Krisada Chaiyasarn

This work was carried out to explore the potential use of used face masks in concrete to develop sustainable green concrete. In this experimental study, used face masks were cut up, removing the ear stripes and internal nose steel wire, to prepare elongated fibers. These fibers were incorporated in cement fly ash mixtures as an additive to determine the response of M20-grade concrete. The Class F fly ash (FA) was employed as a fractional substitute of cement up to 25% by weight, whereas the addition of face masks occurred at 0%, 0.125%, and 0.25% by volume of concrete. The testing scheme focused on the mechanical and durability characteristics of the cement FA mixtures carried out after 3, 28, and 60 days of curing. The inclusion of FA and face mask fibers reduced the density of concrete specimens. The compressive, splitting tensile, and flexural strengths of mixes were also reduced at an early age; however, the strength characteristics improved at later ages, compared to the control mix. The combination of both materials in concrete mixtures resulted in lower water absorption, lower bulk water sorption, and lower mass loss values against acid attack at later ages. Similarly, the electrical resistance of concrete substantially enhanced by increasing the percentage of both materials. The experimental results demonstrated that processed face masks can be utilized in cement fly ash mixes without significantly compromising the resultant concrete characteristics.

Inspecting damage through visual inspection is an outdated and inefficient method, especially for large concrete dam structures. Furthermore, inspecting for damage to dam structures is costly, time-consuming, and hazardous. This research proposes a novel method for detecting and measuring cracks in the concrete structure of dams by utilizing a combination of the Convolutional Neural Network (CNN)-Fully Convolutional Network (FCN) algorithm and image-based 3D modeling. The method enables the identification of cracks on large structures at a pixel level, facilitating detection over a wide area and with high accuracy. Specifically, the approach detects cracks on the threedimensional surface of the dam, providing a comprehensive and effective means of identifying potential structural issues. To accurately determine the size of the detected damage, the local thickness (LT) algorithm is introduced. This algorithm utilizes the pixelprecise detection capabilities of crack detection to provide highly precise crack size measurements. The CNN-FCN system segments cracks at the pixel level on the texture space obtained from a 3D model created through photogrammetry techniques. Then the trained CNN is employed to detect crack patches, which are then imported into a trained FCN system for pixel-level segmentation. The predicted crack pixels are converted to a physical scale by the LT algorithm to quantitatively measure the morphological features of cracks. The resulting crack map is then projected onto a 3D model, providing a comprehensive visual representation of the cracks. The proposed crack detection method can achieve an accuracy of over 90%. The study revealed that the presented system could efficiently and precisely detect, locate, and measure the size of cracks in large dam structures, highlighting the potential of the proposed method to enhance the efficiency and effectiveness of crack inspection. Furthermore, the visualization of cracks on 3D models can help increase inspectors’ inspection efficiency.

Ride-hailing services play an important role in developing countries where conventional transport systems are not enough to meet the needs of commuters because of increased populations. This form of transport has gained much popularity in developing regions because of the inclusion of motorcycles and rikshaws in ride-hailing services. To the best of the authors’ knowledge, there has been little research on passengers’ behavior towards these ride-hailing services that focuses on social protection and the fare system in developing regions. Therefore, this research study is aimed at investigating the behavior of commuters towards these ride-hailing services in Lahore, which is the second largest city in Pakistan and can be considered as a case study of a developing country. A total of 531 useable valid responses were collected through face-to-face interactions, including the sociodemographics (SEDs) and behavior of commuters towards these services. The results of an explanatory factor analysis (EFA) and structural equation modeling (SEM) revealed that some of the significant latent variables of these ride-hailing services are comfort, convenience, privacy and security, the fare system, social protection, and safety. The commuters’ overall evaluation of these services is positive and affects their present and future preferences. The structural coefficient between convenience and the variable of present preference is significant and negative, which shows that there are respondents who infrequently use ride-hailing services despite having high satisfaction. The riders’ satisfaction with privacy, security, social protection, safety, and comfort has a positive and direct impact on their present preferences as the structural estimates are positive, which means that the higher their views on privacy, security, and comfort, the more frequently they intend to use ride-hailing services for commuting. Increased social protection, safety, privacy, and security will improve the evaluations of the commuters and influence their present preferences for these ride-hailing services. Even though there are regulations on these ride-hailing services, some concrete policy interventions are needed for improvements in commuters’ overall evaluations of these services in order to influence their future preferences. The findings of this research study, if applied in the real world, can improve the overall evaluation of the commuters and positively influence their present and future preferences for these ride-hailing services.

