Despite many improvements in safety management, the construction industry still has the highest potential for occupational injuries including High Severe (HS) work events, which result in injuries or fatalities, and Low Severe (LS) work events, which cause near misses or nonserious injuries. The analysis of incidents is highly dependent on the quality of records. Problems in recording and the heterogeneity of incident data may create conflicts while analyzing the relationship between attributes. The objective of the study was to develop a novel model to predict the outcomes of construction incidents using Latent Class Clustering Analysis (LCCA) and Artificial Neural Networks (ANNs) and determine necessary preventative actions. ANN has been used for many years to investigate the nonlinear relation between attributes and generate a logic between them. Herein, ANN was used to perform severity analyses of incidents utilizing real data, which were collected from various construction sites anonymously. Many factors affect the performance of ANN, including the size of the input and the heterogeneity of data. LCCA was used to seek out better performance and accuracy in ANN applications by reducing the heterogeneity of the incidents. By applying LCCA, attributes that possess different probabilities were clustered together and put into the ANN model. Then, the study concluded by providing a necessary preventative measure according to the result of incidents forecasted in advance. The research has two significant contributions. First, the hybrid model revealed promising results as the performance of the ANN-based predictive model was enhanced by addressing the heterogeneity of data. Second, the study presented professionals with practical preventative actions to avoid construction incidents according to the results of prediction.

The line-of-balance (LOB) method of scheduling is well suited to projects that are composed of activities of a linear and repetitive nature. The objective of this study is to set down the basic principles that can be used in the development of a computerized LOB scheduling system that overcomes the problems associated with existing systems and creates solutions to problems encountered in the implementation of repetitive-unit construction. The challenges associated with LOB scheduling include developing an algorithm that handles project acceleration efficiently and accurately, recognizing time and space dependencies, calculating LOB quantities, dealing with resource and milestone constraints, incorporating the occasional nonlinear and discrete activities, defining a radically new concept of criticalness, including the effect of the learning curve, developing an optimal strategy to reduce project duration by increasing the rate of production of selected activities, performing cost optimization, and improving the visual presentation of LOB diagrams.

The outcome of construction litigation depends on a large number of factors. To predict the outcome of such litigation is difficult because of the complex interrelationships between these many factors. Two attempts are reported in the literature that use, respectively, case-based reasoning (CBR) and artificial neural networks (ANN) to overcome this difficulty. These studies were conducted by using the same 102 Illinois circuit court cases; an additional 12 cases were used for testing. Prediction rates of 83% in the CBR study and 67% in the ANN study were obtained. In this paper, CBR and ANN are compared, and their advantages and disadvantages are discussed in light of these two studies. It appears that CBR is more flexible when the system is updated with new cases, has better explanation facilities, and handles missing data and a large number of features better than ANN in this domain. If the use of CBR and ANN is understood better and if, as a result, the outcome of construction litigation can be predict...

In this study, neural networks were used to predict the outcome of construction litigation. Disagreements between the owner and the contractor can arise from such considerations as interpretation of the contract, changes made by the owner, differing site conditions, acceleration and suspension of work, and so forth. When there are disagreements between the contractor and the owner, the result is the inefficient use of resources and higher costs for both the owner and the contractor, as well as damage to the reputation of both sides. Neural networks may help to predict the outcome of construction claims that are normally affected by a large number of complex and interrelated factors. Data composed of characteristics of cases and circuit and appellate court decisions were extracted from cases filed in Illinois appellate courts in the last 12 years. A network was trained using these data, and a rate of prediction of 67% was obtained. If the parties to a dispute know with some certainty how the case would be resolved if it were taken to court, it is believed that the number of disputes could be reduced greatly.