# Analysing Near-Miss Incidents in Construction: A Systematic Literature Review
## 4 Results
### Methods Used to Analyse the Information and Figures That Have Been Obtained
#### 4.3.2 Analysis Using Artificial Intelligence (AI)  
In recent years, research involving the application of artificial intelligence (AI) for the prevention of near-miss events on construction sites has made significant advances. [PERSON] et al. [71] employed machine learning algorithms to analyse safety data, thereby facilitating the identification of patterns and the prediction of potential hazards. A natural language processing (NLP) model was developed for the automatic identification of key event attributes. [PERSON] et al. [74] concentrated their efforts on the construction of artificial neural networks capable of forecasting the safety levels observed on construction sites, based on the data collected from operational sources. [PERSON] et al. [75] explored the potential of smartphones with integrated sensors as a means of data collection. They demonstrated that, when coupled with an ANN, these devices can effectively detect and identify hazardous situations involving falls. By analysing data from accelerometers, the system is able to identify precise motion patterns that could result in an accident, which is crucial for enabling early intervention and enhancing safety on construction sites. A comparable methodology was proposed by [PERSON] et al. [64], who devised a system for categorising near misses, such as slips and trips, through the utilisation of an artificial neural network (ANN). The system incorporates data from smart sensors and an SSN system. The system has been trained to recognise near misses on construction sites. The study concentrated on the collection of data utilising a tri-axial accelerometer embedded in a smartphone, thereby enabling precise real-time monitoring and energy analysis of worker movements. [PERSON] and [PERSON] [76] developed a technology for the automatic classification of safety reports, which allows for faster responses to reports of hazardous situations. [PERSON] et al. [77] and [PERSON] et al. [53] concentrated on text mining as a means of analysing safety reports, thus facilitating a more profound comprehension of the context and causes of potential hazards. To enhance the analysis of reports on near misses on construction sites, an advanced approach based on deep learning utilising Bidirectional Encoder Representations from Transformers (BERT) was developed. This approach enables the efficient and accurate automatic text classification. [PERSON] et al. [72] used advanced stochastic analysis techniques and time series modelling. The researchers employed Markov copula methods and Poisson modelling to assess risk, thereby enabling the dynamic updating of accident probabilities based on historical data accumulation and expert knowledge. The Markov copula model represents a methodology for time series modelling that employs copulas to flexibly and accurately model dependencies between variables. The copula model permits the separation of the modelling of marginal distribution functions from the dependencies between variables, with parameters determined analytically from autocorrelation functions. This approach facilitates the inference of probability and enables a natural extension to higher-order Markov models. Hypothesis testing based on the Probability Integral Transformation Theorem (PITT) is employed to assess the adequacy of the model for near-miss events. [PERSON] et al. [78] introduced an innovative blockchain-based system for creating an automatic monitoring and early warning system at construction sites. This system enables more efficient management of near misses and accident prevention. Blockchain is a digital system of data records. The utilisation of blockchain's intrinsic advantages, namely decentralisation and the impossibility of manipulation, has resulted in a reduction in the time required to detect accident-prone events and an enhancement in the reliability and practicality of safety monitoring. [PERSON] et al.