Borja García de Soto

Analysis of Adverse Weather for Excusable Delays

Severe weather conditions can be disruptive to construction. Contractors typically obtain time extensions for weather days beyond normal conditions. However, contracting parties often dispute the extent of weather-related time extensions. Typical industry contracts may overlook many important points that can provide an acceptable resolution. This paper classifies seven factors causing discrepancies in analysis of adverse weather for time extensions; namely, the definition of normal weather, weather thresholds, type of work, lingering days, criteria for lost days, lost days equivalent due to lost productivity, and work days lost versus calendar days lost. An analysis of an actual weather-caused delay claim illustrates the impacts of those factors on the outcomes of the analysis. A contract should define anticipated weather delay days and their lingering days and provide threshold values for weather parameters to differentiate between predictable and unpredictable severe weather. The contract should clearly define how a time extension is granted in calendar days as a result of work days lost, and also address how a time extension is granted due to inefficiency caused by unusually severe weather. Future research may provide an appropriate mechanism for analyzing equivalent lost days to account for lost productivity.

A Process for the Development and Evaluation of Preliminary Construction Material Quantity Estimation Models Using Backward Elimination Regression and Neural Networks

During the early stages of a project, it is beneficial to have an accurate preliminary estimate of its cost. One way to make those estimates is by determining the amount of construction material quantities that are required and then multiplying the estimated construction material quantities by the corresponding unit cost. One advantage of making estimates in this way is that it allows for the segregation of quantities and costs. This way they can be updated separately as new information becomes available. They can also be tracked separately allowing decision makers to make better decisions about the project during its conceptual phase. There are several techniques that can be used to develop estimation models. The most common include regression analysis and artificial intelligence, such as neural networks. Work has been done, however, in a non-standardized way, leaving practitioners without guidance as to how to develop and evaluate models for their specific purposes. This can be seen in particular in the many different types of metrics used for the evaluation of models. The goal of the work presented in this article was to create a process to (1) develop models to be used to prepare preliminary estimates of construction material quantities taking into consideration the available data during the early stages of a project, and (2) evaluate the developed models using the Akaike information criterion. The proposed process is illustrated with an example in which data from 58 storage buildings was used to develop models to estimate the amount of concrete and reinforcement required using backward elimination regression analysis and neural network techniques. The developed models were then evaluated using a second-order correction Akaike information criterion (AICc) to select the most accurate model for each construction material quantity. The proposed process is useful for practitioners in need of developing robust estimation models in a consistent and systematic way, and the AICc metric proved to be effective for selecting the most accurate models from a set.

A hybrid methodology to estimate construction material quantities at an early project phase

Preliminary project cost estimates are the first serious estimates made on a project. They play an important role during the decision-making process, and are the benchmark with which future estimates are expected to agree. This paper concentrates on the estimation of construction material quantities (CMQs) and presents a methodology to accurately estimate them during an early project phase. We make use of existing data and utilize regression analysis, neural networks and case-based reasoning to provide accurate results. It encompasses data collection, model development and evaluation, and the integration of different techniques. The use of the methodology is demonstrated by estimating CMQs of relevant structures. The accuracy of the methodology is investigated and compared with three state-of-practice approaches. The results obtained show a significant improvement over the state of the practice, and would improve the accuracy of preliminary project costs estimates. Through partial automation, it would likely reduce the time required to make estimates.

Improving the planning and design phases of construction projects by using a Case-Based Digital Building System

Abstract The construction industry is known for having well-defined and systematic processes for the different phases of a project. Improvements, however, are possible with respect to how information is used in these phases and how the experience gained from previous projects is exploited. In this paper, an example of how new technologies can be used to improve the planning and design of buildings is shown. In the example, a Case-Based Digital Building System (CB-DBS) in which every building as an integrated structural system consisting of highly parameterized standard elements is used in the planning phases of projects. In the planning phases, case-based reasoning is used to identify similar completed projects that would enable faster starts of new projects. In the design phases, case-based design is used to identify similar portions of completed projects that would enable faster designs of the more detailed parts of the buildings. A proof of concept using housing projects from a design–build–operate company based in Switzerland is used to test the viability of the proposed system. It is shown that the CB-DBS enabled faster planning and design phases of new projects and improved exploitation of the knowledge gained in previous projects.

Where does the money go? Economic flow analysis of construction projects

ABSTRACT A new approach is introduced for analysing the economic flows in a construction project that will complement the existing methods. The main focus of the new method is to trace the flow of costs in the project and identify their spatial characteristics as well as who are the final recipients. This type of analysis highlights the flow of costs of the specific project in the local economy. It can be used for decision-making purposes from the project-commissioning party. Costs were divided into six categories, namely: labour, materials, energy, office overheads, taxes and personnel, and into two levels: local and national. Three case studies of stone and reinforced concrete structures were selected and analysed to test the application of this method. The case studies showed that this method and the representation by a Sankey diagram can provide useful insights regarding both the spatialized distribution of the cost of a project and the economic flows going to direct labour versus overhead and taxes. For future projects, local authorities or developers can select the most economically suitable strategy maximizing the income of the local community.

Effizienter Betrieb und Unterhalt der technischen Infrastruktur (EFFIN): Schlussbericht

Das Projekt hat bestatigt, dass in Bezug auf die effiziente Durchfuhrung des betrieblichen Unterhalts und der Instandhaltung Forschungsanstrengungen unternommen werden sollen. Die Datenlage bei den Betreiberorganisationen ist noch sehr heterogen und in Bezug auf den Umfang nur sehr unterschiedlich vorhanden. Umgekehrt steigt der Wille, die Mittel effizient einzusetzen und sich hierzu auch mit anderen Betreiberorganisationen zu vergleichen.