Neutralization of ricin toxin on building interior surfaces using liquid decontaminants.

Ricin is a highly toxic protein, capable of inhibiting protein synthesis within cells, and is produced from the beans of the Ricinus communis (castor bean) plant. Numerous recent incidents involving ricin have occurred, many in the form of mailed letters resulting in both building and mail sorting facility contamination. The goal of this study was to assess the decontamination efficacy of several commercial off-the-shelf (COTS) cleaners and decontaminants (solutions of sodium hypochlorite [bleach], quaternary ammonium, sodium percarbonate, peracetic acid, and hydrogen peroxide) against a crude preparation of ricin toxin. The ricin was inoculated onto four common building materials (pine wood, drywall joint tape, countertop laminate, and industrial carpet), and the decontaminants were applied to the test coupons using a handheld sprayer. Decontamination efficacy was quantified using an in-vitro cytotoxicity assay to measure the quantity of bioactive ricin toxin extracted from test coupons as compared to the corresponding positive controls (not sprayed with decontaminant). Results showed that decontamination efficacy varied by decontaminant and substrate material, and that efficacy generally improved as the number of spray applications or contact time increased. The solutions of 0.45% peracetic acid and the 20,000-parts per million (ppm) sodium hypochlorite provided the overall best decontamination efficacy. The 0.45% peracetic acid solution achieved 97.8 to 99.8% reduction with a 30-min contact time.

Artificial neural network, machine learning modelling of compressive strength of recycled coarse aggregate based self-compacting concrete.

This research study aims to understand the application of Artificial Neural Networks (ANNs) to forecast the Self-Compacting Recycled Coarse Aggregate Concrete (SCRCAC) compressive strength. From different literature, 602 available data sets from SCRCAC mix designs are collected, and the data are rearranged, reconstructed, trained and tested for the ANN model development. The models were established using seven input variables: the mass of cementitious content, water, natural coarse aggregate content, natural fine aggregate content, recycled coarse aggregate content, chemical admixture and mineral admixture used in the SCRCAC mix designs. Two normalization techniques are used for data normalization to visualize the data distribution. For each normalization technique, three transfer functions are used for modelling. In total, six different types of models were run in MATLAB and used to estimate the 28th day SCRCAC compressive strength. Normalization technique 2 performs better than 1 and TANSING is the best transfer function. The best k-fold cross-validation fold is k = 7. The coefficient of determination for predicted and actual compressive strength is 0.78 for training and 0.86 for testing. The impact of the number of neurons and layers on the model was performed. Inputs from standards are used to forecast the 28th day compressive strength. Apart from ANN, Machine Learning (ML) techniques like random forest, extra trees, extreme boosting and light gradient boosting techniques are adopted to predict the 28th day compressive strength of SCRCAC. Compared to ML, ANN prediction shows better results in terms of sensitive analysis. The study also extended to determine 28th day compressive strength from experimental work and compared it with 28th day compressive strength from ANN best model. Standard and ANN mix designs have similar fresh and hardened properties. The average compressive strength from ANN model and experimental results are 39.067 and 38.36 MPa, respectively with correlation coefficient is 1. It appears that ANN can validly predict the compressive strength of concrete.

Bond performance between hybrid fiber-reinforced concrete and BFRP bars under freeze-thaw cycle.

This study applied the pull-out test to examine the influence of freeze-thaw cycles and hybrid fiber incorporation on the bond performance between BFRP bars and hybrid fiber-reinforced concrete. The bond-slip curves were fitted by the existing bond-slip constitutive model, and then the bond strength was predicted by a BP neural network. The results indicated that the failure mode changed from pull-out to splitting for the BFRP bar ordinary concrete specimens when the freeze-thaw cycles exceeded 50, while only pull-out failure occurred for all BFRP bar hybrid fiber-reinforced concrete specimens. An increasing trend was shown on the peak slip, but a decreasing trend was shown on the bond stiffness and bond strength when freeze-thaw cycles increased. The bond strength could be increased significantly by the incorporation of basalt fiber (BF) and cellulose fiber (CF) under the same freezing and thawing conditions as compared to concrete specimens without fibers. The Malvar model and the Continuous Curve model performed best in fitting the ascending and descending sections of the bond-slip curves, respectively. The BP neural network also accurately predicted the bond strength, with relative errors of predicted bond strengths ranging from 3.75% to 13.7%, and 86% of them being less than 10%.

