Designing Flow-Through Rockfill Underdrains in Unpredictable Climates
March 1, 2023 |
Papua New Guinea experiences high annual rainfall, has rugged topography, and is one of the most seismically active zones in the world. These conditions have presented challenges to the diversion of surface flow at mine sites in the region.
Flow-Through Rockfill Underdrains in PNGKCB has been involved in the design of flow-through rockfill underdrains for mine sites across BC and in Papua New Guinea (PNG). Flow-through rockfill underdrains have been used at mines to pass flows beneath waste dumps, rather than constructing diversion channels around the dumps. Mines with flow-through rockfill underdrains are common in BC, but KCB has done considerable work over the years with flow-through rockfill underdrains at the Porgera, Hidden Valley and Ok Tedi mines in PNG. The stable dump at Porgera, known as the Kogai dump, has a rock underdrain that has been in operation for about 30 years; the Hidden Valley Western Sector waste dumps have underdrains in operation for over 10 years.
Underdrains are typically built from durable, non-acid generating rock from the open pit. Rock is either dumped from a high dump (> 20 m high) to segregate the rock as it falls, so that the coarsest rock forms the drain at the toe of the dump, or it is screened by size and placed by conventional methods. Underdrain rock must be comprised of strong, durable rock that can withstand the stresses imposed by the overlying waste dump, and resistant to mechanical breakage and chemical weathering.
Many of the dumps in PNG could not be built without the rock underdrain as it is not possible to divert the creek base flow or flood flow. Heavy rainfall needs to be diverted, and natural soil and rock, and waste rock is highly erodible. The rock underdrains act as diversions to allow the dump stability and then serve to pass the creek flow during the mine's operating life and closure.
Designing Flow-Through Rockfill Underdrains using Wilkins’ Formula
Design principles for flow-through rockfill underdrains have been developed from civil engineering applications, including Wilkins’ formula for estimating flow capacity for non-Darcy turbulent flow in rockfill:
Q = flow in m3/sec
W = Wilkins constant, ranging from 5.24 m0.5/sec for crushed gravel to 7.33 m0.5/sec for polished marbles
i = hydraulic gradient
e = void ratio
m = hydraulic mean radius of the rock voids (m)
= void ratio/surface area per unit volume
= e*d/8.4 for coarse angular rock where d is the rock diameter in metres
A = area of rockfill transverse to flow (m2)
KCB's design of flow-through rockfill underdrains uses Wilkins ’ formula with a factor of safety of 10 applied to the calculated area. The design allows for a period of ponding for storms (e.g. 24-hour 100-year return) at the upstream face of the dump where the drain inlet is located. Design flood volumes can then be discharged over a reasonable period, often 7 days.
Improving Efficiencies in Collecting 20-Year-Old Undisturbed Soil in Alaska
January 24, 2023 |
KCB has supported tailings management at the Greens Creek Mine near Juneau, Alaska for nearly two decades. A section of the mine is located in Admiralty Island National Monument, an ecologically sensitive area that is home to one of the largest populations of brown bears in the world and various species of wildlife.
As part of an environmental upgrade project, KCB was tasked with excavating a portion of the mine’s tailings pile and collecting undisturbed soil samples that had been buried for up to 20 years.
A Modified Method to Soil Collection
Collecting undisturbed soil samples is an important component of most site investigation programs. The standard method of collecting undisturbed samples is by piston tube sampling in drill holes or block sampling in test pits.
The corner of the existing tailings pile was going to be excavated which would expose tailings that had been buried in the pile for more than 20 years. This provided an opportunity to collect undisturbed samples of these tailings for advanced geotechnical laboratory testing. The conventional method of block sampling was not practical due to the impact on construction schedule and issues with handling and transporting such samples from the site.
KCB devised a modified method and sampling device to collect undisturbed tube samples from ground surface. The sampling device could be placed directly on a prepared surface to recover in situ samples. The sampler consisted of a hydraulic ram to push modified Shelby tubes into the tailings. The tube was then retrieved by hand and trimmed and sealed. With this approach, multiple samples could be collected from an area without disrupting construction and the samples were manageable for handling and transport from the site to the testing laboratory in California.
