and & Uncertainty quantification \\  & allocation, and workflow & optimization & algorithms, multi-objective \\  & optimization of the relevant & for establishing planning and & algorithms, complex environment \\  & construction process and & design, construction schemes & modeling, large-scale \\  & resource allocation & based on analytic algorithms & computation \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Sample of Cognitive Digital Twins cognition, analytics, and optimization processes in Building Lifecycle Management. and taken full advantage of additional CDT capabilities, including construction schedules, preventive maintenance, and other goals, in the traditional DT sense. The benefits of implementing the CDT concept in the construction industry are intended by opening the optimization tool kit inside the CDT and enhancing real-time or almost real-time choices by interplaying optimization and simulation.