- Developing six hybrid machine learning models based on gaussian process regression and meta-heuristic optimization algorithms for prediction of duration and cost of road tunnels construction. Dans: Tunnelling and Underground Space Technology, v. 130 (décembre 2022). (2022):
- Extreme learning machine evolved by fuzzified hunger games search for energy and individual thermal comfort optimization. Dans: Journal of Building Engineering, v. 60 (novembre 2022). (2022):
- Optimized machine learning modelling for predicting the construction cost and duration of tunnelling projects. Dans: Automation in Construction, v. 139 (juillet 2022). (2022):
- Predicting construction time and cost of tunnels using Markov chain model considering opinions of experts. Dans: Tunnelling and Underground Space Technology, v. 116 (octobre 2021). (2021):
- Machine learning forecasting models of disc cutters life of tunnel boring machine. Dans: Automation in Construction, v. 128 (août 2021). (2021):
- Presenting the best prediction model of water inflow into drill and blast tunnels among several machine learning techniques. Dans: Automation in Construction, v. 127 (juillet 2021). (2021):
- Dynamic reduction of time and cost uncertainties in tunneling projects. Dans: Tunnelling and Underground Space Technology, v. 109 (mars 2021). (2021):
- Forecasting sidewall displacement of underground caverns using machine learning techniques. Dans: Automation in Construction, v. 123 (mars 2021). (2021):
- Forecasting maximum surface settlement caused by urban tunneling. Dans: Automation in Construction, v. 120 (décembre 2020). (2020):
- Decision-making in tunneling using artificial intelligence tools. Dans: Tunnelling and Underground Space Technology, v. 103 (septembre 2020). (2020):
- Updating ground conditions and time-cost scatter-gram in tunnels during excavation. Dans: Automation in Construction, v. 105 (septembre 2019). (2019):