# An Adapted Model of Cognitive Digital Twins for Building Lifecycle Management
## 4 Evaluation of the Proposed CDTsBLM Model
### Factor Analysis
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & \multicolumn{1}{c}{**Questionnaire Statement**} & \multicolumn{1}{c}{**Mean**} & \multicolumn{1}{c}{**SD**} & \multicolumn{2}{c}{**Factor**} & \multicolumn{1}{c}{**Conbach \(\alpha\)**} & \multicolumn{1}{c}{**Rank**} \\ \hline \multirow{6}{*}{_Questionnaire_} & BLM employs a CDT approach, which allows for a highly effective & \multirow{2}{*}{3.26} & \multirow{2}{*}{1.23} & \multirow{2}{*}{0.703} & \multirow{2}{*}{20} \\  & expanded collaborative process built on AEC industry best & & & & & \\  & practices. & & & & & \\ \cline{2-7}  & Using a BLM framework, users can proactively fix real-time & \multirow{2}{*}{3.49} & \multirow{2}{*}{1.25} & \multirow{2}{*}{0.707} & \multirow{2}{*}{19} \\  & problems. RFIs, submittals, and change orders may be minimized or & & & & & \\  & withdrawn. & & & & & \\ \cline{2-7}  & With BLM, designers can make more intelligent choices in a richer & \multirow{2}{*}{0.710} & \multirow{2}{*}{17} & \multirow{2}{*}{18} & \multirow{2}{*}{18} \\  & data context while maintaining greater control over the final & & & & & \\  & product output. & & & & \\ \cline{2-7}  & BLM is intended to minimize waste by forecasting results correctly, & \multirow{2}{*}{3.65} & \multirow{2}{*}{1.14} & \multirow{2}{*}{0.713} & \multirow{2}{*}{18} \\  & defining possible tension points, and improving procedures. & & & & & \\ \hline \multirow{6}{*}{_Questionnaire_} & CDT offers live data feeds for primary metrics, visualizations, & \multirow{2}{*}{4.21} & \multirow{2}{*}{0.95} & \multirow{2}{*}{0.842} & \multirow{2}{*}{8} \\  & models, and scenario generation applications. & & & & & \\ \cline{2-7}  & CDT integrates cognitive components into current process & \multirow{2}{*}{4.34} & \multirow{2}{*}{0.92} & \multirow{2}{*}{0.868} & \multirow{2}{*}{3} \\  & management structures, helping them self-organize and respond to & & & & \\ \cline{2-7}  & unpredictable activities. & & & & & \\ \cline{2-7}  & CDT models aid in decision-making for complex systems, including & \multirow{2}{*}{4.32} & \multirow{2}{*}{1.16} & \multirow{2}{*}{0.864} & \multirow{2}{*}{4} \\  & physical actors. & & & & & \\ \hline \multirow{6}{*}{_Questionnaire_} & CDT is a valuable monitoring and control mechanism that helps in overall system optimization. & \multirow{2}{*}{4.38} & \multirow{2}{*}{0.94} & \multirow{2}{*}{0.876} & \multirow{2}{*}{2} \\  & The connectivity of real-time data allows for fast reporting and data & \multirow{2}{*}{4.12} & \multirow{2}{*}{0.92} & \multirow{2}{*}{0.824} & \multirow{2}{*}{13} \\  & explosion, enabling deep data analytics. & & & & & \\ \cline{2-7}  & In the IoT lifecycle, virtual model assets are needed to identify, & \multirow{2}{*}{4.04} & \multirow{2}{*}{0.89} & \multirow{2}{*}{0.808} & \multirow{2}{*}{14} \\  & detect, and address dependencies across domains in the system, & \multirow{2}{*}{14} & \multirow{2}{*}{0.829} & \multirow{2}{*}{10} \\  & subsystems, and components. & & & & \\ \cline{2-7}  & IoT system architecture allows for simple connectivity, & \multirow{2}{*}{4.19} & \multirow{2}{*}{0.93} & \multirow{2}{*}{0.838} & \multirow{2}{*}{10} \\  & communication, and control across domain-specific applications. & & & & & \\ \hline \multirow{6}{*}{_Questionnaire_} & As a hybrid architecture, IoT connects the physical and virtual & \multirow{2}{*}{4.23} & \multirow{2}{*}{0.91} & \multirow{2}{*}{0.846} & \multirow{2}{*}{6} \\  & worlds. & & & & & \\ \hline \multirow{6}{*}{_Questionnaire_} & Learning introduces new expertise to current data, models, and & \multirow{2}{*}{3.96} & \multirow{2}{*}{1.12} & \multirow{2}{*}{0.792} & \multirow{2}{*}{16} \\  & approaches to learn more reliable models from existing datasets. & & & & & \\ \cline{2-7}  & Cognitive aspects help benefit from past process data and incidents & \multirow{2}{*}{0.813} & \multirow{2}{*}{15} & \multirow{2}{*}{12} \\  & to predict and provide the best feasible solutions for unwanted & \multirow{2}{*}{4.01} & \multirow{2}{*}{1.15} & \multirow{2}{*}{0.802} & \multirow{2}{*}{0.813} & \multirow{2}{*}{15} \\  & events. & & & & & \\ \cline{2-7}  & Hybrid models that self-learn and have proactive cognitive skills. & \multirow{2}{*}{4.13} & \multirow{2}{*}{0.95} & \multirow{2}{*}{0.826} & \multirow{2}{*}{12} \\  & The real and the virtual space can reason and learn about stimuli, & & & & & \\ \cline{2-7}  & interaction, aim, and time. & & & & & \\ \hline \multirow{6}{*}{_Questionnaire_} & Real-time analysis for data-driven models augmented by cognitive & \multirow{2}{*}{4.45} & \multirow{2}{*}{0.96} & \multirow{2}{*}{0.892} & \multirow{2}{*}{1} \\  & resources is conducted to facilitate decision-making and improve & \multirow{2}{*}{1.18} & \multirow{2}{*}{0.844} & \multirow{2}{*}{0.856} & \multirow{2}{*}{7} \\  & learning, optimization, and reasoning. & & & & \\ \cline{2-7}  & Dynamic process optimization techniques contribute to an & \multirow{2}{*}{4.22} & \multirow{2}{*}{1.18} & \multirow{2}{*}{0.856} & \multirow{2}{*}{7} \\  & environment in which digital structure and behavior are continually & \multirow{2}{*}{4.25} & \multirow{2}{*}{1.19} & \multirow{2}{*}{0.850} & \multirow{2}{*}{5} \\  & evolved. & & & & \\ \cline{2-7}  & Process optimization is conducted to support and manipulate & \multirow{2}{*}{4.25} & \multirow{2}{*}{1.19} & \multirow{2}{*}{0.850} & \multirow{2}{*}{5} \\  & physical structures based on CDT models and real-time data. & & & & & \\ \cline{2-7}  & Assessing optimization scenarios in the virtual environment before & \multirow{2}{*}{4.20} & \multirow{2}{*}{1.20} & \multirow{2}{*}{0.840} & \multirow{2}{*}{9} \\  & bringing them into effect in the real world. & & & & \\ \hline \hline \end{tabular}
\end{table}