Benthic megafauna and CO2 bubble dynamics observed by underwater photography during a controlled sub-seabed release of CO2

In 2012, a controlled sub-seabed release of carbon dioxide (CO2) was conducted in Ardmucknish Bay, a shallow (12 m) coastal bay on the west coast of Scotland. During the experiment, CO2 gas was released 12 m below the seabed for 37 days, causing significant disruption to sediment and water carbonate chemistry as the gas passed up through the sediment and into the overlying water. One of the aims of the study was to investigate how the impacts caused by leakage from geological CO2 Capture and Storage (CCS) could be detected and quantified in the context of natural heterogeneity and dynamics. To do this underwater photography was used to analyze (i) the benthic megafaunal response to the CO2 release and (ii) the dynamics of the CO2 bubble streams, emerging from the seabed into the overlying water column. The frequently observed megafauna species in the study area were Virgularia mirabilis (Cnidaria), Turritella communis (Mollusca), Asterias rubens (Echinodermata), Pagurus bernhardus (Crustacea), Liocarcinus depurator (Crustacea), and Gadus morhua (Osteichthyes). No discernable abnormal behavior was observed for these megafauna, in any of the zones investigated, during or after the CO2 release. Time-lapse photography revealed that the intensity and presence of the CO2 bubble plume was affected by the tides, with the most active bubbling seen at low tides and the larger hydrostatic pressure at high tide suppressing CO2 bubbling from the seabed.

Artificial Intelligence Model for Flowable Concrete Mixtures Used in Underwater Construction and Repair

This study explores using artificial neural networks to predict the rheological and mechanical properties of underwater concrete (UWC) mixtures and to evaluate the sensitivity of such properties to variations in mixture ingredients. Artificial neural networks (ANN) mimic the structure and operation of biological neurons and have the unique ability of self-learning, mapping, and functional approximation. Details of the development of the proposed neural network model, its architecture, training, and validation are presented in this study. A database incorporating 175 UWC mixtures from nine different studies was developed to train and test the ANN model. The data are arranged in a patterned format. Each pattern contains an input vector that includes quantity values of the mixture variables influencing the behavior of UWC mixtures (that is, cement, silica fume, fly ash, slag, water, coarse and fine aggregates, and chemical admixtures) and a corresponding output vector that includes the rheological or mechanical property to be modeled. Results show that the ANN model thus developed is not only capable of accurately predicting the slump, slump-flow, washout resistance, and compressive strength of underwater concrete mixtures used in the training process, but it can also effectively predict the aforementioned properties for new mixtures designed within the practical range of the input parameters used in the training process with an absolute error of 4.6, 10.6, 10.6, and 4.4%, respectively.

Factorial Design of Modelling Mix Proportion Parameters of Underwater Composite Cement Grouts

There is an increasing need to identify the rheological properties of cement grout using a simple test to determine the fluidity, and other properties of underwater applications such as washout resistance and compressive strength. This paper reviews statistical models developed using a factorial design that was carried out to model the influence of key parameters on properties affecting the performance of underwater cement grout. Such responses of fluidity included minislump and flow time measured by Marsh cone, washout resistance, unit weight, and compressive strength. The models are valid for mixes with 0.35–0.55 water-to-binder ratio (W/B), 0.053–0.141% of antiwashout admixture (AWA), by mass of water, and 0.4–1.8% (dry extract) of superplasticizer (SP), by mass of binder. Two types of underwater grout were tested: the first one made with cement and the second one made with 20% of pulverised fuel ash (PFA) replacement, by mass of binder. Also presented are the derived models that enable the identification of underlying primary factors and their interactions that influence the modelled responses of underwater cement grout. Such parameters can be useful to reduce the test protocol needed for proportioning of underwater cement grout. This paper attempts also to demonstrate the usefulness of the models to better understand trade-offs between parameters and compare the responses obtained from the various test methods that are highlighted.