Benefits of a Hybrid Sampling Method
While the hybrid sampling method proved to be simple, efficient, and cost effective, the sampling device was heavy and awkward to maneuver, and relies on the weight of people to provide the reaction force for the jack to drive the tube into the ground. In stiff soils, several people are needed to collect samples. However, these challenges could be overcome without impacting sample quality and allowed the team to collect more samples that would have been able using conventional methods.
3D Block Modelling of Tailings Dams
November 7, 2022 |
Tailings dams are often progressively raised during mine operations to offset the start-up capital cost and to reflect changes in the operation. The potential to use mine waste – whether it is waste rock from open pit mining or the sand-fraction from cycloning of whole tailings – for construction of the raises presents the opportunity to offset costs, reduce mine waste storage footprints, and improve the safety of the dam.
The efficient use of mine waste in tailings dam construction is reliant on alignment between the overall mine plan and the tailings management plan (e.g., timing of dam raises). The ability of the tailings management plan to “speak the language” of the mine plan is a key to success.
Mine planners typically use 3-dimensional “block models” of the deposit to track the type, quantity, and timing of materials within the open pit (e.g., high-grade ore, low-grade ore, waste). A similar approach can be applied to the development of a tailings dam in support of planning alignment.
What is a Block Model?
A block model is like a series of Lego® blocks, each with a unique spatial location and extent, and associated attributes and metadata, including material type, and completion data for example. The block models can be filtered by attribute and assessed by planners for upcoming fill placement and construction sequencing. Ensuring alignment with the mine waste plan, as far as practical, can aid in ensuring the appropriate materials are available and placed in the right location at the right time.
The 3D Building Blocks
Generating a 3D block model starts with a 3D model of the dam using design and drafting software (e.g. AutoCAD or Civil3D) to create a series of wireframes, or triangulated meshes representing shapes or surfaces comprising the dam.
Each wireframe connects to adjacent wireframes to make a 3D model, without gaps or overlaps. Wireframes are developed using construction sequence records, drill hole or test control data, and design information. Former TSF models can be developed from historic aerial photography and terrain models as it was constructed.
The accuracy of a 3D block model depends on the amount and quality of available data and the minimum block size. Higher accuracies will require a greater amount of data and more computational effort. Consider that a 1 x 1 x 1 block size will generate 100 times the volume of information compared to a 10 x 10 x 1 block size.
Block Factor and Sub-Blocking
There are several methods for building block models, including block factor and sub-blocking. The block factor method generates blocks of a consistent dimension and volume and is calculated using the percentage of the block that falls within the wireframe. The sub-blocking method subdivides blocks into smaller blocks to “best fit” the wireframe.
The block factor method yields a more accurate volume of the solid wireframe, at the expense of its geometry; whereas the sub-blocking method yields a more accurate geometry of TSF components such as embankment zones, drains, or filters. The sub-blocking method also generates a far greater quantity of data than the block factor method.
Calculating Passive Treatment of Effluent in Constructed Wetlands
October 4, 2022 |
Natural Versus Constructed Wetlands
As opposed to natural wetlands, which are typically found at topographic depressions or in areas with high slopes and low permeability soils, or between stream drainages when land is flat and poorly drained, modern treatment wetlands are constructed systems that have been designed to emphasize specific characteristics of wetland ecosystems for improved treatment capacity.
The technology for passive effluent treatment in constructed wetlands has evolved over the last several years into new system configurations and a much broader range of treatment applications. Passive treatment is often attractive to mining proponents because they are relatively low-cost and low maintenance when compared to other treatment alternatives.
Formula for Effluent Treatment
Effluent treatment in a wetland is influenced by a variety of biological processes and biogeochemical cycles that are not always easy to predict and design for. Other factors, including metal species, metal concentration, flow volume, water temperature, and pH all factor in the applicability of wetland treatment. A simple calculation can provide you with a rule of thumb area required to meet the necessary retention time for treatment, helping to determine if a treatment wetland is a feasible alternative.The formula is: A = [Qd (Ci – Ct)] / RA
A = required wetland area (m2)
Qd = mean daily flow-rate (m3/day)
Ci = mean daily influent contaminant concentration (mg/L)
Ct = required concentration of contaminant in final discharge (mg/L)
RA = area-adjusted contaminant removal coefficient (g/m2/day) (dependent on metal)
It is important to note that the contaminant removal coefficient varies significantly by metal; and, certain metals (e.g., zinc) are not so easily remediated. For example, a calculation for a site with an inbound flow rate of 5 m3/min and iron and zinc at equal concentrations and equal regulatory discharge limits is shown below. A wetland for zinc removal would require over 100 hectares of area, not practically feasible. The removal of iron requires significantly less area.
