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In recent years, fire accidents in engineering structures have often been reported worldwide, leading to a severe risk to life and property safety. The present study is carried out to evaluate the performance of Ground Granulated Blast Furnace Slag (GGBS) and fly ash–blended laterized mortars at elevated temperatures.

This test program includes the replacement of natural river sand with lateritic fine aggregates (lateritic FA) in terms of 0, 50 and 100%. Also, the ordinary Portland cement (OPC) was replaced with fly ash and GGBS in terms of 10, 20, 30% and 20, 40 and 60%, respectively, for producing blended mortars.

This paper presents results related to the determination of residual compressive strengths of lateritic fine aggregates-based cement mortars with part replacement of cement by fly ash and GGBS exposed to elevated temperatures. The effect of elevated temperatures on the physical and mechanical properties was evaluated with the help of microstructure studies and the quantification of hydration products.

A sustainable cement mortar was produced by replacing natural river sand with lateritic fine aggregates. The thermal strength deterioration features were assessed by exposing the control specimens and lateritic fine aggregates-based cement mortars to elevated temperatures. Changes in the mechanical properties were evaluated through a quantitative microstructure study using scanning electron microscopy (SEM) images. The phase change of hydration products after exposure to elevated temperatures was qualitatively analyzed by greyscale thresholding of SEM images using Image J software.

Construction industry is one of the leading causes of pollution generation in today's context. But the fact that the development of construction industry leads to the country's economic and social development cannot be unobserved. Hence, there is a need to develop a sustainable construction methodology, and while doing so, measures must be considered so as to not disturb the natural habitats. With the greater prominence shown toward the concept of green and sustainable construction developments, various tools have been developed in recent years in order to measure the performance of such sustainable and green buildings. In the Indian context, the assessment tools developed to measure the performance of the green building are found to be scanty in addressing various economic and social impacts.

This study aims at developing a building performance score (BPS) model concerning the sustainability model built on the triple bottom priorities considering all the three vital components, viz. environmental, economic and social factors. In this study, the different phases involved in the complete life cycle of the project are recognized and then all the phases are assessed considering all the three major components mentioned in the BPS model.

The outcome of this study specifies that various indicators, such as the topographical and climate change, health and safety of the construction workers, project management consultancy, risk management, security measures and solid waste management, form a chief source of a sustainable building, and these indicators are not being assessed in the existing assessment tools. Also, consideration of environmental, economic and social factors is also equally important in construction industry. Moreover, these indicators are also required to be assessed and included in the evaluation process while assessing the performance of the building.

The BPS model developed in the study will assist to improve in assessing the building performance with respect to all indicators in the complete life cycle of the project.

Inadequacies in several micronutrients in complementary foods, notably iron, zinc, calcium, vitamin A, vitamin B6 and riboflavin have been reported. Moringa oleifera leaf powder (MLP), prepared from dried moringa leaves is nutrient-rich and has been explored for the treatment of micronutrient deficiencies among children in developing countries. This increasing interest in the use of moringa oleifera leaves to improve complementary foods notwithstanding, the unique sensory characteristics of the leaf powder potentially holds implications for the acceptability of local diets that are fortified with it. The purpose of this paper is to investigate the levels of MLP fortification that are most acceptable for feeding infants and young children.

The authors performed a review of the literature, with the aim of investigating the sensory attributes and acceptable levels of fortification of complementary food blends fortified with different levels of MLP.

The minimum amount of MLP to be added to a complementary food blend to observe significant improvements in its nutritional value was estimated to be about 10 per cent. However, at this 10 per cent fortification level also, sensory attributes of the products begin to become less desirable.

For the success of nutrition interventions that involve the use of MLP to improve the nutritional quality of complementary foods, there is a need to consider the acceptability of the sensory attributes of the formulated blends in the target group. Safety of MLP as an ingredient in infant foods must also be investigated.

The authors of this paper make recommendations for the use of MLP to fortify complementary foods to ensure its success as a food fortificant in nutrition interventions. The researchers are not aware of any published study that focuses on this subject.

The purpose of this study to apply stacking ensemble machine learning algorithm for predicting the cost of highway construction projects.

The proposed stacking ensemble model was developed by combining three distinct base predictive models automatically and optimally: linear regression, support vector machine and artificial neural network models using gradient boosting algorithm as meta-regressor.

The findings reveal that the proposed model predicted the final project cost with a very small prediction error value. This implies that the difference between predicted and actual cost was quite small. A comparison of the results of the models revealed that in all performance metrics, the stacking ensemble model outperforms the sole ones. The stacking ensemble cost model produces 86.8, 87.8 and 5.6 percent more accurate results than linear regression, vector machine support, and neural network models, respectively, based on the root mean square error values.

The study shows how stacking ensemble machine learning algorithm applies to predict the cost of construction projects. The estimators or practitioners can use the new model as an effectual and reliable tool for predicting the cost of Ethiopian highway construction projects at the preliminary stage.

