Traditional Engineering Techniques Research Articles

Redefining structural soundness in concrete constructions: A groundbreaking technique for water–cement ratio assessment in sustainable building integrated with explainable artificial intelligence

Predicting concrete’s compressive strength (CS) is a crucial and challenging task in civil engineering as it directly impacts the longevity and structural integrity of infrastructure initiatives. Precise estimation of the water–cement ratio (W/C) is essential for guaranteeing the structural integrity of structures since it is a critical parameter that greatly affects concrete’s CS. This study carries out an extensive investigation of the prediction of the W/C of concrete, utilizing the enormous potential of machine learning, including the backpropagation neural network (BPNN), bilayer neural network, boosted tree algorithm, bagged tree algorithm (BGTA), and support vector regression (SVR), using 108 datasets. We integrate artificial intelligence models with traditional engineering techniques to develop a reliable, precise, and efficient forecasting system. The study input includes curing days (D), fiber (F), cement (C), fine and coarse aggregate (FA and CA), density (Den), CS, water (W), and W/C as the output variables. The result shows that, in comparison to the other models, BGTA-M3 achieved the best performance evaluation criterion. In the calibration and verification phases, NSE, PCC, R, and WI = 1 and MAPE = 0.00, respectively. BPNN-M3 had an MAPE of 0.0004 in the verification phase. The study uses SHapley Additive exPlanations (SHAP), an explainable artificial intelligence (AI) technique, to improve decision-making in complex systems, with cement “C” significantly contributing to higher predictions in SVR-M2. Future studies should expand the dataset to include information from diverse geographic areas, environmental conditions, and concrete mixes to enhance the applicability and dependability of the models.

Estimation of slope stability using ensemble-based hybrid machine learning approaches

Mining is one of the most daunting occupations gain the sector since it entails risk at any point in the operation. In its operation, the main focus is on slope stability. To avoid slope failures, work should be performed in line with both the regulations and the safety criteria. Slope stability is essential in mining activities owing to slope failure putting productivity and safety at risk. Prediction of slope failure is difficult because of the complexity of traditional engineering techniques. Through study, recent technologies have helped mining companies predict slope problems quickly and effectively. In this current research, an ensemble of machine learning intelligence algorithms was used to estimate and assess the Factor of Safety (FOS). In Ostapal Chromicte Mine, India, 79 experimental and failure slope occurrences were tracked to gather in-the-moment field data. The available data were split into training and testing sets at random to build algorithms. The five influenced factors such as the unit weight, the friction angle, the cohesiveness, the mining depth, as well as the slope angle used as input variables to estimate the FOS. Selected machine learning techniques such as Multiple Linear Regression (MLR), Decision Tree, Random Forest (RF), eXtreme Gradient Boosting (XGBoost) and ensemble hybrid model combining eXtreme Gradient Boosting and Random Forest (XGBoost-RF) were developed to evaluate the FOS. The validity and efficiency of created models can be evaluated using standard evaluation parameters such as coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), normalized root mean square error (NRMSE), mean absolute percentage error (MAPE) and mean absolute deviation (MAD). The most precise model to assess the FOS across all models was discovered to be the XGBOOST-RF ensemble model, which had a high R2 of 0.931, MSE of 0.009, NRMSE of 0.069, MAD of 0.037, MAPE of 3.581 and an RMSE of 0.098.

Making a film to share traditional engineering techniques to tackle the global water crisis

Civil engineers in the UK have teamed up with a water charity to make an educational film that helps communities in semi-arid climates manage their water. The team learnt how to construct simple dams, known in India as ‘johads’, that harvest seasonal rainfall, reducing flood and drought risk. They then built a new dam and restored another in Rajasthan district, recording the process to enable others to replicate the work. A language-neutral educational film was then produced and tested with other communities in India and Colombia to ensure applicability to a variety of geographies, cultures and environments. Educating communities on water management in this way will help to achieve the United Nations sustainable development goal 6: clean water and sanitation for all.

