High temperature severely affects the nature of the ingredients used to produce concrete, which in turn reduces the strength properties of the concrete. It is a difficult and time-consuming task to achieve the desired compressive strength of concrete. However, the application of supervised machine learning (ML) approaches makes it possible to initially predict the targeted result with high accuracy. This study presents the use of a decision tree (DT), an artificial neural network (ANN), bagging, and gradient boosting (GB) to forecast the compressive strength of concrete at high temperatures on the basis of 207 data points. Python coding in Anaconda navigator software was used to run the selected models. The software requires information regarding both the input variables and the output parameter. A total of nine input parameters (water, cement, coarse aggregate, fine aggregate, fly ash, superplasticizers, silica fume, nano silica, and temperature) were incorporated as the input, while one variable (compressive strength) was selected as the output. The performance of the employed ML algorithms was evaluated with regards to statistical indicators, including the coefficient correlation (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual models using DT and ANN gave R2 equal to 0.83 and 0.82, respectively, while the use of the ensemble algorithm and gradient boosting gave R2 of 0.90 and 0.88, respectively. This indicates a strong correlation between the actual and predicted outcomes. The k-fold cross-validation, coefficient correlation (R2), and lesser errors (MAE, MSE, and RMSE) showed better performance than the ensemble algorithms. Sensitivity analyses were also conducted in order to check the contribution of each input variable. It has been shown that the use of the ensemble machine learning algorithm would enhance the performance level of the model.

Plastic consumption increases with the growing population worldwide and results in increased quantities of plastic waste. There are various plastic waste management strategies; however, the present management progress is not sustainable, and plastic waste dumping in landfills is still the most commonly employed strategy. Being nonbiodegradable, plastic waste dumping in landfills creates several environmental and human health problems. Numerous research studies have been conducted recently to determine safe and ecologically beneficial methods of plastic waste handling. This article performed a bibliographic analysis of the available literature on plastic waste management using a computational approach. The highly used keywords, most frequently cited papers and authors, actively participating countries, and sources of publications were analyzed during the bibliographic analysis. In addition, the various plastic waste management strategies and their environmental benefits have been discussed. It has been concluded that among the six plastic waste management techniques (landfills, recycling, pyrolysis, liquefaction, road construction and tar, and concrete production), road construction and tar and concrete production are the two most effective strategies. This is due to significant benefits, such as ease of localization, decreased greenhouse gas emissions, and increased durability and sustainability of manufactured materials, structures, and roadways. Conversely, using landfills is the most undesirable strategy because of the associated environmental and human health concerns. Recycling has equal benefits and drawbacks. In comparison, pyrolysis and liquefaction are favorable due to the production of char and fuel, but high energy requirements limit their benefits. Hence, the use of plastic waste for construction applications is recommended.

PurposeBlockchain has the potential to facilitate a paradigm shift in the construction industry toward effectiveness, transparency and collaboration. However, there is currently a paucity of empirical evidence from real-world construction projects. This study aims to systematically review blockchain adoption barriers, investigate critical ones and propose corresponding solutions.Design/methodology/approachAn integrated method was adopted in this research based on the technology–organization–environment (TOE) theory and fuzzy decision-making trial and evaluation laboratory (DEMATEL) approach. Blockchain adoption barriers were first presented using the TOE framework. Then, key barriers were identified based on the importance and causality analysis in the fuzzy DEMATEL. Several suggestions were proposed to facilitate blockchain diffusion from the standpoints of the government, the industry and construction organizations.FindingsThe results highlighted seven key barriers. Specifically, the construction industry is more concerned with environmental barriers, such as policy uncertainties (E2) and technology maturity (E3), while most technical barriers are causal factors, such as “interoperability (T4)” and “smart contracts' security (T2)”.Practical implicationsThis study contributes to a better understanding of the problem associated with blockchain implementation and provides policymakers with recommendations.Originality/valueIdentified TOE barriers lay the groundwork for theoretical observations to comprehend the blockchain adoption problem. This research also applied the fuzzy method to blockchain adoption barrier analysis, which can reduce the uncertainty and subjectivity in expert evaluations with a small sample.