Suicide by a construction nail fired from a pistol.

Penetrating head injury to accomplish suicide by a non-ammunition-related projectile discharged from a nail-gun is a very rare entity. The authors describe even much rarer, and the first reported case of a suicide penetrating head injury by a construction nail discharged from a blank cartridge of a pistol. The absence of beveling and muzzle impression, the non-ejection of the discharged cartridge, and the exit of just the tip of the nail from the other side of wound were the atypical features in this firearm fatality sustained at a contact-range. The entry wound prototypes like abrasion and grease collar, and blackening were absent. An improvisation to insert a construction nail into the chamber of firearm, for utilization as a projectile was another unique highlight here. The deceased was a construction builder. Being debt-ridden, he probably could not manage to purchase even one live cartridge for his licensee pistol to bring suicidal ideation to culmination.

Study on design optimization of GFRP tubular column composite structure based on machine learning method.

Circular reinforced concrete wound glass fiber reinforced polymer (GFRP) columns and reinforced concrete filled GFRP columns are extensively utilized in civil engineering practice. Various factors influence the performance of these two types of GFRP columns, thereby impacting the whole project. Therefore, it is highly significant to establish the prediction models for ultimate displacement and ultimate bearing capacity to optimize the design of the two types of GFRP columns. In this study, based on the experiments conducted under different conditions on the two kinds of GFRP columns, automatic machine learning along with four other commonly used machine learning methods were employed for modeling to analyze how the column parameters (cross section shape, concrete strength, height of GFRP column, wound GFRP wall thickness, inner diameter of wound GFRP column) affect their performance. The differences in performance among these five machine learning methods were analyzed after modeling. Subsequently, we obtained the variation patterns in ultimate displacement and ultimate bearing capacity of the columns influenced by each parameter by testing the data using the optimal model. Based on these findings, the optimal design schemes for the two types of GFRP columns are proposed. The contribution of this paper is three-fold. First, AutoML sheds light on the automatic prediction of ultimate displacement and ultimate bearing capacity of GFRP column. Second, in this paper, two optimal design schemes of GFRP columns are proposed. Third, for AEC industrial practitioners, the whole process is automatic, accurate and less reliant on data expertise and the optimization design scheme proposed in the article is relatively scientific.

Zero-Emission Cement Plants with Advanced Amine-Based CO2 Capture.

Decarbonization of the cement sector is essentially required to achieve carbon neutrality to combat climate change. Amine-based CO2 capture is a leading and practical technology to deeply remove CO2 from the cement industry, owing to its high retrofittability to existing cement plants and extensive engineering experience in industrial flue gas decarbonization. While research efforts have been made to achieve low-carbon cement with 90% CO2 removal, a net-zero-emission cement plant that will be required for a carbon neutrality society has not yet been investigated. The present study proposed an advanced amine-based CO2 capture system integrated with a cement plant to achieve net-zero CO2 emission by pushing the CO2 capture efficiency to 99.7%. Monoethanomaine (MEA) and piperazine/2-amino-2-methyl-1-propanol (PZ-AMP) amine systems, which are considered to be the first- and second-generation capture agents, respectively, were detailed investigated to deeply decarbonize the cement plant. Compared to MEA, the advanced PZ-AMP system exhibited excellent energy performance with a regeneration duty of ∼2.6 GJ/tonne CO2 at 99.7% capture, 39% lower than the MEA process. This enabled a low CO2 avoided cost of $72.0/tonne CO2, which was 18% lower than that of the MEA-based zero-emission process and even 16.2% lower than the standard 90% MEA process. Sensitivity analysis revealed that the zero-emission capture cost of the PZ-AMP system would be further reduced to below $56/tonne CO2 at a $4/GJ steam production cost, indicating its economic competitiveness among various CO2 capture technologies to achieve a zero-emission cement plant.

