## 4 Results
### Literature Review
#### 4.2.8 Uncertainty PD  
[PERSON] et al. [118] provided an ML-based prediction system to forecast the risk of a task in a software project being delayed. Experimental results show that the collective classification method that this paper proposes significantly outperforms traditional approaches. Apart from that, the modeling of the probability distribution of project task duration by means of fuzzy expert estimates and fuzzy numbers is suggested in [119] which achieves more accurate estimations than existing methods under information uncertainty. [PERSON] et al. [120] have designed and developed a well-performing project risk management tool that provides risk identification, analysis, response, and monitoring by means of an ML approach via cased based reasoning. Although it employs risk-related knowledge from past construction projects, it may be applicable in other sectors with minor changes. Furthermore, the literature review paper [121] revealed the popularity of hybrid AI methods, such as fuzzy ANNs, fuzzy-analytical network processing, and fuzzy-simulation for risk assessment in construction projects. However, as stated in the previous study, a hybrid approach of fuzzy logic and an extended form of Bayesian belief network would better capture complexity-risk interdependencies under uncertainty moderating cost overruns. Moreover, [PERSON] et al., [122] provided an ML-based approach for developing leading indicators that classify sites in terms of their safety risk in construction projects. Five ML algorithms were used for training the sets (decision trees, RF, Logistics Regression, k-nearest neighbour, and SVM). Results show that RF gives the best prediction performance. In addition, an ACO model for planning a safe construction site layout that considers different safety objective functions was introduced in [123]. Moreover, the article [124] presented a stability prediction of construction projects based on ML algorithms. Furthermore, a framework based on fuzzy logic for digitalized PM was presented in [125]. It is applied for risk management in a railway project in Africa. Furthermore, [PERSON] et al. [126] introduced a fuzzy GA-based SVM model that gives accurate prediction of PPP dispute resolution outcomes in construction. Moreover, [PERSON] et al. [127] proposed an optimized hybrid AI method that integrates a fast messy GA with an SVM to forecast dispute propensity in PPP construction projects; GA-SVM provides better prediction accuracy than other baseline models. Furthermore, [PERSON] et al. [128] asserted the suitability of the multilayer perceptron NN approach for predicting the outcome of a dispute in construction projects using data from variation claims in India. Moreover, [PERSON] et al. [129] provided an ANN-based decision support system to predict project performances for project selection, which relates critical success factors with project success by classifying the level of a project's riskiness via the experiences of project managers. Furthermore, the article [130] employs fuzzy decision making for project selection under uncertainty. [PERSON] et al. [131] dealt with the application of AI-based NLP in PM, with a special focus on construction projects. In such a context, NLP, promising with an ANN approach, is proposed to efficiently extract knowledge from databases on construction accidents and translate it into useful data for safety risk management and also to predict risks in the bidding process of a project, by analyzing the uncertainty in the bidding document and extracting the influencing factors from it for bidding/tender risk forecasting. In addition, the explorative study [132] presented some AI applications to manage megarojects, most of them being related to the management of workers' safety and health. Predicting the presence of a disease, a risk condition, or the need for repairing heavy equipment are the benefits of ML applicable to megarojects.