A novel apparatus and methodology for the high frequency mechanical characterisation of ultrasoft materials

Characterising the mechanical response of ultra-soft materials is challenging, particularly at high strain rates and frequencies [1]. Time Temperature Superposition (TTS) can sometimes be used to mitigate these limitations [2], however not all materials are suitable for TTS. Biological tissues are particularly difficult to test: in addition to the extreme softness, challenges arise due to specimen inhomogeneity, sensitivity to boundary conditions, natural biological variability, and complex post-mortem changes. In the current study, a novel experimental apparatus and methodology was developed and validated using low modulus silicone elastomers as model materials. The full field visco-elastic shear response was characterised over a wide range of deformation frequencies (100-1000+ Hz) and amplitudes using Digital Image Correlation (DIC) and the Virtual Fields Method (VFM). This methodology allows for the extraction of fullfield material properties that would be difficult or impossible to obtain using traditional engineering techniques.

The use of soil bioengineering to overcome erosion problems in a pipeline river crossing in South America

Exposed pipelines are susceptible to geotechnical–hydraulic phenomena and to a high-risk level of failures caused by scouring or erosive processes in the river bed and river banks. Currently, in the prevention, maintenance, or remediation of river crossings, traditional engineering techniques are generally applied, which often leads to insufficient solutions concerning technical, financial, and environmental issues. The soil bioengineering methods, using vegetation in combination with inert materials for soil stabilization and protection against erosion, can be an alternative, a complementary tool, or even a substitute for traditional engineering practice. In this context, this work aims to present one case study of an exposed pipeline in which were used some soil bioengineering approaches to prevent natural erosion processes at pipeline river crossings. Compared to conventional methods, the soil bioengineering techniques used in the presented case study have proved to be advantageous in economic, environmental, and aesthetic aspects.

Learning from Network Device Statistics

We estimate end-to-end service metrics from network device statistics. Our approach is based upon statistical, supervised learning, whereby the mapping from device-level to service-level metrics is learned from observations, i.e., through monitoring the system. The approach enables end-to-end performance prediction without requiring an explicit model of the system, which is different from traditional engineering techniques that use stochastic modeling and simulation. The fact that end-to-end service metrics can be estimated from local network statistics with good accuracy in the scenarios we consider suggests that service-level properties are “encoded” in network-level statistics. We show that the set of network statistics needed for estimation can be reduced to a set of measurements along the network path between client and service backend, with little loss in estimation accuracy. The reported work is largely experimental and its results have been obtained through testbed measurements from a video streaming service and a KV store over an OpenFlow network .

Effects of soil conditions on survival and growth of black willow cuttings.

Current streambank restoration efforts focus on providing bank stability, enhancing water quality, and improving woody habitat using native vegetation rather than traditional engineering techniques. However, in most cases harsh site conditions limit restoration success. A two-year field study was conducted at Twentymile Creek, in northern Mississippi, investigating edaphic factors governing the survival of black willow (Salix nigra) cuttings used for streambank restoration. Low height growth, above-ground biomass production, and average leaf area were observed in willow cuttings grown in plots subjected to moisture deficits. However, sediment texture emerged as the dominant factor determining willow post growth, health, and survival. Shoot biomass, leaf biomass, and total above-ground biomass were 15-, 10-, and 14-fold greater for large willow cuttings (posts) grown in plots with sandy sediments relative to those grown in plots with similar moisture and soil redox potential but with silt and clay sediments. Average leaf size, average leaf mass and specific leaf area were all lower in fine textured plots. Under moisture conditions present at our sites, coarse-grained sediment (sand) was more conducive to willow growth, biomass production, and survival than were fine-grained sediments (silt/clay). Our results strongly suggest that soil texture and moisture conditions can determine restoration success. Therefore, it is critical that site conditions are factored into the selection of project locations prior to the initiation of willow planting restoration projects.

Fast tooling

Rapid prototyping (RP) has become a popular and well-known technique for speeding time-to-market. The next step, rapid tooling, promises the same benefits but poses rather more problems. A development project is described. Rapid tooling is a technique that draws on both existing RP technologies and traditional engineering techniques, such as pattern making. Parts are manufactured via the intended production route and produced in the required production material. Laminated sheets are cut and assembled, then machined further to get a precise result. Problems of tooling pressure and accurate lamina assembly are considered.