As the most commonly used construction material, concrete produces extreme amounts of carbon dioxide (CO2) yearly. For this resulting environmental impact on our planet, supplementary materials are being studied daily for their potentials to replace concrete constituents responsible for the environmental damage caused by the use of concrete. Therefore, the production of bio-concrete has been studied by utilizing the environmental and structural benefit of the bacteria, Bacillus subtilis, in concrete. This bio-concrete is known as self-healing concrete (SHC) due to its potential to trigger biochemical processes which heal cracks, reduce porosity, and improve strength of concrete throughout its life span. In this research paper, the life cycle assessment (LCA) based on the environmental impact indices of global warming potential, terrestrial acidification, terrestrial eco-toxicity, freshwater eco-toxicity, marine eco-toxicity, human carcinogenic toxicity, and human non-carcinogenic toxicity of SHC produced with Bacillus subtilis has been evaluated. Secondly, predictive models for the mechanical properties of the concrete, which included compressive (Fc), splitting tensile (Ft), and flexural (Ff) strengths and slump (S), have been studied by using artificial intelligence techniques. The results of the LCA conducted on the multiple data of Bacillus subtilis-based SHC mixes show that the global warming potential of SHC-350 mix (350 kg cement mix) is 18% less pollutant than self-healing geopolymer concrete referred to in the literature study. The more impactful mix in the present study has about 6% more CO2 emissions. In the terrestrial acidification index, the present study shows a 69–75% reduction compared to the literature. The results of the predictive models show that ANN outclassed GEP and EPR in the prediction of Fc, Ft, Ff, and S with minimal error and overall performance.

In this work, the compressive strength of concrete made from recycled aggregate is studied and an intelligent prediction is proposed by using a novel artificial neural network (ANN), which utilizes a sigmoid function and enables the proposal of closed-form equations. An extensive literature search was conducted, which gave rise to 476 data points containing cement, sand, aggregates, recycled aggregates of fine to coarse texture, water, and plasticizer as the constituents of the concrete and the input variables of the intelligent model. The compressive strength (fc) of the recycled aggregate concrete (RAC), which was studied through multiple experiments, was the output variable of the model. The data points of concrete strength collected through literature show a consistent and sustained strength improvement with the increase in the recycled aggregate proportions. However, the outcome of the concrete compressive strength predictive model shows remarkable performance indices as follows; r is 0.99 and 0.99, R2is 0.98 and 0.97, MSE is 28.67% and 44.64%, RMSE is 5.35% and 6.68%, MAE is 4.12% and 5.01%, and MAPE is 12.73% and 13.83% for the model training and testing respectively. These results compared well with previous studies conducted on RAC with less data, different activation functions, and different techniques. Generally, the closed-form equation, which performed at an average accuracy of 97.5% with an internal consistency of 99%, has shown its potential to be applied in RAC design and construction activities for a sustainable performance evaluation of recycled aggregate concrete. Doi: 10.28991/CEJ-2022-08-08-011 Full Text: PDF

The soil water retention curve (SWRC) or soil–water characteristic curve (SWCC) is a fundamental feature of unsaturated soil that simply shows the relationship between soil suction and water content (in terms of the degree of saturation and volumetric or gravimetric water content). In this study, the applications of the SWRC or SWCC have been extensively reviewed, taking about 403 previously published research studies into consideration. This was achieved on the basis of classification-based problems and application-based problems, which solve the widest array of geotechnical engineering problems relevant to and correlating with SWRC geo-structural behavior. At the end of the exercises, the SWRC geo-structural problem-solving scope, as covered in the theoretical framework, showed that soil type, soil parameter, measuring test, predictive technique, slope stability, bearing capacity, settlement, and seepage-based problems have been efficiently solved by proffering constitutive and artificial intelligence solutions to earthwork infrastructure; and identified matric suction as the most influential parameter. Finally, a summary of these research findings and key challenges and opportunities for future tentative research topics is proposed.

