Duration and cost variability of construction activities: an empirical study

The unique nature of construction projects can mean that construction activities often suffer from duration and cost variability. Because this variability is unplanned, it can present a problem when attempting to complete a project on time and on budget. Various factors causing this variability have been identified in the literature, but they predominantly refer to the nature and/or context of the whole project rather than specific activities. In this paper, the order of magnitude of and correlation between activity duration and cost variability is analyzed in 101 construction projects with over 5,000 activities. To do this, the first four moments (mean, standard deviation, skewness, and kurtosis) of actual versus planned duration and cost (log) ratios are analyzed by project, phase of execution, and activity type. Results suggest that, contrary to common wisdom, construction activities do not end late on average. Instead, the large variability in the activity duration is the major factor causing significant project delays and cost overruns. The values of average activity duration and cost variability gathered in this study will also serve as a reference for construction managers to improve future construction planning and project simulation studies with more realistic data

Earned Schedule min-max: Two new EVM metrics for monitoring and controlling projects

[EN] Earned Value Management (EVM) is a well-known project management technique for monitoring project pro-gress. Over the last 15 years, many promising EVM metrics have been proposed to get, among other improve-ments, better actual project duration and cost estimates. Papers comparing the performance of all these metricsare, however, scarce and sometimes contradictory.In this paper, a simulation and empirical comparison of 26 deterministic project duration forecasting tech-niques under the EVM framework is developed. Among them, two new metrics: Earned Schedule min (ESmin) andEarned Schedule max (ESmax) are proposed.ESminandESmaxoffer a new and simpler activity-level calculationapproach of the traditional Earned Schedule metric. Top performing (most accurate) metrics: Earned Schedule(ES), Earned Duration (ED) and Effective Earned Schedule (ES(e)) with Performance Factor 1 (PF= 1), areslightly outperformed by the new metrics which also offer some interesting applications for enhanced projectcontrol.The first author acknowledges the Spanish Ministry of Science, Innovation and Universities for his Ramon y Cajal contract (RYC-2017-22222) co -funded by the European Social Fund. This work was also supported by the second author's "Estancias de movilidad en el extranjero Jose Castillejo para jovenes doctores, 2017 (grant ref. CAS17/00488)" and the fourth author's "Estancias de profesores e investigadores senior en centros extranjeros, incluido el programa "Salvador de Madariaga" 2018 (grant ref. PRX18/00381)", both also from the Spanish Ministry of Science, Innovation and UniversitiesBallesteros Pérez, P.; Sanz-Ablanedo, E.; Mora-Melià, D.; González-Cruz, M.; Fuentes Bargues, JL.; Pellicer, E. (2019). Earned Schedule min-max: Two new EVM metrics for monitoring and controlling projects. Automation in Construction. 103:279-290. https://doi.org/10.1016/j.autcon.2019.03.016S27929010

Global application of an unoccupied aerial vehicle photogrammetry protocol for predicting aboveground biomass in non‐forest ecosystems

P. 1-15Non-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non-forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low-stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjusted R2 of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave-one-out cross-validation of 3.9%. Biomass per-unit-of-height was similar within but different among, plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1–10 ha−1. Photogrammetric approaches could provide much-needed information required to calibrate and validate the vegetation models and satellite-derived biomass products that are essential to understand vulnerable and understudied non-forested ecosystems around the globe.S

Global application of an unoccupied aerial vehicle photogrammetry protocol for predicting aboveground biomass in non‐forest ecosystems

This is the final version. Available on open access from Wiley via the DOI in this recordData Availability Statement: The data collected for this publication, including aerial images, marker and plot coordinates and dry sample weights, as well as site and survey metadata, are available from the NERC Environmental Information Data Centre . Code for photogrammetric processing and statistical analysis is available at Zenodo Non-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non-forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low-stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjusted R2 of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave-one-out cross-validation of 3.9%. Biomass per-unit-of-height was similar within but different among, plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1–10 ha−1. Photogrammetric approaches could provide much-needed information required to calibrate and validate the vegetation models and satellite-derived biomass products that are essential to understand vulnerable and understudied non-forested ecosystems around the globe

From consumer to enterprise grade : How the choice of four UAS impacts point cloud quality

Uncrewed aerial systems (UAS), combined with structure-from-motion photogrammetry, has already proven to be very powerful for a wide range of geoscience applications and different types of UAS are used for scientific and commercial purposes. However, the impact of the UAS used on the accuracy of the point clouds derived is not fully understood, especially for the quantitative analysis of geomorphic changes in complex terrain. Therefore, in this study, we aim to quantify the magnitude of systematic and random error in digital elevation models derived from four commonly used UAS (XR6/Sony α6000, Inspire 2/X4s, Phantom 4 Pro+, Mavic Pro) following different flight patterns. The vertical error of each elevation model is evaluated through comparison with 156 GNSS reference points and the normal distribution and spatial correlation of errors are analysed. Differences in mean errors (0.4 to 1.8 cm) for the XR6, Inspire 2 and Phantom 4 Pro are significant but not relevant for most geomorphological applications. The Mavic Pro shows lower accuracies with mean errors up to 4.3 cm, thus showing a higher influence of random errors. QQ plots revealed a deviation of errors from a normal distribution in almost all data. All UAS data except Mavic Pro exhibit a pure nugget semivariogram, suggesting spatially uncorrelated errors. Compared to the other UAS, the Mavic Pro data show trends (i.e. differences increase with distance across the survey—doming) and the range of semivariances is 10 times greater. The lower accuracy of Mavic Pro can be attributed to the lower GSD at the same flight altitude and most likely, the rolling shutter sensor has an effect on the accuracy of the camera calibration. Overall, our study shows that accuracies depend highly on the chosen data sampling strategy and that the survey design used here is not suitable for calibrating all types of UAS camera equally