Productivity estimating of ready mixed concrete batch plant is an essential tool for the successful completion of the construction process. It is defined as the output of the system per unit of time. Usually, the actual productivity values of construction equipment in the site are not consistent with the nominal ones. Therefore, it is necessary to make a comprehensive evaluation of the nominal productivity of equipment concerning the effected factors and then re-evaluate them according to the actual values.

In this paper, the forecasting system was employed is an Artificial Intelligence technique (AI). It is represented by Artificial Neural Network (ANN) to establish the predicted model to estimate wet ready mixe

Conventional concretes are nearly unbendable, and just 0.1 percent of strain potential makes them incredibly brittle and stiff. This absence of bendability is a significant cause of strain failure and has been a guiding force in the production of an elegant substance, bendable concrete, also known as engineered cement composites, abbreviated as ECC. This type of concrete is capable of displaying dramatically increased flexibility. ECC is reinforced with micromechanical polymer fibers. ECC usually uses a 2 percent volume of small, disconnected fibers. Thus, bendable concrete deforms but without breaking any further than conventional concrete. This research aims to involve this type of concrete, bendable concrete, that will give solut

Reinforcing asphalt concrete with polyester fibers considered as an active remedy to alleviate the harmful impact of fatigue deterioration. This study covers the investigation of utilizing two shapes of fibers size, 6.35 mm by 3.00 mm and 12.70 mm by 3.00 mm with mutual concentrations equal to 0.25 %, 0.50 % and 0.75 % by weight of mixture. Composition of asphalt mixture consists of different optimum (40-50) asphalt cement content, 12.50 mm nominal aggregate maximum size with limestone dust as a filler. Following the traditional asphalt cement and aggregate tests, three essential test were carried out on mixtures, namely: Marshall test (105 cylindrical specimens), indirect tensile strength test (21 cylindrical specimens)

As human societies grow, the problem of waste management becomes one of the pressing issues that need to be addressed. Recycling and reuse of waste are effective waste management measures that prevent pollution and conserve natural resources. In this study, the possibility of using glass waste as an alternative was used as a partial weight substitute for fine aggregates with replacement ratios of 10, 20, 30, and 40% by the weight, and formed into test models (15 cm * 15 cm ) cube and (15 cm * 30 cm) cylinder, then matured and tested their strength compression and tensile strength at the age of 7 and 28 days and compared with a reference or conventional concrete with a mixing ratio (1: 1.5: 3) as well as testing its worka

The introduction of concrete damage plasticity material models has significantly improved the accuracy with which the concrete structural elements can be predicted in terms of their structural response. Research into this method's accuracy in analyzing complex concrete forms has been limited. A damage model combined with a plasticity model, based on continuum damage mechanics, is recommended for effectively predicting and simulating concrete behaviour. The damage parameters, such as compressive and tensile damages, can be defined to simulate concrete behavior in a damaged-plasticity model accurately. This research aims to propose an analytical model for assessing concrete compressive damage based on stiffness deterioration. The prop

Moisture-induced damage is one of the primary causes of premature distress in asphalt pavements, leading to reduced service life and increased maintenance costs. Although nanomaterials have shown potential in enhancing asphalt performance, the underlying composite interaction mechanisms among nanomaterials, asphalt binder, and aggregate phases under moisture exposure are still not fully understood. In addition, comparative evaluations under consistent experimental conditions remain limited. This study investigates the influence of five nanomaterials: nano-silica (NS), nano-alumina (NA), nano-titanium dioxide (NT), nano-zinc oxide (NZ), and carbon nanotubes (CNT) on the physical and mechanical properties of asphalt binders and mixtur