Given the excessive demolition of structures each year, the issues related to the generated structural waste are striking. Bricks being a major constituent in the construction industry, also hold a significant proportion of the construction waste generated annually. The reuse of this brick waste in new constructions is an optimal solution considering cost-effectiveness and sustainability. However, the problems related to the substandard peak stress and ultimate strain of concrete constructed with recycled brick aggregates (CRAs) limit its use in non-structural applications. The present study intends to improve the unsatisfactory mechanical characteristics of CRAs by utilizing low-cost glass fiber chopped strand mat (FCSM) sheets. The efficacy of FCSM sheets was assessed by wrapping them around CRA specimens constructed with different concrete strengths. A remarkable increase in the peak compressive stress and the ultimate strain of the CRA specimens were observed. For low, medium, and high strength CRAs, the ultimate strain improved by up to 320%, 308%, and 294%, respectively, as compared to the respective control specimens. Several existing analytical models were utilized to predict the peak compressive stress and ultimate strain of the CRAs strengthened using FCSM sheets. None of the considered models reproduced experimental results accurately. Therefore, equations were formulated using regression predicting the peak stress and ultimate strain of the CRAs confined with FCSM sheets. The predicted values were found to correlate well with the experimental values.

The present study focused on the development and application of two computer numerical models, namely, a seepage model developed using SEEP/W software and a groundwater model developed using Visual MODFLOW software. The seepage model was applied to a 38 km length of the tail reach of the Trimmu–Sidhnai (T-S) link canal passing through a severely waterlogged area of 32,000 ha, with a water table within 0–1.5 m from the ground surface; this was to quantify the canal seepage under the present condition (without any intervention) and with the interventions of a concrete lining of the complete prism of the T-S link canal and concrete side protection of the T-S link canal, with the canal bed unlined. The groundwater model evaluated the effectiveness of three waterlogging management interventions, which included: (i) the rehabilitation of the 43 existing drainage tube wells, (ii) the rehabilitation of the existing surface drains, and (iii) a combination of the rehabilitation of the 43 existing drainage tube wells and the rehabilitation of the existing surface drains. The seepage modeling revealed that the concrete lining intervention can reduce 50% of the seepage of the T-S link canal, whereas the concrete side protection intervention can reduce only 21% of the canal seepage. The groundwater modeling revealed that the waterlogging management intervention of the rehabilitation of the 43 drainage tube wells and surface drains can lower the groundwater level from 139.2 to 138.3 m (0.9 m drop), resulting in the mitigation of waterlogging in 45% (14,400 ha) of the severely waterlogged area. The present study recommends that complete concrete lining of the T-S link canal has a huge potential to reduce seepage from the canal, and the combination of the rehabilitation of the 43 drainage tube wells and surface drains also offers a great potential for controlling waterlogging. This intervention can also be considered to mitigate waterlogging from the severely waterlogged area. Cost-effectiveness analysis of the concrete lining of the T-S link canal, the rehabilitation of the 43 existing drainage tube wells, and the rehabilitation of the existing surface drains need to be performed for decision-making and selection of the most cost-effective intervention for implementation. A study needs to be conducted for the development and evaluation of economical and socio-technically feasible and acceptable preventive waterlogging management interventions, including the improved management of irrigation systems, improved irrigation management practices at the farm, improved conjunctive management of surface and groundwater, and improved management of drainage systems at the primary, secondary, and tertiary canal command levels.

Reusing construction brick waste to fabricate new concrete is an economical and sustainable solution for the ever-increasing quantity of construction waste. However, the substandard mechanical properties of the concrete made using recycled crushed brick aggregates (RBAC) have limited its use mainly to non-structural applications. Several studies have shown that the axial compressive performance of the concrete is a function of the lateral confining pressure. Therefore, this study proposes to use low-cost glass fiber-reinforced polymer (LC-GFRP) wraps to improve the substandard compressive strength and ductility of RBAC. Thirty-two rectilinear RBAC specimens were constructed in this study and tested in two groups. The specimens in Group 1 were tested without the provision of a corner radius, whereas a corner radius of 26 mm was provided in the Group 2 specimens. Specimens in both groups demonstrated improved compressive behavior. However, the premature failure of LC-GFRP wraps near the sharp corners in Group 1 specimens undermined its efficacy. On the contrary, the stress concentrations were neutralized in almost all Group 2 specimens with a 26 mm corner radius, except low-strength specimen with six layers of LC-GFRP. As a result, Group 2 specimens demonstrated a more significant improvement in peak compressive strength and ultimate strain than Group 1 specimens. An analytical investigation was carried out to assess the efficiency of existing compressive stress–strain models to predict the peak compressive stress and ultimate of LC-GFRP-confined RBAC. Existing FRP models were found unreliable in predicting the key parameters in the stress–strain curves of LC-GFRP-confined RBAC. Equations were proposed by using nonlinear regression analysis, and the predicted values of the key parameters were found in good agreement with the corresponding experimental values.