Compression stress-strain curve of lithium slag recycled fine aggregate concrete.

As one of the key materials used in the civil engineering industry, concrete has a global annual consumption of approximately 10 billion tons. Cement and fine aggregate are the main raw materials of concrete, and their production causes certain harm to the environment. As one of the countries with the largest production of industrial solid waste, China needs to handle solid waste properly. Researchers have proposed to use them as raw materials for concrete. In this paper, the effects of different lithium slag (LS) contents (0%, 10%, 20%, 40%) and different substitution rates of recycled fine aggregates (RFA) (0%, 10%, 20%, 30%) on the axial compressive strength and stress-strain curve of concrete are discussed. The results show that the axial compressive strength, elastic modulus, and peak strain of concrete can increase first and then decrease when LS is added, and the optimal is reached when the LS content is 20%. With the increase of the substitution rate of RFA, the axial compressive strength and elastic modulus of concrete decrease, but the peak strain increases. The appropriate amount of LS can make up for the mechanical defects caused by the addition of RFA to concrete. Based on the test data, the stress-strain curve relationship of lithium slag recycled fine aggregate concrete is proposed, which has a high degree of agreement compared with the test results, which can provide a reference for practical engineering applications. In this study, LS and RFA are innovatively applied to concrete, which provides a new way for the harmless utilization of solid waste and is of great significance for the control of environmental pollution and resource reuse.

Experimental and numerical investigation on the reinforced concrete boundary beam-wall system subjected to axial compression.

This paper proposes a reinforced concrete (RC) boundary beam-wall system that requires less construction material and a smaller floor height compared to the conventional RC transfer girder system. The structural performance of this system subjected to axial compression was evaluated by performing a structural test on four specimens of 1/2 scale. In addition, three-dimensional nonlinear finite element analysis was also performed to verify the effectiveness of the boundary beam-wall system. Three test parameters such as the lower wall length-to-upper wall length ratio, lower wall thickness, and stirrup details of the lower wall were considered. The load-displacement curve was plotted for each specimen and its failure mode was identified. The test results showed that decrease in the lower wall length-to-upper wall length ratio significantly reduced the peak strength of the boundary beam-wall system and difference in upper and lower wall thicknesses resulted in lateral bending caused by eccentricity in the out-of-plane direction. Additionally, incorporating cross-ties and reducing stirrup spacing in the lower wall significantly improved initial stiffness and peak strength, effectively minimizing stress concentration.

Evaluation of the engineering properties of asphaltic concrete composite produced from recycled asphalt pavement and polyethylene plastic.

This study investigated the suitability of recycled asphalt pavement and polyethylene wastes as coarse aggregate in asphaltic concrete by evaluating the impact of the use of polyethylene polymer wastes and recycled asphalt pavement composite as aggregates on the physical and mechanical properties of the asphaltic concrete. The physical characteristics of the aggregate and bitumen were determined using relevant parametric tests. Recycled asphalt pavement was used to make asphaltic concrete samples using LDPE at 5%, 10%, 15%, RAP at 5% and HDPE at 5%, 10%, 15%, and a mixture of LDPE + HDPE at 5+5%, 7.5+7.5% and 10+10% RAP at 5% as additives. Marshall Stability test was conducted to assess the mechanical strength of the asphaltic concrete, and the results included information on the aggregate's stability, flow, density, voids filled with bitumen, voids filled with air, and voids in mineral aggregate. In addition, the surface and crystal structure of the aggregates was studied by carrying out a microscopic examination with a Scanning Electron Microscope (SEM) and X-Ray diffraction (XRD). The results obtained from this study demonstrated that RAP, HDPE & LDPE are viable conventional aggregate substitute for asphalt concrete production.

Ultra-high performance concrete alleviates ecotoxicological effects in aquatic organisms.