This study aims at the development of an artificial neural network-based model for the estimation of weekly sediment load at a catchment located in northern part of Pakistan. The adopted methodology has been based upon antecedent sediment conditions, discharge, and temperature information. Model input and data length selection was carried out using a novel mathematical tool, Gamma test. Model training was carried out by using three popular algorithms namely Broyden-Fletcher-Goldfarb-Shanno (BFGS), back propagation (BP), and local linear regression (LLR) using forward selection of input variables. Evaluation of the best model was carried out on the basis of basic statistical parameters namely R-square, root mean squared error (RMSE), and mean biased error (MBE). Results indicated that BFGS-based ANN model outperformed all other models with significantly low values of RMSE and MBE. A strong correlation was also found between the observed and estimated sediment load values for the same model as the value of Nash-Sutcliffe model efficiency coefficient (R-square) was found to be quite high as well.

PurposeSince construction workers often need to carry various types of loads in their daily routine, they are at risk of sustaining musculoskeletal injuries. Additionally, carrying a load during walking may disturb their walking balance and lead to fall injuries among construction workers. Different load carrying techniques may also cause different extents of physical exertion. Therefore, the purpose of this paper is to examine the effects of different load-carrying techniques on gait parameters, dynamic balance, and physiological parameters in asymptomatic individuals on both stable and unstable surfaces.Design/methodology/approachFifteen asymptomatic male participants (mean age: 31.5 ± 2.6 years) walked along an 8-m walkway on flat and foam surfaces with and without a load thrice using three different techniques (e.g. load carriage on the head, on the dominant shoulder, and in both hands). Temporal gait parameters (e.g. gait speed, cadence, and double support time), gait symmetry (e.g. step time, stance time, and swing time symmetry), and dynamic balance parameters [e.g. anteroposterior and mediolateral center of pressure (CoP) displacement, and CoP velocity] were evaluated. Additionally, the heart rate (HR) and electrodermal activity (EDA) was assessed to estimate physiological parameters.FindingsThe gait speed was significantly higher when the load was carried in both hands compared to other techniques (Hand load, 1.02 ms vs Head load, 0.82 ms vs Shoulder load, 0.78 ms). Stride frequency was significantly decreased during load carrying on the head than the load in both hands (46.5 vs 51.7 strides/m). Step, stance, and swing time symmetry were significantly poorer during load carrying on the shoulder than the load in both hands (Step time symmetry ration, 1.10 vs 1.04; Stance time symmetry ratio, 1.11 vs 1.05; Swing time symmetry ratio, 1.11 vs 1.04). The anteroposterior (Shoulder load, 17.47 mm vs Head load, 21.10 mm vs Hand load, −5.10 mm) and mediolateral CoP displacements (Shoulder load, −0.57 mm vs Head load, −1.53 mm vs Hand load, −3.37 ms) significantly increased during load carrying on the shoulder or head compared to a load in both hands. The HR (Head load, 85.2 beats/m vs Shoulder load, 77.5 beats/m vs No load, 69.5 beats/m) and EDA (Hand load, 14.0 µS vs Head load, 14.3 µS vs Shoulder load, 14.1 µS vs No load, 9.0 µS) were significantly larger during load carrying than no load.Research limitations/implicationsThe findings suggest that carrying loads in both hands yields better gait symmetry and dynamic balance than carrying loads on the dominant shoulder or head. Construction managers/instructors should recommend construction workers to carry loads in both hands to improve their gait symmetry and dynamic balance and to lower their risk of falls.Practical implicationsThe potential changes in gait and balance parameters during various load carrying methods will aid the assessment of fall risk in construction workers during loaded walking. Wearable insole sensors that monitor gait and balance in real-time would enable safety managers to identify workers who are at risk of falling during load carriage due to various reasons (e.g. physical exertion, improper carrying techniques, fatigue). Such technology can also empower them to take the necessary steps to prevent falls.Originality/valueThis is the first study to use wearable insole sensors and a photoplethysmography device to assess the impacts of various load carrying approaches on gait parameters, dynamic balance, and physiological measures (i.e. HR and EDA) while walking on stable and unstable terrains.