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A pandemic is a force majeure event, and contracting parties can invoke conditions under force majeure to minimize liability for unforeseen, uncontrollable, and unavoidable circumstances. This study develops a conceptual model to assist in the management of delays and cost overruns due to force majeure events arising from the construction sector in Small Island Developing States (SIDS). A critical case study analysis of past epidemics and pandemics was conducted to develop a survey questionnaire for administration to construction professionals in Trinidad and Tobago. Based on the empirical data of 65 construction professionals, the structural equation model shows that there are strong causal effects from the implications of COVID-19 and force majeure events, which in turn have a dire impact on the construction industry. The leading implication of COVID-19 is the drastic increases in the cost of materials. Also, granting an extension of time to contractors was the main risk variable under the force majeure conditions. From the results, the measurement model verifies that events under force majeure and its perceived implications strongly influence the construction industry, and proposes that force majeure contractual clauses require explicit treatment of the periodic reoccurrence of pandemics to avoid conflicts among contracting parties. This research explores and builds on new avenues from the latest COVID-19 scholarship to better understand existing impacts on the construction industry, and consequently add to the novel body of knowledge on the implications of pandemics on construction contracts. Overall, this research provides a risk-guidance framework for construction professionals and academia to mitigate unforeseen, uncontrollable, and unavoidable risks on construction projects. [ABSTRACT FROM AUTHOR]

The current investigation evaluated the discharge coefficient of a combined compound rectangular broad-crested-weir (BCW) gate (Cdt) using the computational fluid dynamics (CFD) modeling approach and soft computing models. First, CFD was applied to the experimental data and 61 compound BCW gates were numerically simulated by resolving the Reynolds-averaged Navier–Stokes equations and stress turbulence models. Then, six data-driven procedures, including M5P tree, random forest (RF), support vector machine (SVM), Gaussian process (GP), multimode ANN and multilinear regression (MLR) were used for estimating the coefficient of discharge (Cdt) of the weir gates. The results showed the superlative accuracy of the SVM model compared to M5P, RF, GP and MLR in predicting the discharge coefficient. The sensitivity investigation revealed the h1/H as the most effective parameter in predicting the Cdt, followed by the d/p, b/B0, B/B0 and z/p. The multimode ANN model reduced the root mean square error (RMSE) of M5P, RF, GP, SVM and MLR by 37, 13, 6.9, 6.5 and 32%, respectively. The graphical inspection indicated the multimode ANN model as the most suitable for predicting the Cdt of a BCW gate with minimum RMSE and maximum correlation. [ABSTRACT FROM AUTHOR]

A tunnel type sediment excluder is a preventive fluidic device constructed on the bed of the river in front of head regulator to mitigate the sediment in the off-taking canal. The water approaching the head regulator is separated into two parts by the roof slab of the tunnels. The top part having relatively clearer water enters into the offtake canal, while the bottom layer which carries most of sediment flows through the tunnels and is discharged from the undersluices to downstream side. Very crude methods for design of sediment excluder are in vogue to date which are based on past experiences and physical model study. So, a rational approach of machine learning methods; multivariate adaptive regression splines (MARS), group method of data handling (GMDH) and M5P has been utilized for estimating the removal efficiency of sediment excluder (E). These machine learning methods have also been compared with the conventional models given by earlier researchers. Sensitivity study indicated that extraction ratio (R) was the most influencing parameter, which is followed by size of silt (S). Parametric study further suggests that with increase of R, E is getting enhanced. [ABSTRACT FROM AUTHOR]

India has the worst pollution status amongst all the polluted countries in the world, wherein several cities face severe environmental problems due to the rampant increase in pollution. For research investigation two cities, Gaya and Tirupati were selected to estimate the air pollution SO2 trends by three different modelling techniques: M5P and artificial neural network (ANN) and bagged artificial neural network (BANN). Both cities are cultural centres and important pilgrimage destinations in India. Data were collected with 340 observations for Gaya and 1477 observations for Tirupati between 2017 and 2020 from Central Pollution Control Board (CPCB) and used to generate the SO2 models. During the study, results of three models show that BANN is the best model for Gaya with coefficient correlation (CC) 0.8563, mean absolute error (MAE) 1.7575, root mean square error (RMSE) 2.9023, Nash–Sutcliffe efficiency (NSE) 0.7243, Willmott index (WI) 0.9154 and normalized root mean square error (SI) 0.2457 having a testing stage with input combinations, particulate matter (PM2.5), nitric oxide (NO), NO2 (nitrogen dioxide), NOx, carbon monoxide (CO), ozone (O3), relative humidity (RH), wind speed (WS), wind direction (WD), solar radiation (SR), vertical wind speed (VWS) and absolute temperature (AT), and for Tirupati, also BANN is the best model with CC 0.8634, MAE 1.9088, RMSE 2.5891, NSE 0.7421, WI 0.9233 and SI 0.3409 having a testing stage with input combinations, PM2.5, NO, NO2, NOx, CO, O3, RH, WS, WD, SR, VWS, AT and SO2. The most influential parameters are WD and NO2 for Gaya and NO2 and O3 for Tirupati. Graphical results also confirm that BANN-based model is best performing for both cities Gaya and Tirupati. [ABSTRACT FROM AUTHOR]