The study provides insight into the machine learning algorithm application in forecasting the cost of future highway construction projects in Ethiopia.

The purpose of this study is to identify how the responsibilities and costs of planning, controlling and executing the material, resource and waste flows are shifted between actors when introducing a construction logistics setup (CLS) as a product innovation in a construction project, compared to the traditional way of organizing these activities.

This study is an analytical conceptual research study which aims to bring new insights into a problem through logical relationship building. Empirical data are gathered in two cases where CLSs are used, through observations and interviews regarding how the activities within the order-to-delivery process are performed. The results have been discussed at workshops with suppliers, installation companies, contractor firms and trade unions.

The outcome of this study is a model for illustrating how costs and responsibilities are shifted in the construction project and supply chain when a CLS is introduced. The cost shift is dependent on the activity shift that accompanies the services included in the setup.

The practical contribution of this work is twofold. First, this study provides a methodology of how to evaluate the impact of logistics services on the actors in the construction project. Second, this study shows shifts in costs and responsibilities in logistics activities with the introduction of construction logistics services.

The theoretical contributions of the model and this study lie in the inclusion of a multi-actor perspective in total cost modelling in supply chains.

The purpose of this paper is to investigate the potential use of construction and demolition waste materials (C&DWM) as an alternative for natural fine aggregates (NFA), in view to solve the disposal problems caused due to landfills. In addition, to evaluate its suitability as a sustainable material, mechanical and durability properties have been performed on different proportions of concrete blending and the results recorded were compared with the reference concrete values.

In this research, the NFA were replaced at the proportion of 25%, 50%, 75% and 100% of C&DWM with a constant slump range of 130 mm–150 mm. This parameter will assess the consistency of the fresh concrete during transportation process. The characteristics of the end product was evaluated through various tests conducted on hardened concrete samples, namely, compressive strength, flexural strength, depth of penetration of water under pressure, rapid chloride penetration test, carbonation test and ultrasonic pulse velocity (UPV) test. All results recorded were compared with the reference concrete values.

The results demonstrated that the use of C&DWM in concrete portrayed prospective characteristics that could eventually change the concept of sustainable concrete. It was noted that the compressive and flexural strength decreased with the addition of C&DWM, but nevertheless, a continuous increase in strength was observed with an increase in curing period. Moreover, the increase in rapid chloride penetration and decrease in UPV over time period suggested that the concrete structure has improved in terms of compactness, thus giving rise to a less permeable concrete. The mechanical tests showed little discrepancies in the final results when compared to reference concrete. Therefore, it is opined that C&DWM can be used effectively in concrete.

This study explores the possible utilisation of C&DWM as a suitable surrogative materials in concrete in a practical perspective, where the slump parameter will be kept constant throughout the experimental process. Moreover, research on this method is very limited and is yet to be elaborated in-depth. This approach will encourage the use of C&DWM in the construction sector and in the same time minimise the disposal problems caused due to in landfills.

This paper aims to focus on studying the influence of gradient parameters, namely, thickness coefficient, length coefficient and height ratio of auxetic structure on responses such as strength, stiffness and specific energy absorption (SEA) under compressive loading. Optimization of significant parameters is also performed to maximize responses. Further, efforts have also been made to develop regression models for strength, stiffness and SEA of auxetic structure.

Central composite design of response surface methodology is used for planning experiments. Auxetic structures of acrylonitrile butadiene styrene (ABS) and poly-lactic acid (PLA) materials are fabricated by the material extrusion (ME) technique of additive manufacturing. Fabricated structures are tested under in-plane uniaxial compressive loading. Grey relational analysis is used for the optimization of gradient parameters of the unit cell of auxetic structure to maximize responses and minimize weight and time of fabrication.

From the analysis of variance of experimental data, it is found that the compressive strength of auxetic structures increases with a decrease in length coefficient and height ratio. In the case of ABS structures, stiffness increases with a decrease in thickness coefficient and length coefficient, while in the case of PLA structures, stiffness increases with a decrease in length coefficient and height ratio. SEA is influenced by length coefficient and thickness coefficient in ABS and PLA structures, respectively. Based on the analysis, statistical non-linear quadratic models are developed to predict strength, stiffness and SEA. Optimal configuration of auxetic structure is determined to maximize strength, stiffness, SEA and minimize weight and time of fabrication.

The present study is limited to re-entrant type of auxetic structures made of ABS and PLA materials only under compressive loading. Also, results from the current study are valid within a selected range of gradient parameters. The findings of the present study are useful in the optimal selection of gradient parameters for the fabrication of auxetic structures of maximum strength, stiffness and SEA with minimum weight and time of fabrication. These outcomes have wide applications in domains such as automotive, aerospace, sports and marine sectors.

Limited literature is available on studying the influence of gradient parameters of ME manufactured auxetic structure of ABS and PLA materials on responses, namely, strength, stiffness and SEA under compressive loading. Also, no work has been reported on studying the influence of gradient parameters on mechanical properties, weight and time of fabrication of auxetic structures. The present study is an attempt to fulfil the above research gaps.

