## 4 Results
### Literature Review
#### 4.2.4 Planning PD
Different AI techniques (Artificial NN, GA, and fuzzy logic) were applied in [46] using data from past NASA projects, concluding that ANN methods give the best performance. An effective ML ensemble model composed of SVM, NN, and Generalized Linear Models is provided in [32]. In addition, [PERSON] [47] investigated the effect of noisy domains on the learning accuracy of eight ML algorithms (SVM and ANN among them) and statistical pattern recognition algorithms. The study derived a solution from a probabilistic perspective that improves prediction for software effort corrupted by noise with better accuracy. The assignment of human resources to project tasks that employ AI heuristics is another appearing topic in the literature [48; 49; 50; 51]. The works [42] and [48] applied feasible ACO algorithms (improved Max-Min ACO and Hyper-Cube ACO, respectively) for worker-task assignment in software projects to minimize the project duration. The authors in [51] presented a novel ACO rescheduling strategy for human resource assignment to eliminate delays. Findings revealed that the new adaptative ACO outperforms common ACO and GAs. A tabu search algorithm was employed in [50] so as to solve the resource management problem for multi-unit construction projects. The case study analyzed manifested a reduction of 50% in the project execution time when using that algorithm. Furthermore, aimed at allocating the suitable software developers for a particular project, [PERSON] et al. [52] proposed a DL-based approach to determine the software developer's coding expertise by analyzing prior written source code. Three DL methods were developed, trained, and evaluated in the study: Long Short-Term Memory (LSTM), one-dimensional convolutional NN, and a hybrid model that combines LSTM and the previous NN. Evaluation results indicate that LSTM outperforms the other two models, which achieves good accuracy levels. Moreover, in the paper [53], evolutionary and hybrid evolutionary algorithms that are based on GA's principles are implemented for resource leveling in a ship construction project. Experimental results show that hybrid approaches provide slightly better behavior. Furthermore, a novel algorithm based on Sonar inspired optimization to address benchmark and resource-leveling problems was introduced in [54]. Evaluation findings revealed that it provides better performance than hybrid GAs. [PERSON] and [PERSON] [55] proposed a well-performing threshold-based hyperheuristic for solving construction resource levelling and allocation. In addition, [PERSON] and [PERSON] [56] employed ACO in a metaheuristic approach to solve resource leveling problems. A real-time project was used to verify the efficiency of their model. The results were near-global optimum solutions. Moreover, [PERSON] et al. [57] presented a planning optimization system using multi-objective GA, namely the NSGA-II, for road pavement rehabilitation. The model was able to choose the optimal allocation of heavy equipment with costs and duration objectives. The study [58] demonstrates that SVMs and ANN ensemble techniques provide better results than single ANN models for predicting project costs. Well-performing models for construction cash flow forecasting were proposed in [59; 60], which employ least squares SVM and a fuzzy SVM-GA ensemble, respectively. More recently, [PERSON] et al. [61] proposed an AI-based hybrid model, named symbiotic organisms search-optimized (SOS) NN LSTM, that accurately predicts the cash flow of construction projects; this novel DL-based model uses data from completed construction projects in Taipei. Moreover, [PERSON] et al. [62] focused on the way construction projects can minimize the building cost and materials wastages based on a GA-SVM inference system. Furthermore, a decision support model for dispatching construction machines was presented in [63], where a rule-based inference engine determines the most proper construction machine type considering both economic and technical criteria.