With the advancement of cementitious material technologies, ultra-high performance concretes incorporating nano- and(or) micro-sized particle materials have been developed; however, their environmental risks are still poorly understood. This study investigates the ecotoxicological effects of ultra-high performance concrete (UC) leachate by comparing with that of the conventional concrete (CC) leachate. For this purpose, a dynamic leaching test and a battery test with algae, water flea, and zebrafish were performed using standardized protocols. The conductivity, concentration of inorganic elements (Al, K, Na, and Fe), and total organic concentration were lower in the UC leachate than in the CC leachate. The EC50 values of the CC and UC leachates were 44.9 % and >100 % in algae, and 8.0 % and 63.1 % in water flea, respectively. All zebrafish exposed to the CC and UC leachates survived. A comprehensive evaluation of the ecotoxicity of the CC and UC leachate based on the toxicity classification system (TCS) showed that their toxicity classification was "highly acute toxicity" and "acute toxicity", respectively. Based on the hazard quotient and principal component analysis, Al and(or) K could be significant factors determining the ecotoxicity of concrete leachate. Furthermore, the ecotoxicity of UC could not be attributed to the use of silica-based materials or multi-wall carbon nanotubes. This study is the first of its kind on the ecotoxicity of UC leachate in aquatic environments, and the results of this study can be used to develop environment-friendly UC.

Effect of housing construction material on childhood acute respiratory infection: a hospital based case control study in Bangladesh.

Despite several studies conducted to investigate housing factors, the effects of housing construction materials on childhood ARI symptoms in Bangladesh remain unclear. Hence, the study aimed to measure such a correlation among children under the age of five. A hospital-based case-control study was conducted, involving 221 cases and 221 controls from January to April 2023. Bivariate and multivariate binary logistic regression was performed to measure the degree of correlation between housing construction materials and childhood ARI symptoms. Households composed of natural floor materials had 2.7 times (95% confidence interval 1.27-5.57) and households composed of natural roof materials had 1.8 times (95% confidence interval 1.01-3.11) higher adjusted odds of having under-five children with ARI symptoms than household composed of the finished floor and finished roof materials respectively. Households with natural wall type were found protective against ARI symptoms with adjusted indoor air pollution determinants. The study indicates that poor housing construction materials are associated with an increased risk of developing ARI symptoms among under-five children in Bangladesh. National policy regarding replacing poor housing materials with concrete, increasing livelihood opportunities, and behavioral strategies programs encouraging to choice of quality housing construction materials could eliminate a fraction of the ARI burden.

Co-processing of end-of-life wind turbine blades in portland cement production.

The objective of this paper is to evaluate the feasibility of co-processing wind turbine blade (WTB) material in cement manufacturing to provide an end-of-life means to divert the solid waste of decommissioned WTBs from landfills. Many WTBs consist primarily of glass fiber reinforced thermoset polymers that are difficult to recover or recycle. Portland cement is produced world-wide in large quantities, requiring immense quantities of raw materials (mostly calcium oxide and silicon oxide) and kiln temperatures approaching 1,450 °C. This work contributes analyses of WTB material composition, and predicts the energy provided through the combustible components of the WTBs and raw material contributions provided by incorporating the incombustible components of the WTBs to produce cement. Approximately 40 to 50 % of the WTB material will contribute as fuel to cement production, and approximately 50 to 60 % of the WTB material is expected to be incombustible. One tonne of WTB material can displace approximately 0.4 to 0.5 tonne of coal, while also contributing approximately 0.1 tonne of calcium oxide and 0.3 tonne of silicon oxide as raw material to the cement production process. The glass fiber WTB tested had an average boron content of 4.5 % in the ash. The effects of this high boron content on the cement and its production process should be evaluated. Co-processing WTBs in cement plants will slightly reduce combustion-related CO2 emissions due to avoided calcination. It seems feasible to co-process glass-fiber reinforced WTBs in cement production as WTBs provide suitable raw materials and compatible fuel for this process.

Creep constitutive modeling of the shear strength of the permafrost-concrete interface considering the stress level at -1°C.

