## 1 Introduction
Although mathematical modeling methods need a significant amount of time and effort, they can suitably help in minimizing time-space conflicts in construction sites. 3. Building Information Modeling (BIM)-based Methods: [PERSON] and [PERSON] [17] linked a 3D BIM model with the construction schedule and space requirements as a 4D model to simulate space conflicts for a multi-story construction project. [PERSON] and [PERSON] [18] proposed a 4D-BIM framework for conflict management in construction projects. In their method, time-space conflicts are recognized by calculating bounding boxes for clashing objects. [PERSON] and [PERSON] [19] introduced a life-cycle approach for workspace modeling and planning. In their study, no scientific method is provided for the rapid and automatic generation of space requirements. As a BIM-based active simulation system, [PERSON] et al. [20] implemented a Genetic Algorithm (GA) procedure for an alternative schedule to minimize the levels of simultaneous interference of schedule workspaces. [PERSON] et al. [21] proposed a 4D-BIM optimization framework to find a plan with minimum workspace interference. This method automatically moves the activities within their CPM Total Floats (TF) to decrease workspace conflicts. [PERSON] et al. [22] used an Industry Foundation Class (IFC) compliant 4D tool for workspace management. In their interactive resolution method, time-space conflicts are identified and visually represented. Using a 4D-BIM method, [PERSON] et al. [8] proposed a workspace planning framework to identify workspace requirements, represent workspace occupation, and detect time-space conflicts. [PERSON] et al. [23] proposed a method for managing time-space conflicts in construction projects by combining 5D BIM and time-cost tradeoff analysis. [PERSON] et al. [24] introduced a 4D-BIM time-space system detecting conflicts regarding labor movement. Recently, [PERSON] et al. [1; 25] introduced a method using BIM technologies and immersive VR applications for manual activity workspace planning. As a result, they evaluated the historic passive and active data from the worker's immersive VR activity simulation and improved workspace planning. 4. Neural network based path planning technique: A Neural Network-Based Navigation Approach for Autonomous Mobile Robot Systems involves the use of neural networks, which are machine learning models inspired by biological brain structures, to assist in navigating mobile robots. It has emerged as a promising method for solving the path planning problem in robotics, which is essentially about finding the optimal and safest path from a starting point to a destination [26; 27]. [PERSON] et al. [28] discusses the importance of obstacle avoidance in navigation problems and presents a neural network model that was trained using human decisions on motion types in various obstacle scenarios. The model was tested in a mobile robot navigation simulator and showed an accuracy level close to 90%. [PERSON] et al. [29] propose a new approach to mobile robot path planning in dynamic environments using Q-learning. The authors propose a new definition for the state space to limit the number of states and reduce the size of the Q-table, improving the speed of the navigation algorithm. The paper also discusses the challenges of path planning in mobile robot navigation. For instance, changes in the environment can make path planning difficult. The robot may need to constantly update its plan as new obstacles appear or existing obstacles move. [PERSON] et al. [30] propose a new approach to solve the problem of autonomous movement of robots in environments that contain both static and dynamic obstacles. The solution uses Q-learning and a neural network planner to solve path planning problems. The algorithm has been tested in both virtual reality and on a real mobile robot for experimental validation. Although several studies have been conducted in the area of time-space conflict management, the following gaps exist in the literature.