Construction time can vary drastically within each structural system as there are many factors that affect the schedule of the project irrespective of whether they are expected or unexpected. To counter these variations in construction time, each structural system have been studied for both planned schedule of activities and their actual start and end dates. The cost is not only linked to the structural system used, but it also comes down to the type of equipment used, such as a boom placer or a concrete pump (Lowe et al., 2006). Other reasons are to where cost variation arise from the use of different types of shuttering, the type of soil encountered and the proximity to the water table at the site which decides the complexity of the foundation, and the earth retaining structures (Sonmez, 2004). Each activity has a normal and a crash time of completion. Associated with the normal time are normal cost and normal performance quality, and with crash time are crash costs and crash quality. This method is not suitable for present study as it is unpredictable about the crash time and crash cost of each project.

Researchers have identified the uncertainty of construction cost and need to improve the capability of cost prediction models (Khang and Myint, 1999). To address this issue, substantial efforts have been made and considerable conceptual cost prediction models are presently available in practice based on techniques, such as probabilistic cost estimation, regression analysis, Neural Network (NN), Fuzzy Logic (FL), Genetic Algorithm (GA), and Case-Based Reasoning (CBR). The advantages and disadvantages were analyzed by researchers and documented (Eashwar and Geetha, 2016; Saputra and Latiffianti, 2015; Fu and Zhang, 2016). However, a review of updated literature related to the current study is presented in this report.

On the studies on regression analysis, a linear regression model for buildings in Hong Kong was proposed by Li et al. (2005), while a multivariate regression model named estimate score procedure was developed by Trost and Oberlender (2003). A subsequent study by Shankar et al. (2011) proposed the Evolutionary Fuzzy Neural Inference Model (EFNIM). The EFNIM was subsequently combined with WWW and historical data to form Evolutionary Web- Based Conceptual Cost Estimators (EWCCE), which provided two types of estimators for conceptual construction cost was developed by Cheng et al. (2009), which was claimed to be effective for accurate cost estimation during the early stages of construction projects. Other recent NN-based models included those reported by Juszczyk (2013); Bala et al. (2014),and Aibinu et al. (2015).

In CBR model, the problems were solved by recognizing the similarity to a problem and adapting solutions to solve the previous problems (Zayed and Halpin, 2005). Many studies on model development based on CBR were reported. An et al. (2007) proposed a CBR model using the Analytic Hierarchy Process (AHP), which included experience in all processes of cost estimation. While few similar models were developed by Koo et al. (2010), Hong et al. (2011), and Ji et al. (2011), an advanced CBR model with 101 cases of multi-family housing projects was reported by Koo et al. (2011) integrating the advantages of CBR, Multiple Regression Analysis (MRA), ANN, and the optimization process using GA. The model was developed by the Microsoft Excel-based Visual Basic Application (VBA), which is user-friendly (Sonmez, 2005; Lai and Lee, 2006; Kim, 2013). In a recent study, the CBR model was integrated with AHP for cost estimation of highway projects (Gunaydin and Dogan, 2004; Siqueria, 1999).

Researchers also reported on developing new and hybrid prediction models. The online analytical processing environment introduced by Moon et al. (2007), the Principal Item Ratios Estimating Method (PIREM) proposed by Yu (2006), and the bootstrap way presented by Sonmez (2008) are good examples for new methods. Although green buildings are designed to reduce adverse environmental impacts with enhanced functionality, initial cost is a matter of concern for the clients (Cheng et al., 2010; Kutner et al., 2004). This problem was addressed by Ahn (2010), who developed a multiple regression model to say the relationship between initial cost and saving using Life Cycle Cost (LCC) and number of floors. RS Means was used to estimate the national average of construction costs for the year 2014.

The objectives of this study are to,

• Study the variation of cost and time between different structural used in the Bengaluru region.
• Summarise the quality requirements of each structural system.
• Prepare an empirical model that uses data from ongoing and completed building projects which would assist consultants and clients to select a suitable set of systems to a given scenario.

In this study, the collected data are like structural BOQ, Schedules, and Quantity estimation documents were from various structural consultants, Project management consultancies and contractors working in greater Bangalore region. After going through the data, the authors classified the data into residential and commercial projects and further, based on number of storeys, it was categorized into three groups, i.e. buildings between 7 and 10 storeys, between 10 and 20 storeys, and more than 20 storeys. Each BOQ was analyzed in detail and quantities of Concreting work, Form works, Minor works, etc., were extracted from that and a spreadsheet was created. This analysis was carried out to each projects data. Later to get the idea of unit quantity required for one meter cube of construction, a term called quantity index was coined.

Quantity index is the ratio of “Quantity of one activity divided by the total built up area”.

Quantity index = Quantity of a activity / Total built up area

In the further stages, quantity index for each activity in each projects was calculated.

Example: A project has a total built up area of 355560 Sqm and for concreting of 1^st floor slab 850 Cum of concrete is used then its quantity index will be,

                 Quantity index = 850/355560 = 0.002391

Quantity index is constant and does not have any unit of measurement. After the calculation of quantity index. Each activity was categorized into horizontal components, Vertical components, etc.

For each type of structural system, average quantity index for all the components is calculated. In case of substructure since area of foundation does not cover the entire built up area, quantity index cannot be calculated so “Split quantity index” was introduced. Here instead of total built up area, the authors have considered only the area of the podium and basements.

All the data extracted from BOQs are consolidated and grouped according to the classification and represented in Tables 1 and 2.

Table 1. Quantity Index (Commercial Projects)

Table 2. Quantity Index (Residential Projects)

Using all the consolidated data, a software application has been created which assists the clients, PMC and structural consultants in choosing the suitable structural system for a project. Additionally Bye-laws of the local municipal authority is taken into consideration before the report is generated.

Step by step working of software application is shown in Figures 1, 2, 3, 4, and 5.

Figure 1. Home Page of the Software

Figure 2. Selection of Project

Figure 3. Selection of Units

Figure 4. Price Chart

Figure 5. Data Entry and Comparison Model

• This study gives a comparison between various structural systems that are used in the construction. This application assists everyone involved in construction project like client, PMC, architect, structural consultant, quantity surveyor, etc.
• It help the clients in predicting the basic cost and time required to complete a project without involving any third parties like architects and consultants. This allows them to assess their land to find its value and to predict the minimum investment, they would need to develop their land. Clients can also use this application in selecting a structural consultant who is experienced or specilized in designing a specific structural system.
• It is a general practice among Architects to give a rough estimate for a project for which they assume standard column, beam, and slab thicknesses, using this application in the initial stages of a project gives a more realistic analysis of the structural cost, quantity of materials required.
• The project management and quantity surveyors can use this application to check on their initial estimates. It also acts as a guide in monitoring the schedule.
• A structural consultant who is new to the industry and does not have a database of quantities, different structural systems could be used in this application to give initial estimates. 
•  The study has considered around thirty projects in greater Bangalore region, this shall be expanded to more projects making the program more accurate. The study shall include other structural systems, which are not included now, such as Prefab, Steel, composite, and pre-engineered buildings, which expands our scope of study.
• The next stage would be expansion into other major metropolitan areas, such as Navi Mumbai, Chennai, Pune, Ahmedabad, Greater Noida, and NCR.
• To include other works, such as different types of blockwork, plastering, partition walls, and finishes.