Incorporating inflation rate in construction projects cost: Forecasting model.

Over time, the change in the inflation rate causes cost overruns by deviating the prices of goods and services in construction projects that require practitioners to make budgeting revisions. Hence, this study aims to develop a construction rates forecasting model that can incorporate the changing impact of the inflation rate on construction rates and predict the prices in a particular year, which can be adjusted when developing the Bill of Quantities. Following the time series analysis standards, a mathematical model was developed using MATLAB for forecasting.

Concrete by Preplaced Aggregate Method Using Silica Fume and Polypropylene Fibres.

Preplaced aggregate concrete (PAC) is prepared in two steps, with the coarse aggregate being initially laid down in the formwork, after which a specialised grout is injected into it. To enhance the properties of concrete and to reduce the emission of CO produced during the production of cement, supplementary cementitious materials (SCMs) are used to partially substitute ordinary Portland cement (OPC). In this study, 100 mm × 200 mm (diameter x height) PAC cylinders were cast with 10 per cent of cement being substituted with silica fume; along with that, 1.

Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Machine Learning Techniques.

Silica fume (SF) is a frequently used mineral admixture in producing sustainable concrete in the construction sector. Incorporating SF as a partial substitution of cement in concrete has obvious advantages, including reduced CO emission, cost-effective concrete, enhanced durability, and mechanical properties. Due to ever-increasing environmental concerns, the development of predictive machine learning (ML) models requires time.

Predicting the Ultimate Axial Capacity of Uniaxially Loaded CFST Columns Using Multiphysics Artificial Intelligence.

The object of this research is concrete-filled steel tubes (CFST). The article aimed to develop a prediction Multiphysics model for the circular CFST column by using the Artificial Neural Network (ANN), the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Gene Expression Program (GEP). The database for this study contains 1667 datapoints in which 702 are short CFST columns and 965 are long CFST columns.

Predictive Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Artificial Intelligence Approaches: MLPNN, ANFIS, and GEP.

Silica fume (SF) is a mineral additive that is widely used in the construction industry when producing sustainable concrete. The integration of SF in concrete as a partial replacement for cement has several evident benefits, including reduced CO emissions, cost-effective concrete, increased durability, and mechanical qualities. As environmental issues continue to grow, the development of predictive machine learning models is critical.

Artificial Neural Network (ANN) and Finite Element (FEM) Models for GFRP-Reinforced Concrete Columns under Axial Compression.

In reinforced concrete structures, the fiber-reinforced polymer (FRP) as reinforcing rebars have been widely used. The use of GFRP (glass fiber-reinforced polymer) bars to solve the steel reinforcement corrosion problem in various concrete structures is now well documented in many research studies. Hollow concrete-core columns (HCCs) are used to make a lightweight structure and reduce its cost.

Forecasting Strength of CFRP Confined Concrete Using Multi Expression Programming.

This study provides the application of a machine learning-based algorithm approach names "Multi Expression Programming" (MEP) to forecast the compressive strength of carbon fiber-reinforced polymer (CFRP) confined concrete. The suggested computational Multiphysics model is based on previously reported experimental results. However, critical parameters comprise both the geometrical and mechanical properties, including the height and diameter of the specimen, the modulus of elasticity of CFRP, unconfined strength of concrete, and CFRP overall layer thickness.

Mechanical and Microstructural Characterization of Quarry Rock Dust Incorporated Steel Fiber Reinforced Geopolymer Concrete and Residual Properties after Exposure to Elevated Temperatures.

The purpose of this research is to study the effects of quarry rock dust (QRD) and steel fibers (SF) inclusion on the fresh, mechanical, and microstructural properties of fly ash (FA) and ground granulated blast furnace slag (SG)-based geopolymer concrete (GPC) exposed to elevated temperatures. Such types of ternary mixes were prepared by blending waste materials from different industries, including QRD, SG, and FA, with alkaline activator solutions. The multiphysical models show that the inclusion of steel fibers and binders can enhance the mechanical properties of GPC.

Multigene Expression Programming Based Forecasting the Hardened Properties of Sustainable Bagasse Ash Concrete.

The application of multiphysics models and soft computing techniques is gaining enormous attention in the construction sector due to the development of various types of concrete. In this research, an improved form of supervised machine learning, i.e.

Axial Compressive Strength Models of Eccentrically-Loaded Rectangular Reinforced Concrete Columns Confined with FRP.

The majority of experimental and analytical studies on fiber-reinforced polymer (FRP) confined concrete has largely concentrated on plain (unreinforced) small-scale concrete columns, on which the efficiency of strengthening is much higher compared with large-scale columns. Although reinforced concrete (RC) columns subjected to combined axial compression and flexural loads (i.e.

Thickness Optimization of Thin-Film Tandem Organic Solar Cell.

The polymer solar cells also known as organic solar cells (OSCs) have drawn attention due to their cynosure in industrial manufacturing because of their promising properties such as low weight, highly flexible, and low-cost production. However, low restricts the utilization of OSCs for potential applications such as low-cost energy harvesting devices. In this paper, OSCs structure based on a triple-junction tandem scheme is reported with three different absorber materials to enhance the absorption of photons which in turn improves the , as well as its correlating performance parameters.

Investigation on Hamburg Wheel-Tracking Device Stripping Performance Properties of Recycled Hot-Mix Asphalt Mixtures.

Moisture damage in hot mix asphalt pavements is a periodic but persistent problem nowadays, even though laboratory testing is performed to identify different moisture-susceptible mixtures. In this study, a Hamburg Wheel Tracking device (HWTD) was used for rutting tests which were conducted on control and a high percentage of recycled asphalt pavement (RAP), i.e.