The shear creep characteristics of the contact surface between the permafrost and the structure play an important role in the study of the law of deformation and the measures for the prevention and control of pile foundations. In order to study the creep law and the development tendency of the contact surface between permafrost and concrete, it is necessary to establish an accurate creep model. In this study, based on the Nishihara model, a nonlinear element and damage factor D were introduced to establish an intrinsic model of permafrost-concrete contact surfaces considering the effect of shear stress. Creep tests with graded loading of concrete and frozen silt with different roughness at -1°C were conducted using a large stress-controlled shear apparatus. The adequacy of the model was checked using the test data and the regularity of the parameters of the model was investigated. The results show that the creep curves of the contact surface obtained with the improved Nishihara model agree well with the test results and can better describe the whole process of creep of the contact surface of frozen concrete. The analysis of the experimental data shows that: the roughness of the concrete has an inhibiting effect on the creep deformation of the contact surface, When the roughness R varies from 0 mm to 1.225 mm, the specimen corresponds to a long-term strength of 32.84 kPa to 34.57 kPa. For the same roughness and creep time, the creep deformation of the contact surface is more significant with the increasing shear stress τ. The results of the study can provide a theoretical basis for the design and numerical simulation of pile foundations in permafrost regions.

Analysis of decarbonization measures for the Indian Cement Sector.

Cement is one of the widely used materials in construction, and its production is both energy- and emission-intensive, contributing significantly to industrial emissions. This study investigates multiple methods for reducing emissions in the Indian cement sector based on the mass and energy balances of a representative cement plant. A novel methodology for calculating the overall emissions reduction per tonne of cement with multiple emission reduction measures and their interdependencies is proposed. The effect of captive power plants in the cement industry on emissions reduction is also considered. The results are depicted using an emission abatement curve, which gives the CO2 abatement cost against cumulative emission reduction per tonne of cement, and a cost premium curve, which shows the cumulative abatement cost against percentage abatement. The analysis shows that up to 30% emissions reduction is possible using existing emission reduction measures in all the cases considered with no additional cost, and near-zero emission reduction is only possible with the adoption of emerging technologies such as carbon capture and storage. The proposed methodology is the first to explore the impact of multiple measures for emission reduction on a given cement plant, allowing for a realistic estimate of emission reduction from the measures implemented.

Simulation test of short-term and long-term clogging of permeable pavement.

Permeable pavement is superior in functions such as reducing noise, improving traffic safety, and protecting urban water environment. However, contaminants on the pavement due to spillage of transported goods, deposits from vehicle wear and tear, and natural settlement can cause the risk of blockage when these contaminants enter the interior of the permeable pavement. Timely maintenance of permeable performance can effectively solve the degradation of environmental performance of permeable pavement caused by clogging. Consequently, exploring permeable pavement clogging patterns can support determining the timing of maintenance. In this paper, simulation clogging material gradations were formulated based on actual road clogging conditions. According to the self-developed permeable pavement clogging simulator, the clogging behavior of permeable pavement was comprehensively explored, taking external conditions, mix proportion, structural combination, and long-term clogging conditions into consideration. Moreover, the effect of external conditions on the clogging pattern was simulated by varying the rainfall intensity and clogging particle size. Furthermore, the effects of gradation, void rate, nominal maximum particle size, and the overwater section on the clogging resistance were investigated. The clogging-sensitive particle size under different structures was determined. It is demonstrated that the water head height is the crucial factor on the clogging behavior. Greater rainfall intensity and water head height lead to more severe clogging. The increase of nominal maximum aggregate size is beneficial to the anti-clogging ability of permeable pavement. Also, the clogging material with small particle sizes is more likely to cause structural clogging. Finally, the evaluation index of clogging level was put forward, which divides the clogging level of permeable pavement into mild, moderate, and severe. The research can support the rationalization, intelligence, and convenience of permeable pavement maintenance timing decisions. Meanwhile, there is key significance to the promotion application and performance maintenance.

Mechanical characteristics of waste-printed circuit board-reinforced concrete with silica fume and prediction modelling using ANN.

