## 4 Results
### Literature Review
#### 4.2.7 Measurement PD  
Several works dealt with AI-enabled project duration forecasting in Earned Value Management (EVM) context ([96; 97; 98; 99; 100]). Different ML algorithms, SVM in [100], k-nearestneighbor in [98], and others such as RF and decision tree in [99], were compared with the best performing EVM methods and it was concluded that AI techniques give better prediction than traditional EVM methods if the training and test sets are similar. [PERSON] et al. [96] suggested a fuzzy NN method (Locally Linear Neuro-fuzzy), whose accuracy, relevance, and applicability of the proposal were demonstrated via testing Iranian IT projects. The new ensemble learning model introduced in [97] was validated using data from real projects, showing that it notably outperforms well-known estimators. Moreover, [PERSON] et al. [101] proposed a robust and reliable tool that predicts delay levels in construction projects. For that aim, a hybrid AI model that combines GA with the RF-ML technique is employed, which is trained with data from past construction projects in Iraq. Furthermore, [PERSON] et al. [102] suggested an original scatter search algorithm that applies the total weighted tardiness flow shop problem in construction PM, which considers technological and organizational restrictions; it produces better results than tabu search. Apart from that, a tool for recognizing the activity of workers in construction projects was presented in [103]. When smartphone body movements are capturedML techniques can then be applied to determine the type of activity. What is more, [PERSON] et al. [104] introduced a model that utilizes vision-based action recognition of construction workers using ML. SVMs are integrated with action learning classification, providing a notable accuracy enhancement with respect to other state-of-the-art solutions. A case-based reasoning model to forecast the cost index of overhead transmission lines was provided in [105]. More recently, the study [106] introduced a new DL-based algorithm (LSTM NN) for highway construction cost index prediction, which provides precise forecasts in the short, medium, and long term. The model is trained with highway construction cost indexes from the Texas Department of Transport. In addition, the works [96; 100] also presented successful cost prediction in EVM, applying fuzzy NN and SVM, respectively. What is more, [PERSON] et al. [107] used L-R fuzzy numbers to formulate EVM in vagueness environments for better planning, which provides efficient cost forecasting. Moreover, the research in [108] developed a precise system that adopts DL with convolutional NN computer vision for the automatic remote monitoring of power substation construction management. Moreover, [PERSON] et al. [109] presented a hybrid AI model that accurately predicts the productivity of a construction project, which combines ML-based least square SVM, SOS, and feature selection techniques. Datasets from two Canadian past projects are utilized to build such a forecasting model. In addition, [PERSON] et al. [110] suggested an emotion-based automatic ML approach to predict the priority of a bug report. Moreover, several studies use AI techniques for the Project Monitoring and Controlling purpose:  
[PERSON] et al. [111] applied an enhanced fuzzy cognitive maps approach for project monitoring that integrates diagnosis, decision, and prediction during project evaluation. The validation of the proposed model is performed by evaluating project records related to the PM knowledge areas of scheduling, cost, resource, quality, and procurement from the Research Database Repository of the University of Informatics Sciences of Cuba. Performance results indicated that the novel proposal outperforms fuzzy cognitive maps and neutrosophic cognitive maps techniques and experts expressed their satisfaction with the outcome obtained.