Employee Spotlight – Eugene Cheung
September 21, 2022 |
Eugene Cheung leads the Electrical Engineering team at our Vancouver office. He joined KCB in 2010 and is proud to be an Associate.
1. What does a typical day look like for you?
I find an early start enables me to warm up and get into gear before the daily barrage of e-mails and back-to-back meetings begins. My personal goal is to respond to every inquiry before the next day starts, albeit I’m not always successful! Like others, I’m still trying to perfect my multi-tasking: switching between writing letters/reports/memos, marking-up design drawings, coordinating workflows, and welcoming guests at my desk. I also try to look ahead to navigate the forest through the trees.
Although I don’t travel for work as frequently as before, I still try to take advantage of chances to visit new sites and participate in equipment testing as opportunities arise. Otherwise, travelling to warm and sunny destinations is my preference!
2. What has been the most fulfilling part about your role?
I feel a significant component of engineering consulting is akin to working in the customer service industry. As a result, my most fulfilling aspect is to keep clients happy (some harder than others!), such that they’re eager to return with more business. And just like in customer service, there inevitably will come times when issues have to be professionally resolved; it’s extremely gratifying if I can help turn frowns upside down.
As a side note, I’ve definitely tried to apply the experience I’ve gained while serving as the president of my condo strata over the past 15 years, including interfacing with various personalities in the building and team members of the Pattullo Bridge Replacement Project (literally being constructed right outside my bedroom window!).
3. What is something you find challenging about your role?
Planning, organizing, and optimizing productivity. I think my affinity for this challenge formed while growing up on such min-max video games as Civilization and Master of Magic. Since then, I’ve graduated to playing Stellaris and defeating the toughest aliens on XCOM2 (ironmode legend mode).
At work, I enjoy the dynamics of managing my own workload, as well as thinking ahead to overall project deliverables and how that translates to day-to-day activities for the team. At the same time, it’s also important that I recognize that everyone is at a different place in their career, works at a unique pace, and handles stress differently. Additionally, since some staff prefer an early start and some prefer a late finish, I try to be available as much as possible to keep workflows moving, and to fill in any gaps that form.
4. What is your biggest achievement?
I was awarded the Governor General’s Academic Medal (first-in-class) upon graduating high school. Growing up in a traditional Asian household culminated in this focal point. However, it was not long afterward, when I progressed to university and into the working world, that I realized there are many, many other brilliant people more capable than myself, all with distinct talents and working as a team towards common objectives.
5. What advice would you give someone pursuing a career in your field?
Like most things, engineering isn’t as simple as it used to be. Beyond the traditional fields of civil, mechanical, and electrical, there are numerous subfields from which to choose. An education in electrical engineering may center on computers, software/tech, telecommunications, semiconductors, and others, or a combination thereof. Then within those subfields are varying roles in research & development, manufacturing, sales, design integration, and construction. In my case, working in the area of power engineering within consulting and at KCB allows me to maintain exposure to many of these areas, which keeps me on my toes!
Thus as advice, I’d recommend someone interested in engineering to research and speak with a variety of engineers to gain an overall perspective on the types of engineering careers that would best suit their personal situation. During one’s studies, I recommend giving strong consideration to internships (albeit I didn’t have the chance to do so having studied and graduated during the “.COM Bubble” era when co-op opportunities were limited). Finally, since several engineering industries are cyclical, with some industries even at the risk of obsolescence, it’s important to select an engineering field and role based on one’s outlook.
6. What qualities do you think make a good engineer?I think soft skills are critical. Here are a few that my engineering role models possess:
- Honesty – good engineers are true to others and to themselves. The trust of technical judgment goes no further than the trust of character.
- Self-improvement – good engineers are humble, open-minded, and learn from their mistakes.
- Positive attitude - finally, good engineers are innovative, energetic, and optimistic, with a willingness to tackle all sorts of difficult and unexpected challenges.
7. What is your favourite thing about working at KCB?Aside from staff and project work, I feel KCB’s unique position within engineering consulting cannot be understated. Specifically, KCB is a mid-size engineering company that has been taking a leading, active role on relatively large projects that are typically only awarded to gargantuan engineering conglomerates nowadays. This has provided the company and staff the ability to be flexible, creative, and responsive to our clients’ needs, presenting rewarding personal and business growth opportunities.
