Economical design of buried concrete pipes subjected to UK standard traffic loading

The British standard (BS) uses the indirect design method to design buried concrete pipes under the effect of traffic load; in this method the laboratory capacity of the pipe is obtained and linked to the field capacity using an empirical factor called the bedding factor. However, the BS design bedding factors have not been rigorously tested. This paper therefore presents a rigorous analysis of the response of buried pipes under the BS traffic loading requirements and tests the robustness of the BS design bedding factors using a validated finite-element model. It was found that the BS bedding factors are overly conservative, with a ratio of calculated values to the design bedding factors ranging from 1·63 to 4·92. This over-conservatism can be attributed to an oversimplification in the BS methodology for calculating the force applied to the pipe. Therefore, new bedding factors have been proposed utilising the evolutionary polynomial regression analysis technique. These bedding factors implicitly account for the error due to the oversimplification in the design force calculation. Hence, more economical and robust designs can be achieved by using the new bedding factors. </jats:p

Enhanced design approaches for rigid and flexible buried pipes using advanced numerical modelling

Buried pipelines are a vital element in maintaining modern life, as they provide a convenient way for transporting products such as gas, potable water, storm water and waste water. These buried structures have to resist external forces due to backfill soil weight and traffic loading. Therefore, the buried pipe needs to be designed properly to withstand these forces. However, careful examination of the current design standards showed significant issues with the existing design methodology for both rigid and flexible pipes. Thus, this research aimed to use advanced finite element modelling and novel advanced machine learning techniques (namely evolutionary polynomial regression (EPR)) to improve the understanding and propose improvements in the design methods for buried rigid (concrete) and flexible (polyvinyl chloride (PVC)) pipes, to aid with the achievement of a more economic and robust design. The outcomes of this research are a critical literature review, highlighting issues in the previous studies; an improved understanding of the behaviour of buried concrete and PVC pipes; novel design models for buried concrete pipes; and a novel design chart for buried PVC pipes. These design models and chart could be easily used by designers for an economic and robust design of buried concrete and PVCu pipes

Effect of Sand Percentage on the Compaction Properties and Undrained Shear Strength of Low Plasticity Clay

This paper investigates the influence of sand content on the mechanical behavior of a low plasticity clay that collected from south of Iraq (Sumer town). Samples have been prepared with sand contents of 0%, 10%, 20%, 30%, and 40% of the clay weight. Standard Proctor and unconfined compression tests have been carried out and the optimum moisture content, maximum dry density, and undrained shear strength have been determined. The results show a gradual increasing trend of the maximum dry density with the increase of the sand content up to 30%. The highest dry density reaches 1.90 g/cm3 corresponding to an optimum moisture content of 12%. In addition, this paper shows that the undrained shear strength is inversely proportional to the increase of the percentage of sand. The results of this work provide a useful addition to the literature regarding the behaviour or low plasticity clay-sand mixture

Surrogate models to predict maximum dry unit weight, optimum moisture content and California bearing ratio form grain size distribution curve

This study evaluates the applicability of using a robust, novel, data-driven method in proposing surrogate models to predict the maximum dry unit weight, optimum moisture content, and California bearing ratio of coarse-grained soils using only the results of the grain size distribution analysis. The data-driven analysis has been conducted using evolutionary polynomial regression analysis (MOGA-EPR), employing a comprehensive database. The database included the particle diameter corresponding to a percentage of the passing of 10%, 30%, 50%, and 60%, coefficient of uniformity, coefficient of curvature, dry unit weight, optimum moisture content, and California bearing ratio. The statistical assessment results illustrated that the MOGA-EPR provides robust models to predict the maximum dry unit weight, optimum moisture content, and California bearing ratio. The new models’ performance has also been compared with the empirical models proposed by different researchers. It was found from the comparisons that the new models provide enhanced accuracy in predictions as these models scored lower mean absolute error and root mean square error, mean values closer to one, and higher a20−index and coefficient of correlation. Therefore, the new models can be used to ensure more optimised and robust design calculations

Shear strength assessment of reinforced recycled aggregate concrete beams without stirrups using soft computing techniques

This paper presents a study to predict the shear strength of reinforced recycled aggregate concrete beams without stirrups using soft computing techniques. The methodology involves the development of a Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR) and Gene Expression Programming (GEP) models. The input variables considered are the longitudinal reinforcement ratio, recycled coarse aggregate ratio, beam cross-section dimensions, and concrete compressive strength. Data collected from the literature were used to train and validate the models. The results showed that the MOGA-EPR and GEP models can accurately predict the shear strength of beams without stirrups. The models also performed better than equations from the codes and literature. This study provides an alternative approach to accurately predict the shear strength of reinforced recycled aggregate concrete beams without stirrups

Soft computing models for assessing bond performance of reinforcing bars in concrete at high temperatures

The bond between steel and concrete in reinforced concrete structures is a multifaceted and intricate phenomenon that plays a vital role in the design and overall performance of such structures. It refers to the adhesion and mechanical interlock between the steel reinforcement bars and the surrounding concrete matrix. Under elevated temperatures, the bond is more complex under higher temperatures, yet having an accurate estimate is an important factor in design. Therefore, this paper focuses on using data-driven models to explore the performance of the concrete-steel bond under high temperatures using a Gene Expression Programming (GEP) soft computing model. The GEP models are developed to simulate the bond performance in order to understand the effect of high temperatures on the concrete-steel bond. The results were compared to the multi-objective evolutionary polynomial regression analysis (MOGA-EPR) models for different input variables. The new model would help the designers with strength predictions of the bond in fire. The dataset used for the model was obtained from experiments conducted in a laboratory setting that gathered a 316-point database to investigate concrete bond strength at a range of temperatures and with different fibre contents. This study also investigates the impact of the different variables on the equation using sensitivity analysis. the results show that the GEP models are able to predict bond performance with different input variables accurately. This study provides a useful tool for engineers to better understand the concrete-steel bond behaviour under high temperatures and predict concrete-steel bond performance under high temperatures

The influence of electronic waste and attapulgite clay on lightweight polyester concrete

Natural aggregate consumption for producing concrete depletes the natural aggregate, necessitating the development of alternative materials that do not cause a burden on natural resources. Electronic plastic waste (EPW) like digital video discs (DVDs) and compact discs (CDs) are becoming an extreme burden to the environment due to the high quantities generated, which pose serious harm to both the environment and its inhabitants. This study presents the concept of recycling EPW and converting it into construction materials with high specifications. Using 100% EPW in place of sand and 4% unsaturated polyester resin with 20% high reactivity attapulgite (HRA) as a filler, the study generated lightweight polyester concrete (LWPC). The HRA was used after calcination at three temperatures (300, 600 and 900 °C), and for comparison, without calcination, various concentrations of the concrete components were used to produce LWPC using EPW with the optimum polyester resin percentage and HRA burning temperature. The study assessed the physical and mechanical properties of 24 mixtures of LWPC and showed the possibility of producing a novel type of high-strength, sustainable, LWPC with high properties (rapid-set, followability and ductility). The results showed that reducing the concrete’s density to below 1385 kg/m 3 and, when optimal quantities of polyester resin, EPW, and HRA were used, enhanced the workability, flowability, and mechanical properties of fresh and hardened concrete.</p