The main cause of boiler tube failure in high-temperature thermal power plants is molten sulphate-vanadate-induced hot corrosion. Applying thermal spray coatings on alloy steels can reduce hot corrosion. This study aims to examine the impact of nano yttria stabilized zirconia (YSZ) reinforcement on Ni-20Cr composite coatings on hot corrosion behaviour of T22 steel in a corrosive environment of Na₂SO₄-60%V₂O₅ at 650 °C over 50 cycles.

The coatings were deposited using a high velocity oxyfuel technique. The samples were subjected to hot corrosion in a Silicon tube furnace at 650 °C for 50 cycles. Weight gain data after each cycle were used to analyse the kinetics of corrosion behaviour. Corrosion products were examined using X-ray diffraction, scanning electron microscopy, energy dispersive and cross-sectional analytical methods.

During investigation, nano YSZ-reinforced Ni-20Cr composite coatings on T22 steel were discovered to give superior corrosion resistance in a molten salt environment at 650 °C. Throughout the experiment, the coatings gained less weight and formed protective oxide scales. Increased YSZ concentration in the coating matrix resulted in better protection against hot corrosion.

The inclusion of nano YSZ reduced porosity in Ni-20Cr coatings by filling voids and interlocking particles, as well as blocking the penetration of corroding species, hence improving the corrosion resistance of composite coatings. The corrosion rate decreased with increasing YSZ content in the coating matrix.

It should be noted that the high temperature corrosion behaviour of thermally sprayed nano YSZ-Ni-20Cr composite coatings has never been investigated and is not documented in the literature. As a result, the current study can provide useful information for the application of nano YSZ-reinforced coatings in high-temperature fuel combustion conditions.

The Government’s investment in infrastructure projects is considerably high, especially in bridge construction projects. Government authorities must establish an initial forecasted budget to have transparency in transactions. Early cost estimating is challenging for Quantity Surveyors due to incomplete project details at the initial stage and the unavailability of standard cost estimating techniques for bridge projects. To mitigate the difficulties in the traditional preliminary cost estimating methods, there is a requirement to develop a new initial cost estimating model which is accurate, user friendly and straightforward. The research was carried out in Sri Lanka, and this paper aims to develop the artificial neural network (ANN) model for an early cost estimate of concrete bridge systems.

The construction cost data of 30 concrete bridge projects which are in Sri Lanka constructed within the past ten years were trained and tested to develop an ANN cost model. Backpropagation technique was used to identify the number of hidden layers, iteration and momentum for optimum neural network architectures.

An ANN cost model was developed, furnishing the best result since it succeeded with around 90% validation accuracy. It created a cost estimation model for the public sector as an accurate, heuristic, flexible and efficient technique.

The research contributes to the current body of knowledge by providing the most accurate early-stage cost estimate for the concrete bridge systems in Sri Lanka. In addition, the research findings would be helpful for stakeholders and policymakers to propose policy recommendations that positively influence the prediction of the most accurate cost estimate for concrete bridge construction projects in Sri Lanka and other developing countries.

Challenges associated with ensuring the accuracy and reliability of cost estimation of highway construction bid items are of significant interest to state highway transportation agencies. Even with the existing research undertaken on the subject, the problem of inaccurate estimation of highway bid items still exists. This paper aims to assess the accuracy of the cost estimation methods employed in the selected studies to provide insights into how well they perform empirically. Additionally, this research seeks to identify, synthesize and assess the impact of the factors affecting highway unit prices because they affect the total cost of highway construction costs.

This paper systematically searched, selected and reviewed 105 papers from Scopus, Google Scholar, American Society of Civil Engineers (ASCE), Transportation Research Board (TRB) and Science Direct (SD) on conceptual cost estimation of highway bid items. This study used content and nonparametric statistical analyses to determine research trends, identify, categorize the factors influencing highway unit prices and assess the combined performance of conceptual cost prediction models.

Findings from the trend analysis showed that between 1983 and 2019 North America, Asia, Europe and the Middle East contributed the most to improving highway cost estimation research. Aggregating the quantitative results and weighting the findings using each study's sample size revealed that the average error between the actual and the estimated project costs of Monte-Carlo simulation models (5.49%) performed better compared to the Bayesian model (5.95%), support vector machines (6.03%), case-based reasoning (11.69%), artificial neural networks (12.62%) and regression models (13.96%). This paper identified 41 factors and was grouped into three categories, namely: (1) factors relating to project characteristics; (2) organizational factors and (3) estimate factors based on the common classification used in the selected papers. The mean ranking analysis showed that most of the selected papers used project-specific factors more when estimating highway construction bid items than the other factors.

This paper contributes to the body of knowledge by analyzing and comparing the performance of highway cost estimation models, identifying and categorizing a comprehensive list of cost drivers to stimulate future studies in improving highway construction cost estimates.