Could “Smart” Bridges Be the Way of The Future?
August 30, 2022 |
Our Bridge Infrastructure is Aging
The 2021 American Society of Civil Engineers report card for America’s infrastructure found that the national backlog for bridge repair exceeded $125 billion. The report also found that 42% of all bridges were at least 50 years old and that 46,154 (7.5%) were considered structurally deficient. In Canada, the 2019 Canadian Infrastructure Report Card found that 9,661 (12.4%) bridge and tunnel structures were in poor or very poor condition. As North America’s bridge infrastructure continues to deteriorate, and a growing population pushes demand for mobility to new highs, researchers have recognized an opportunity to apply technology to support the operation and maintenance of these critical assets.
Sensor-Based Monitoring (SBM) of Bridge Assets
Much research over the past two decades has focused on the development of structural monitoring approaches to collect quantitative data relating to bridge performance (e.g., accelerations, displacements, strains). However, significant challenges remain surrounding how this data can be effectively used by asset owners and engineering consultants to assess bridge conditions and inform operation and maintenance decisions. As a result, the use of monitoring has largely been limited to academic studies and very targeted applications.
This raises a number of important questions: What is preventing the widespread use of sensors to monitor bridge infrastructure? And what information would a sensor system need to produce to prove to be useful for bridge owners and engineering consultants?
To investigate the answers to these questions, an anonymous survey was circulated amongst industry and academic participants to get their insights into the current state of the use of sensors for monitoring bridge infrastructure. KCB recently presented the findings of this survey at the 11th International Conference on Short and Medium Span Bridges.
Survey ResultsRespondents identified a lack of knowledge about sensor-based monitoring (SBM) and a lack of requirements for regular bridge operation and maintenance as the primary reasons they had not previously used or implemented an SBM system. Respondents also felt that to be widely used and accepted in practice to support bridge operation and maintenance, an SBM system would need to offer the following benefits:
- Provide monetary value in the form of reduced operation and maintenance costs,
- Locate and identify potential damage indicators,
- Estimate the likely remaining service life of the asset, and
- Reduce the bridge inspection frequency.
Based on the survey results, respondents felt that a useful SBM system should provide value to asset operators with minimal engineering analysis required for decision-making. Furthermore, asset operators should be able to quantify the accuracy of the information provided by an SBM system since they are ultimately responsible for invoking maintenance action. Sensor limitations and assumptions about SBM data could be quantified to develop confidence intervals for output data to further assist with decision-making. The potential for applying a mathematical framework to support decision-making related to operation and maintenance is worth exploring in future research. Similarly, the value of applying a reliability-based framework of analysis to SBM should be explored.
The large datasets produced by SBM systems over the life cycle of a bridge could lend themselves well to the application of artificial intelligence and machine learning algorithms to recognize trends and is worth exploring in future research. Given the amount of time needed to capture these datasets and observe changes in bridge performance, the effect of prolonged environmental exposure on sensor networks also merits further investigation.
To gain buy-in from asset owners, a cost-benefit analysis should be performed to determine the financial viability of proposed SBM packages over the life cycle of the asset. The installation and operation costs of the system should be compared against potential savings on operation and maintenance, and traditional bridge inspection costs. An SBM system designed to collect data from several different bridge components could be deployed in phases using a cost-benefit analysis to determine the optimal order and timing of installation for each sensing option.
With the rapid advancement of technology in the 21st century, many companies have realized that data generated by their operations can be collected and used to optimize their business practices. Advancements in the field of artificial intelligence have turned the once labour-intensive task of parsing large datasets into a powerful and increasingly accessible tool to help businesses improve their bottom line. In many cases, industries that fail to collect data to improve their business practices are falling behind in this competitive digital and technological landscape. It is imperative we do not allow bridge engineering to be one of those industries.Read the full paper here.
Portable Light Percussion Drilling: A Practical Solution for Challenging Site Conditions
July 8, 2022 |
Some geotechnical site investigations face challenging conditions such as poor site access, restrictions on the operation of heavy equipment, and limited budget and time to complete the program. In these situations, the use of portable light percussion drilling systems can be a practical and efficient method for obtaining soil samples. In KCB’s project work at the Fruta Del Norte mine in Los Encuentros, Ecuador, these systems have been an invaluable tool because of their mobility and ease of use.