The use of electronic waste in cement concrete as a fibre additive has proven to be very promising for improving mechanical characteristics and developing sustainable construction materials to reduce the waste dumped in landfills. The following study investigated the effect of electronic waste (printed circuit boards (PCBs)) on the mechanical properties of concrete and predicted the same properties with an appropriate machine learning technique. PCB fibres 45 mm in length and 1.5 mm in width were manufactured and added as fibre additions to two sets of concrete mixes with and without silica fume. A 10% volume replacement of cement was substituted with silica fume (SF) to enhance the characteristics of PCB fibre-reinforced concrete and minimize cement consumption. The study included an evaluation of the fresh properties and mechanical characteristics after a 28-day curing period; thereafter, the results were compared and studied using the Levenberg-Marquardt backpropagation algorithm for predictions. The results show that the mechanical properties improved up to a 5% addition of PCB fibres, resulting in strengths of 63.55 MPa and 69.92 MPa for mixtures of PCB5% and SFPCB5%, respectively. A similar trend was achieved for other properties, such as the tensile and flexural strengths. The results of the ANN model predicted values with R2 values ranging from 0.94 to 0.99, indicating the efficacy of the model.

Multi-index control strategy from cement calcination denitration system: a model predictive control method for combined control of nitrogen oxide and ammonia escape.

The cement industry is one of the main sources of NOx emissions, and automated denitration systems enable precise control of NOx emission concentration. With non-linearity, time delay and strong coupling data in cement production process, making it difficult to maintain stable control of the denitration system. However, excessive pursuit of denitration efficiency is often prone to large ammonia escape, causing environmental pollution. A multi-objective prediction model combining time series and a bi-directional long short-term memory network (MT-BiLSTM) is proposed to solve the data problem of the denitration system and achieve simultaneous prediction of NOx emission concentration and ammonia escape value. Based on this model, a model predictive control framework is proposed and a control strategy of denitration system with multi-index model predictive control (MI-MPC) is built based on neural networks. In addition, the differential evolution (DE) algorithm is used for rolling optimization to find the optimal solution and to obtain the best control variable parameters. The control method proposed has significant advantages over the traditional PID (proportional integral derivative) controller, with a 3.84% reduction in overshoot and a 3.04% reduction in regulation time. Experiments prove that the predictive control framework proposed in this paper has better stability and higher accuracy, with practical research significance.

Failure evolution and instability prediction of fiber-reinforced polymer-confined cement mortar specimens under axial compression.

In geotechnical engineering, a large number of pillars are often left in underground space to support the overlying strata and protect the surface environment. To enhance pillar stability and prevent instability, this study proposes an innovative technology for pillar reinforcement. Specifically, local confinement of the pillar is achieved through fiber-reinforced polymer (FRP) strips, resulting in the formation of a more stable composite structure. In order to validate the effectiveness of this structural approach, acoustic emission characteristics and surface strain field characteristics were monitored during failure processes, while mathematical models were employed to predict specimen instability. The test results revealed that increasing FRP strip confinement width led to heightened activity in acoustic emission events during failure processes, accompanied by a decrease in shear cracks but an increase in tensile cracks. Moreover, ductility was improved and deformation resistance capacity was enhanced within specimens. Notably, initial crack generation occurred within unconfined regions of specimens during failures; however, both length and width as well as overall numbers of cracks significantly decreased due to implementation of FRP strips. Consequently, specimen failure speed was slowed down accordingly. Finally, the instability of the partial FRP-confined cement mortar could be more accurately predicted based on the model of FRP-confined concrete. It was verified by the test results.

Thermally-treated asbestos-cement wastes as supplementary precursor for geopolymeric binders: CO2 emission and properties.

This article explores the impact of thermally treated asbestos-cement waste (ACWT) on metakaolin-based geopolymers, using liquid sodium silicate (LSS) and liquid potassium silicate (LKS) as alkali activators. Through statistical mixture design, various formulations were tested for rheological parameters, mineralogical composition, efflorescence mass, electrical conductivity, compressive strength, and CO2 emissions. Formulations with sodium silicate exhibited higher yield stress compared to those with potassium silicate, while flash setting occurred in LKS-activated mixtures with high ACWT content. Alkali activator content significantly affected mechanical strength and leachate electrical conductivity. CO2 emissions were higher for LKS-activated formulations but lower for those with more ACWT. Finally, by incorporating ACWT, it was possible to optimize the formulations, resulting in high compressive strength, reduced free ions, and reduced negative environmental impact.