The maximum amount of lateral formwork pressure exerted by self-compacting concrete is essential to design a technically correct, cost-effective, safe, and robust formwork. A common practice of designing formwork is primarily based on using the hydrostatic pressure. However, several studies have proven that the maximum pressure is lower, thus potentially enabling a reduction in the cost of formwork by, for example, optimizing the casting rate. This article reviews the current knowledge regarding formwork pressure, parameters affecting the maximum pressure, prediction models, monitoring technologies and test setups. The currently used pressure predicting models require further improvement to consider several pressures influencing parameters, including parameters related to fresh and mature material properties, mix design and casting methods. This study found that the maximum pressure is significantly affected by the concretes’ structural build-up at rest, which depends on concrete rheology, temperature, hydration rate and setting time. The review indicates a need for more in-depth studies.

Digital transformation of concrete technology is one of the current“hot topics”tackled byboth academia and industry. Thefinal goal is to fully integrate the already existing advancedconcrete technologies with novel sensors, virtual reality, or Internet of things to create self-learning and highly automated platforms controlling design, production, and long-termusage and maintenance of concrete and concrete structures. The digital transformationshould ultimately enhance sustainability, elongate service life, and increase technologicaland cost efficiencies. This review article focuses on up-to-date developments. It explorescurrent pathways and directions seen in research and industrial practices. It indicatesbenefits, challenges, and possible opportunities related to the digital transformation ofconcrete technology.

Despite the advantageous benefits offered by self-compacting concrete, its uses are still limited due to the high pressure exerted on the formwork. Different parameters, such as those related to concrete mix design, the properties of newly poured concrete, and placement method, have an impact on form pressure. The question remains unanswered on the degree of the impact for each parameter. Therefore, this study aims to study the level of impact of these parameters, including slump flow, T500 time, fresh concrete density, air content, static yield stress, concrete setting time, and concrete temperature. To mimic the casting scenario, 2 m columns were cast at various casting rates and a laboratory setup was developed. A pressure system that can wirelessly and continuously record pressure was used to monitor the pressure. Each parameter’s impact on the level of pressure was examined separately. Casting rate and slump flow were shown to have a greater influence on pressure. The results also demonstrated that, while higher thixotropy causes form pressure to rapidly decrease, a high casting rate and high slump flow lead to high pressure. This study suggests that more thorough analysis should be conducted of additional factors that may have an impact, such as the placement method, which was not included in this publication.

Self-Compacting Concrete (SCC) offers favourable properties which help accelerate the casting time, especially in congested reinforced structures but when casting with SCC uncertainty remains a challenge on the behaviour of its formwork pressure. Researchers have introduced several design models to predict pressure and its behaviour. This research aims to assess the design models that have been reported in the literature. The assessment was carried out through a series of rigorous laboratory tests and the results from the tests served as input for the mathematical model evaluation. Twelve concrete columns with 2 m height were cast in the laboratory to study the effect of varying the input parameters in the existing design models. The formwork pressure was documented by a pressure monitoring system, with the capacity to produce instant results for real-time remote monitoring of the pressure development during and after concrete casting. The formwork pressures were calculated according to the current design models and were compared with pressure data acquitted from the laboratory tests. The results showed that the pressure predicted by the design models was typically greater than the pressure observed during the laboratory tests. The DIN18218 design model showed a relatively close approximation of the pressure distribution over the formwork height and casting time. The limitation of the models is observed when the casting rate varies, and models are sensitive to the input parameters. Thus, additional development of the current design models is needed to enable reliable estimations of the pressure, for example, in the case of low and high casting rates. The laboratory tests also showed that high casting rates and high slump flows generate higher pressures whereas higher thixotropy results in faster pressure reduction during construction.

The prediction of formwork pressure exerted by self-compacting concrete (SCC) remains a challenge not only to researchers but also to engineers and contractors on the construction site. This article aims to utilize shallow neural networks (SNN) and deep neural networks (DNN) using Long Short-Term Memory (LSTM) approach to develop a prediction model based on real-time data acquitted from controllable laboratory testing series. A test setup consisting of a two-meter-high column, ø160 mm, was prepared and tested in the laboratory. A digital pressure monitoring system was used to collect and transfer the data to the cloud on a real-time basis. The pressure was monitored during- and after casting, following the pressure build-up and reduction, respectively. The two main parameters affecting the form pressure, i.e., casting rate and slump flow, were varied to collect a wide range of input data for the analysis. The proposed model by DNN was able to accurately predict the pressure behavior based on the input data from the laboratory tests with high-performance indicators and multiple hidden layers. The results showed that the pressure is significantly affected by the casting rate, while the slump flow had rather lower impact. The proposed model can be a useful and reliable tool at the construction site to closely predict the pressure development and the effects of variations in casting rate and slump flow. The model provides the opportunity to increase safety and speeding up construction while avoiding costly and time-consuming effects of oversized formwork.

Forecasting the maximum pressure exerted by cast-in-place self-compacting concrete (SCC) is a major concern for formwork designers, researchers, and site engineers to accurately design the bearing capacity of the formwork and control the casting rate for safe and fast construction. This article aims to utilize the ARX-Laguerre model, which is a data-driven model to forecast the maximum form pressure. A laboratory instrumented setup was used to cast a 2-m column using SCC made with two different types of cement. A pressure system consisting of four sensors was used to document the pressure during casting. The data were sent to the cloud at every 1-min interval for real-time monitoring. The data were used to develop the model. The results demonstrated that forecasting with the ARX-Laguerre model is highly accurate. The model can forecast the maximum pressure exerted by SCC with less complexity. The model performance was also found to be consistent with insignificant differences between actual experimental results and predicted results. With a recursive and straightforward representation, the resulting model, known as the ARX-Laguerre model, ensures the parameter number reduction. Providing fast prediction of the maximum pressure. The model enables formwork designers to forecast the form pressure to design safe formwork and also helps to control the casting rate when SCC is used.

Machine learning techniques have been used in different fields of concrete technology to characterize the materials based on image processing techniques, develop the concrete mix design based on historical data, and predict the behavior of fresh concrete, hardening, and hardened concrete properties based on laboratory data. The methods have been extended further to evaluate the durability and predict or detect the cracks in the service life of concrete, It has even been applied to predict erosion and chemical attaches. This article offers a review of current applications and trends of machine learning techniques and applications in concrete technology. The findings showed that machine learning techniques can predict the output based on historical data and are deemed to be acceptable to evaluate, model, and predict the concrete properties from its fresh state, to its hardening and hardened state to service life. The findings suggested more applications of machine learning can be extended by utilizing the historical data acquitted from scientific laboratory experiments and the data acquitted from the industry to provide a comprehensive platform to predict and evaluate concrete properties. It was found modeling with machine learning saves time and cost in obtaining concrete properties while offering acceptable accuracy.