The Architecture, Engineering, and Construction (AEC) industry is known to be overwhelmed with resource planning, risk management, and logistic challenges, resulting in design defects, project delivery delays, cost overruns, and contractual disputes. These challenges have instigated research in the application of advanced machine learning algorithms such as Deep learning (DL) to help with diagnostic and prescriptive analysis of causes and preventive measures. Construction 4.0 develops through continuous innovations towards digitalization and intelligence, in order to realize a considerable boost in automation, productivity, and reliability. The Digital Twins, as the next level of planning and process control and automation towards Construction 4.0, will incorporate cognitive features that enable sensing complex and unpredicted behavior and reason about dynamic strategies for process optimization to support decision-making. However, there is still a lack of awareness regarding the real impact of DT integration, DL, and IoT, all connected to self-learning hybrid models with proactive cognitive capabilities for smart planning and construction. This study investigates the potential integration of DT and DL to facilitate smart planning and construction through an explorative analysis to identify and close this gap. Data were collected from global industry experts in a mixed approach with interviews, focus groups, and a survey focusing on the applicability and interoperability of DL integrated DT with decision-support capabilities for process optimization. Based on the results of quantitative and qualitative analyses, a conceptual model of the framework has been developed. The evaluations support that the DL integrated DT model will incorporate cognitive abilities to detect complex and unpredictable actions and reasoning about dynamic process optimization strategies to support decision-making in smart planning and construction. / Pre-study for SPARK-SBE potential project