The Fruta Del Norte mine is located in the province of Zamora, in the jungle region of Ecuador. KCB has been working at the mine since 2009 undertaking site investigations, geotechnical assessments, and feasibility studies at the tailings storage facility (TSF) and Plant Site. The mine is in a densely vegetated jungle where the presence of thick residual soil horizons and high yearly precipitation (3000 mm per year) make the logistics for field programs difficult.
To manage some of the challenges in recovering soil samples at the project site, the KCB team adopted a portable light percussion drilling system. The system is a portable gas-powered percussion drilling apparatus with a core sampler. It operates by advancing steel gouges and/or core samplers into the ground by a telescopic drilling method, where progressively smaller diameter gouges are driven into the soil. The soils contained in each gouge is sampled through a window in the side of the tube. Depending on the drilling apparatus’s specifications, some have drilling depths of up to 10 m.
- Low cost compared to conventional drilling rigs.
- Easy transportation and operation.
- High penetration rates (up to 3 holes of 6 m per day).
- Good recovery up to 6 m depth.
- Very good recovery in ‘cohesive’ fine-grained soils.
- Disturbance of each sample is unavoidable.
- Requires a few people to manoeuvre the equipment.
- Poor recovery of wet coarse-grained soils with the supplied core sampler, and at depths below 6 m.
Decision Analysis: A Structured Approach to Improving Project Success
June 21, 2022 |
Decisions are a part of our everyday life. They can include a low-stake decision like buying a cup of coffee before your drive into the office, or a high-stake decision like buying a house. As engineering professionals, your project work requires a multitude of decisions and the opinions of stakeholders throughout the process. Decisions made in a project setting often require a more structured approach to produce a rational and auditable methodology for determining a choice between competing options.
A decision analysis process can help build consensus among stakeholders, consider a wide range of options, identify potential risks, and develop a plan with specific actions. There are several that are commonly used in project management including the Kepner-Tregoe method and Multiple Accounts Analysis. The advantage of a decision analysis process is it can bring together informed people in many fields (fields of interest are called accounts) and can include social, environmental, technical, and cost aspects of a project. These fields of interest often have competing requirements and different risk profiles which the process describes in plain language and evaluates from different stakeholder viewpoints, often in a workshop setting.
Define the ProblemThe first step when conducting a decision analysis is defining the problem. During this step, you will develop a thorough description of the situation, its purpose, and identify a core team of stakeholders from a variety of fields (geotechnical, environmental, operations, finance, etc.) who can contribute. The core team should determine the purpose of the decision and any constraints that will guide the scope of the process. It is during this step that you will consider multiple criteria, identify the account structure (social, environmental, technical, cost, etc.), and define assumptions.
Establish the ObjectiveOnce you have developed an understanding of the problem, you can establish the objectives of the decision. Start by compiling a list of objectives and divide them into “musts” and “wants”. "Must” objectives are criteria that must be met for an alternative to be successful (e.g. regulatory criteria). “Want” objectives provide the means of differentiating between options (e.g. maximize opportunity to reach passive care) and do not need to be met for an alternative to succeed.
Identify AlternativesIn this step, you will identify alternatives to meet the decision. These can be achieved through a matrix of elements to develop various options which are discussed throughout the process. If any alternatives do not fulfill all the “must” objectives, screen them out.
Engage StakeholdersNow, compare the alternatives to the defined objectives. (This is typically undertaken in a workshop setting with key stakeholders and a capable facilitator guiding the process.) Rank each alternative based on its ability to achieve the objectives. For this step, alternatives must meet all the “must” objectives and are evaluated against the “want” objectives. Assess your results by conducting sensitivity analyses and a risk assessment to reduce the overall risk to as minimal as possible.
Make your DecisionDecide on an alternative based on how it ranks above other alternatives, its costs, and risks. Once a decision has been made, develop an action plan, documenting the approach and rationale of the decision, to move the project forward. This step usually includes a forward high-level work plan for the project.
Tips when conducting a decision analysis:
- Be clear on the situation appraisal and problem analysis prior to undertaking the decision analysis.
- Have the right people in the room to make decisions. Identify and include key stakeholders to increase the success of the decision analysis process.
