Hybrid Grey Wolf Optimization-Based Gaussian Process Regression Model for Simulating Deterioration Behavior of Highway Tunnel Components

Characteristics of Vibration Waves Measured in Concrete Lining of Excavated Tunnel during Blasting in Adjacent Tunnel

The effect of a blasting vibration from an excavating tunnel on an adjacent excavated tunnel is of great importance for the stability and security of twin tunnels. Due to the relatively small distance between the tunnel face of the excavating tunnel and the concrete lining of the excavated tunnel, the impact of blasting could be significant and should be considered in a practical project. In order to control the blasting scales during the excavation of one tunnel and minimize the effect of blasting on the adjacent one, research based on field-blasting tests performed on twin tunnels is presented in this study. The particle velocities on the concrete lining of the excavating tunnel caused by blasting from the adjacent excavated tunnel were measured and analysed during six rounds of blasts. According to the measured vibration waves, it was clear that the peak particle velocity (PPV) from each blast was always induced by cut blasting, therefore, the maximum vibration due to each blast was mainly dependent on cut blasting. The measured maximum PPV for all the blasts was 15.55 cm/s, corresponding to a maximum tensile stress of 1.44 MPa observed on the concrete lining, which was smaller than the tensile strength of the concrete lining, in accordance with the one-dimensional elastic-wave theory. Moreover, the attenuation of the vibration waves varied in different regions, and they could be utilized to demonstrate the impact characteristics of the blasting; e.g., the particle velocities in the region along the excavating direction were 1.12 to 1.79 times larger than those in the region opposite to the excavating direction, and the difference increased with the increasing distance to the blasting source. The particle velocities on the side of the excavated tunnel close to the excavating tunnel were larger than those on the other side of the excavated tunnel. However, the particle velocities of the two aforementioned regions were similar when the distance between the measuring point and the blasting source was more than 6 m in the longitudinal direction of the tunnels. Furthermore, the measured vibration waves could be used to evaluate and improve the blast designs of tunnelling with the drill-and-blast method.

Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

The effects of developing technology and rapid population growth on the environment have been expanding gradually. Particularly, the growth in water consumption has revealed the necessity of water management. In this sense, accurate flow estimation is important to water management. Therefore, in this study, a grey wolf algorithm (GWO)-based gated recurrent unit (GRU) hybrid model is proposed for streamflow forecasting. In the study, daily flow data of Üçtepe and Tuzla flow observation stations located in various water collection areas of the Seyhan basin were utilized. In the test and training analysis of the models, the first 75% of the data were used for training, and the remaining 25% for testing. The accuracy and success of the hybrid model were compared via the comparison model and linear regression, one of the most basic models of artificial neural networks. The estimation results of the models were analyzed using different statistical indexes. Better results were obtained for the GWO-GRU hybrid model compared to the benchmark models in all statistical metrics except SD at the Üçtepe station and the whole Tuzla station. At Üçtepe, the FMS, despite the RMSE and MAE of the hybrid model being 82.93 and 85.93 m3/s, was 124.57 m3/s, and it was 184.06 m3/s in the single GRU model. We achieved around 34% and 53% improvements, respectively. Additionally, the R2 values for Tuzla FMS were 0.9827 and 0.9558 from GWO-GRU and linear regression, respectively. It was observed that the hybrid GWO-GRU model could be used successfully in forecasting studies.

Critical Infrastructures: Reliability, Resilience and Wastage

By 2050, according to the UN medium forecast, 68.6% of the world’s population will live in cities. This growth will place a strain on critical infrastructure distribution networks, which already operate in a state that is complex and intertwined within society. In order to create a sustainable society, there needs to be a change in both societal behaviours (for example, reducing water, energy or food waste activities) and future use of smart technologies. The main challenges are that there is a limited aggregated understanding of current waste behaviours within critical infrastructure ecosystems, and a lack of technological solutions to address this. Therefore, this article reflects on theoretical and applied works concerning waste behaviours, the reliability/availability and resilience of critical infrastructures, and the use of advanced technologies for reducing waste. Articles in the Scopus digital library are considered in the investigation, with 51 papers selected by means of a systematic literature review, from which 38 strains, 86 barriers and 87 needs are identified, along with 60 methods of analysis. The focus of the work is primarily on behaviours, barriers and needs that create an excess or wastage.