In this research study, extensive literature searches on the compressive strength of concrete produced from the addition of fly ash (FA) and silica fume (SF) as extra constituents to the conventional concrete mixes, which gave rise to 330 mix points of concrete database. Due to the worrisome environmental impact of concrete production and usage in concrete activities, it has been pertinent to conduct the life cycle impact assessment of this procedure. Secondly, due to the over dependence of concrete production experts on laboratory exercise, there is also an urgent need to propose equations that reduce this dependence, that can be used in design, construction and performance evaluation of concrete infrastructure, hence the multi-objective nature of this research work. The results of the global warming potential (GWP) based on cement dosage show that Portland cement contributes about 90% of the total score. This is followed by the use of coarse aggregate contributing 6%, superplasticizer, 3% and fine aggregates, 2%. These show the functions of CO2 emissions and other greenhouses gas emissions in the entire system. Also, the result of the terrestrial acidification potential (TAP) for the concrete mixes in this study show that the lowest cement mix “C340-FAg658-FA0-SF15ˮ has a human toxicity, both carcinogenic and non-carcinogenic that showed an added impact of about 14 kg of 1, 4 equivalents of dichlorobenzene (DCB eq.). This result is 428% less impact than other studies found in the literature that used FA. Finally, it was found that the addition of FA and SF in concrete has a lowering effect on the environmental impact indicators due to reduced cement dosage. Furthermore, the results of the model predictions show that ANN with a performance index of 0.986 (4.8%) showed decisive superiority to predict the compressive strength of the FA-SF concrete over EPR, 0.951 (8.7%), GP, 0.94 (9.5%) and GEP, 0.93 (10%).

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It has become very important in the field of concrete technology to develop intelligent models to reduce overdependence on laboratory studies prior to concrete infrastructure designs. In order to achieve this, a database representing the global behavior and performance of concrete mixes is collected and prepared for use. In this research work, an extensive literature search was used to collect 112 concrete mixes corresponding to fly ash and binder ratios (FA/B), coarse aggregate and binder ratios (CAg/B), fine aggregate and binder ratios (FAg/B), 28-day concrete compressive strength (Fc28), and the environmental impact point (P) estimated as a life cycle assessment of greenhouse gas emissions from fly ash- and cement-based concrete. Statistical analysis, linear regression (LNR), and artificial intelligence (AI) studies were conducted on the collected database. The material binder ratios were deployed as input variables to predict Fc28 and P as the response variables. From the collected concrete mix data, it was observed that mixes with a higher cement content produce higher compressive strengths and a higher carbon footprint impact compared to mixes with a lower amount of FA. The results of the LNR and AI modeling showed that LNR performed lower than the AI techniques, with an R2(SSE) of 48.1% (26.5) for Fc and 91.2% (7.9) for P. But ANN, with performance indices of 95.5% (9.4) and 99.1% (2.6) for Fc and P, respectively, outclassed EPR with 90.3% (13.9) and 97.7% (4.2) performance indices for Fc and P, respectively. Taylor’s and variance diagrams were also used to study the behavior of the models for Fc28 and P compared to the measured values. The results show that the ANN and EPR models for Fc28 lie within the RMSE envelop of less than 0.5% and a standard deviation of between 15 MPa and 20 MPa, while the coefficient of determination sector lies between 95% and 99% except for LNR, which lies in the region of less than 80%. In the case of the P models, all the predicted models lie within the RMSE envelop of between 0.5% and 1.0%, a coefficient of determination sector of 95% and above, and a standard deviation between 2.0 and 3.0 points of impact. The variance between measured and modeled values shows that ANN has the best distribution, which agrees with the performance accuracy and fits. Lastly, the ANN learning ability was used to develop a mix design tool used to design sustainable concrete Fc28 based on environmental impact considerations. Doi: 10.28991/CEJ-SP2023-09-03 Full Text: PDF

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