- Achieve consensus at each step.
- An objective framing workshop is a useful way to engage decision makers and identify policy, strategic and tactical objectives.
- Characterize each alternative with a supporting body of knowledge enough to compare each option without prior judgement.
- It can be as simple or complicated as it needs to be. But do not over-complicate. Not all the answers are needed to make an informed decision.
Employee Spotlight – Matthew Forbes
May 18, 2022 |
Matthew Forbes is a Hydrogeochemist based in our Brisbane office.
1. What does a typical day look like for you?A normal workday involves getting up to walk the dog, drinking some coffee, and then riding my bike to work. However, during the floods, my bike was stolen and the office was flooded so that is not so normal now. Nowadays, I like to get into the water so I try to swim at lunchtime at Musgrave Park at least once a week. After work, more dog walking, dinner, a beer, and some TV. On weekends I like to head to the beach or the pool or head to Bunnings and buy useless things for my herb and veggie garden.
2. What has been the most fulfilling part about your role?Using my previous experiences, which are outside the consulting realm, to help provide solid scientific answers to project questions.
3. What is something you find challenging about your role?Learning about the mindset of industry clients, in terms of understanding what they really want and how much they are willing to pay to get it.
4. What is your biggest achievement?The first is successfully running a multi-million biogeochemical laboratory at Stanford University. The second is publishing a paper on the global carbon cycle that now has been cited in over 500 subsequent publications.
5. What advice would you give someone pursuing a career in your field?Learn to plan, because as they say, failing to plan is planning to fail. Be happy but humble about your professional achievements.
6. What qualities do you think make a good scientist?Experience makes a good scientist, a good geoscientist, and overall a good consultant. I have been very lucky over the last 20 years to work in the fields of hydrogeology, geomorphology, quaternary climate science, oceanography, and soil science across state government, CSRIO, commercial start-ups, world-leading university research institutions, national research centres, and now industries with KCB. All these experiences make me the professional I am now and gave me the skills I bring to KCB.
7. What is your favourite thing about working at KCB?Diversity of the tasks and challenges and the places that you go to, that you would have not never otherwise.
Selecting a Gridding Algorithm for Geology or Ground Models
May 10, 2022 |
Geology or ground models are critical elements of engineering or geoscience design. They define the interaction between the built and natural environments.
The built or engineering environment is two-dimensional, consisting of straight lines and formed curves. It is a consistent, patterned design, that is regular and conforms to mathematical summation.
The natural environment (geology) is three-dimensional and inconsistent in form and continuity; variation and change are everywhere and affect every component of the system. Randomness does not work well with engineering design, and order and simplicity do not usually apply to geology.
However, great success in modelling the natural environment occurs when these aspects are integrated in an efficient and defensible manner.
Gridding algorithms are mathematical processes that read irregularly distributed data and convert these into a regularly spaced array. In simpler terms, the process converts drill hole contacts to a format that can be converted to wireframes, geological models or block models, which in turn inform many of our analyses and interpretations.
It is our role as professionals to select an appropriate algorithm that balances aesthetic visualization with the defensible representation of the data. When the wrong algorithm is selected this results in unrealistic data construction that is difficult to identify and is time-consuming and frustrating to correct.
Algorithm ConsiderationsThe main considerations in selecting an interpolation algorithm include:
Applying Common Algorithms to Geological ModelsThe following drill hole data (left) was contoured using three algorithm methods: triangulation, minimum curvature and kriging.
TriangulationThe triangulation algorithm uses lines to create triangles between data points. Triangulation results are blocky and abrupt, yet often best reproduce the original data.
Mininum CurvatureWidely used in earth sciences, minimum curvature generates a smooth interpolated surface from the data points. Minimum curvature results honour the original data while achieving smoothed contours.
KrigingKriging is a geostatistical gridding method used to express trends suggested in the data. Kriging results include troughs and peaks and smooths the areas of limited data. Where base data is sparse or geological contacts are abrupt, kriging can skew the data.
Use the following principles when selecting a gridding algorithm:
As a starting point, the minimum curvature algorithm is a good all-rounder. Apply a moderate grid density, by aiming initially for 10,000 nodes (or 100 x 100). Always compare the constructed surface with the original data by running a “residuals check” to assess the algorithm’s performance. In areas of sparse data, create sections or view the data to check for algorithm-generated troughs or peaks.