Making the construction industry resilient to extreme weather:lessons from construction in hot weather conditions

The construction industry is susceptible to extreme weather events (EWEs) due to most of its activities being conducted by manual workers outdoors. Although research has been conducted on the effects of EWEs, such as flooding and snowfall, limited research has been conducted on the effects of heatwaves and hot weather conditions. Heatwaves present a somewhat different risk profile to construction, unlike EWEs such as flooding and heavy snowfall that present physical obstacles to work onsite. However, heatwaves have affected the construction industry in the UK, and construction claims have been made due to adverse weather conditions. With heatwaves being expected to occur more frequently in the coming years, the construction industry may suffer unlike any other industry during the summer months. This creates the need to investigate methods that would allow construction activities to progress during hot summer months with minimal effect on construction projects. Hence, the purpose of this paper. Regions such as the Middle East and the UAE in particular flourish with mega projects, although temperatures soar to above 40̊C in the summer months. Lessons could be learnt from such countries and adapted in the UK. Interviews have been conducted with a lead representative of a client, a consultant and a contractor, all of which currently operate on UAE projects. The key findings include one of the preliminary steps taken by international construction companies operating in the UAE. This involves restructuring their entire regional team by employing management staff from countries such as Lebanon, Palestine, Iraq, and their labour force from the sub-continent such as India and Pakistan. This is not only due to the cheap wage rate but also to the ability to cope and work in such extreme hot weather conditions. The experience of individuals working in the region allows for future planning, where the difference in labour productivity during the extreme hot weather conditions is known, allowing precautionary measures to be put in place.

How good are citizen weather stations? Addressing a biased opinion

The UK is home to a dense network of Citizen Weather Stations (CWS) primarily set up by members of the public. The majority of these stations record air temperature, relative humidity and precipitation, amongst other variables, at sub-hourly intervals. This high resolution network could have benefits in many applications, but only if the data quality is well characterised. Here we present results from an intercomparison field study, in which popular CWS models were tested against Met Office standard equipment. The study identifies some common instrumental biases and their dependencies, which will help us to quantify and correct such biases from the CWS network.

Wind power forecasts using Gaussian processes and numerical weather prediction

Since wind at the earth's surface has an intrinsically complex and stochastic nature, accurate wind power forecasts are necessary for the safe and economic use of wind energy. In this paper, we investigated a combination of numeric and probabilistic models: a Gaussian process (GP) combined with a numerical weather prediction (NWP) model was applied to wind-power forecasting up to one day ahead. First, the wind-speed data from NWP was corrected by a GP, then, as there is always a defined limit on power generated in a wind turbine due to the turbine controlling strategy, wind power forecasts were realized by modeling the relationship between the corrected wind speed and power output using a censored GP. To validate the proposed approach, three real-world datasets were used for model training and testing. The empirical results were compared with several classical wind forecast models, and based on the mean absolute error (MAE), the proposed model provides around 9% to 14% improvement in forecasting accuracy compared to an artificial neural network (ANN) model, and nearly 17% improvement on a third dataset which is from a newly-built wind farm for which there is a limited amount of training data. © 2013 IEEE.

Quantifying uncertainty in citizen weather data

The sheer volume of citizen weather data collected and uploaded to online data hubs is immense. However as with any citizen data it is difficult to assess the accuracy of the measurements. Within this project we quantify just how much data is available, where it comes from, the frequency at which it is collected, and the types of automatic weather stations being used. We also list the numerous possible sources of error and uncertainty within citizen weather observations before showing evidence of such effects in real data. A thorough intercomparison field study was conducted, testing popular models of citizen weather stations. From this study we were able to parameterise key sources of bias. Most significantly the project develops a complete quality control system through which citizen air temperature observations can be passed. The structure of this system was heavily informed by the results of the field study. Using a Bayesian framework the system learns and updates its estimates of the calibration and radiation-induced biases inherent to each station. We then show the benefit of correcting for these learnt biases over using the original uncorrected data. The system also attaches an uncertainty estimate to each observation, which would provide real world applications that choose to incorporate such observations with a measure on which they may base their confidence in the data. The system relies on interpolated temperature and radiation observations from neighbouring professional weather stations for which a Bayesian regression model is used. We recognise some of the assumptions and flaws of the developed system and suggest further work that needs to be done to bring it to an operational setting. Such a system will hopefully allow applications to leverage the additional value citizen weather data brings to longstanding professional observing networks.

A conceptual framework for understanding resilience of construction SMEs to extreme weather events

Purpose Small and Medium-sized Enterprises (SMEs), which form a significant portion in many economies, are some of the most vulnerable to the impact of Extreme Weather Events (EWEs). This is of particular importance to the construction industry, as an overarching majority of construction companies are SMEs who account for the majority of employment and income generation within the industry. In the UK, previous research has identified construction SMEs as some of the worst affected by EWEs. Design/methodology/approach Given the recent occurrences of EWEs and predictions suggesting increases in both the intensity and frequency of EWEs in the future, improving the resilience of construction SMEs is vital for achieving a resilient construction industry. A conceptual framework is first developed which is then populated and expanded based on empirical evidence. Positioned within a pragmatic research philosophy, case study research strategy was adopted as the overall research strategy in undertaking this investigation. Findings Based on the findings of two in-depth case studies of construction SMEs, a framework was developed to represent EWE resilience of construction SMEs, where resilience was seen as a collective effect of vulnerability, coping strategies and coping capacities of SMEs, characteristics of the EWE and the wider economic climate. Originality/value The paper provides an original contribution towards the overarching agenda of the resilience of SMEs, and policy making in the area of EWE risk management by presenting a novel conceptual framework depicting the resilience of medium-sized construction companies.

Weather forecasting to predict practical dynamic asset rating of overhead lines

This paper describes the potential of pre-setting 11kV overhead line ratings over a time period of sufficient length to be useful to the real-time management of overhead lines. This forecast is based on short and long term freely available weather forecasts and is used to help investigate the potential for realising dynamic rating benefits on the electricity network. A comparison between the realisable benefits in ratings using this forecast data, over the period of a year has been undertaken.